SOCIAL-COGNITIVE AND EMOTION PREDICTORS OF … · SOCIAL-COGNITIVE AND EMOTION PREDICTORS OF...

SOCIAL-COGNITIVE AND EMOTION PREDICTORS OF

COLORECTAL CANCER SCREENING RELUCTANCE IN

AUSTRALIA

VICTORIA ELLEN HAMILTON

B.A. (Psych, Honours), University of Queensland

Faculty of Life and Social Sciences

Swinburne University of Technology

Hawthorn, Victoria, Australia

A thesis submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

October 2011

ii

ABSTRACT

Colorectal cancer (CRC) screening is an effective but relatively underutilized

screening test in Australia and internationally. Many studies have sought to explain this

problem by applying major social-cognitive health behaviour models grounded in

assumptions about rational, cognizant decision-making. A large literature amalgamates

components and constructs from different models however, there is relatively little

variance in screening uptake explained by the few studies exploring the unique

contribution of any single theoretical model. Further issues include the deficit of

research exploring the contribution of discrete emotions in health intentions and

behaviours such as cancer screening, compounded by insufficient integration of emotion

into social-cognitive models. As well, heuristics and their related biases have been

under-explored in health behaviour research. The dual-process theoretical approach

from which they are understood may offer additional insight into the processes

underlying screening intention. In addition to some of the most common variables from

major health behaviour models, including demographic, and health variables, this

dissertation describes an in-depth examination of the roles of heuristics and discrete

emotions on CRC screening intentions and participation in an Australian sample.

Two related studies explore the facility of both traditional and emerging

cognitive, emotion, and social factors in explaining CRC screening intention and

behaviour. Study 1 was a preliminary investigation in a sample of convenience (N =

202) with 47 men and 155 women ranging in age from 18 to 75 years (M = 27). It was

designed to examine the psychometric properties of the scales, refine the measures

using factor analysis, and investigate the relationships between variables, in order to

inform a second, community-based study. From the results of this pilot study, a range of

variables were understood to be related to screening reluctance, in particular, heuristic

biases and a number of different types of emotion relating to embarrassment in medical

settings, and fear in connection with screening procedures and complications.

Traditional social-cognitive variables also emerged as important correlates, including

self-efficacy and social support. Study 2 largely confirmed the preliminary findings in a

community sample of 240 Australians (80 men; 160 women) aged 35 to 87 years (M =

59), and further tested the ability of these variables to predict reluctance to screen for

colorectal cancer. The results of a discriminant analysis suggest that specific emotions

such as fear of screening procedures and medical embarrassment, and heuristic biases,

are important in the prediction of colonoscopy and faecal occult blood test intentions,

iii

while screening participation may be best predicted by ‘fixed’ demographic and health

factors, and by cognitions, including screening biases.

Together, the findings support an emerging research focus on broader

experiential processes, such as discrete emotions and heuristic biases, in the prediction

of screening reluctance. The conclusions drawn from the present results include

recommendations for greater systematic, empirical study of these variables and the

incorporation of such evidence into public health interventions for cancer detection and

prevention.

iv

ACKNOWLEDGEMENTS

A number of people deserve sincere thanks for their support and guidance

throughout a unique and gratifying four years.

I had the good fortune to have an incredible guide and mentor advising me

throughout – my principle supervisor, Professor Susan Moore. Sue provided support

and expert direction; wisdom and insight; and importantly, was a readily accessible

supervisor. I was lucky to have swift replies to all my queries, and Sue was always

generous with her time. Having a great supervisor helped make this experience

enjoyable, enlightening, and achievable. I would also like to thank Dr. Denny Meyer

who provided invaluable statistical direction on confirmatory factor analysis (resulting

in a colossal reduction of confusion and stress). Denny was also forthcoming with

appointments in my times of need. I also wish to thank Dr. Elizabeth Hardie, who

supervised the earlier parts of my PhD experience, and Dr. Simon Knowles, who

provided a fresh perspective at the final stage of this project.

I am indebted to all the participants who shared their time and opinions about a

potentially sensitive topic. Although I can’t thank them personally, they have

contributed greatly to this project, and it is genuinely appreciated.

I dedicate this thesis to my parents, Isabella and Andrew. I’m grateful and

thankful for the love, support, education, and opportunity they provided, and for which I

do not take for granted. Thank you to my partner Darren, who encouraged me, accepted

my occasional (this is subject to interpretation) reclusiveness, and for being supportive

of me from day one. Support was key, and on this note, I would like to thank my

friends, who deftly avoided discussions about my thesis– a commendable achievement.

In particular, I appreciate the friendship of fellow student Sarah Buckingham, who

shared the Amos learning curve with me, and was a sounding board for many thesis

anxieties during our long morning runs around Melbourne.

Lastly, I thank Swinburne University for the Postgraduate Conference Scheme

Grant in 2009, which assisted me with the registration costs of presenting a conference

paper, and to Sue, who also contributed. Thanks to Professor Michael Gilding for the

experiences afforded me as a result of the smartHEALTH grant. I am also thankful to

have been a recipient of funding to attend the Australian Consortium of Social and

Political Research Inc. (ACSPRI) Structural Equation Modelling course, and of the

Swinburne University Postgraduate Research Award. Clearly, a thesis is a team effort,

and this rewarding experience will remain with me always.

Vic

Typewritten Text

Vic

Typewritten Text

DECLARATION BY CANDIDATE

This dissertation is submitted to Swinburne University of Technology in fulfillment of

the requirements for the degree of Doctor of Philosophy.

this thesis is an original work, which has not been

person. Any citing of non-original writing is referenced accordingly. I hereby declare

that I have not submitted this material, in full or in part, for a degree at this or any other

institution.

DECLARATION BY CANDIDATE


the requirements for the degree of Doctor of Philosophy. To the best of my knowledge

this thesis is an original work, which has not been published or written by another

original writing is referenced accordingly. I hereby declare


___________________

Victoria Ellen Hamilton

October 2011

v


To the best of my knowledge

original writing is referenced accordingly. I hereby declare


vi

TABLE OF CONTENTS

ABSTRACT....................................................................................... ...............................ii

ACKNOWLEDGEMENTS.............................................................................................iv

DECLARATION...............................................................................................................v

TABLE OF CONTENTS.................................................................................................vi

LIST OF TABLES..........................................................................................................xv

LIST OF FIGURES.......................................................................................................xvii

LIST OF APPENDICES................................................................................................xix

LIST OF PUBLICATIONS ARISING FROM THIS RESEARCH...............................xx

LIST OF ABBREVIATIONS........................................................................................xxi

CHAPTER 1. COLORECTAL CANCER SCREENING INTENTIONS AND

BEHAVIOUR: BACKGROUND, DEFINITION, AND MEASUREMENT

1.1 Background to Research Area...............................................................................1

1.2 Status of CRC Screening in Australia...................................................................4

1.2.1 Epidemiology and medical definition...........................................................4

1.2.1.1 Epidemiology........................................................................................4

1.2.1.2 Definition..............................................................................................4

1.2.2 CRC screening tests......................................................................................6

1.2.2.1 Faecal occult blood testing....................................................................7

1.2.2.2 Flexible sigmoidoscopy........................................................................7

1.2.2.3 Colonoscopy.........................................................................................8

1.2.2.4 Barium enema.......................................................................................9

1.2.2.5 Emerging screening tests......................................................................9

1.2.3 Uptake of CRC screening...........................................................................10

1.3 CRC Screening Intentions: Demographic and Health Factors............................13

1.3.1 Health history and family history of CRC..................................................13

1.3.2 Demographic associations with CRC screening.........................................14

1.3.3 Gender differences in CRC screening intentions........................................16

1.4 Intention, Decisional Conflict, and Health-Related Decisions............................17

1.5 Theoretical Models of Health Behaviour and Screening Intention.....................18

1.5.1 Health Belief Model...................................................................................19

1.5.2 Protection Motivation Theory....................................................................21

vii

1.5.3 Theory of Reasoned Action and Theory of Planned Behaviour.................23

1.5.4 Social Cognitive Theory.............................................................................24

1.5.5 “Major Theorists” Model...........................................................................24

1.6 Do Health Behaviour Models Effectively Predict CRC Screening Intentions?..26

1.7 A Dual Process Theoretical Framework.............................................................28

1.7.1 Evidence for dual information processing systems in decision-making....30

1.7.2 Heuristic processes, biases, and health behaviour decisions......................31

1.8 Summary and Thesis Outline..............................................................................33

1.8.1 Chapter summary........................................................................................33

1.8.2 Overview of the project..............................................................................34

CHAPTER 2. THE ROLE OF COGNITIVE FACTORS ON CRC SCREENING

INTENTION AND PARTICIPATION

2.1 Chapter Overview................................................................................................37

2.2 Cognitive Paradigms and Empirical Approaches to CRC Screening..................37

2.2.1 Risk perception...........................................................................................38

2.2.1.1 Risk perception as a predictor of CRC screening...............................38

2.2.1.2 Typologies of risk perception.............................................................40

2.2.1.3 Measuring risk perception in the context of cancer screening............41

2.2.1.4 Section summary: risk perception.......................................................42

2.2.2 Self-efficacy...............................................................................................43

2.2.2.1 Potential facilitators of screening self-efficacy...................................44

2.2.3 Test-efficacy...............................................................................................45

2.2.4 Cancer knowledge......................................................................................47

2.2.4.1 Knowledge and socioeconomic status................................................48

2.2.5 Worry..........................................................................................................50

2.2.5.1 Measurement of worry........................................................................51

2.3 Dual-Processing and Cognitive Bias: Recap of the Theoretical Framework......53

2.3.1 Heuristic biases: definitions.......................................................................53

2.4 Heuristic Biases and their Role in Behavioural Cancer Research.......................54

2.4.1 Availability errors in health behaviour decisions.......................................56

2.4.1.1 Availability, risk perception, and CRC...............................................57

2.4.2 Anchoring and adjustment errors in health behaviour decisions................57

2.4.3 Representativeness bias in health behaviour decisions..............................58

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2.5 Offsetting the ‘Cost’ of Heuristic Biases.............................................................60

2.6 Chapter Summary: Cognitions, Biases and Screening Intentions.......................62

CHAPTER 3: THE INFLUENCE OF EMOTION ON CRC SCREENING

INTENTION AND PARTICIPATION


3.2 How Emotions Differ from Cognitions in Explanations of Health Behaviour...66

3.3 Affect or Emotion? .............................................................................................68

3.4 Defining and Measuring Emotion.......................................................................69

3.5 Neuropsychological Evidence for the Role of Emotion in Decision-Making.....70

3.6 Emotional Biases and Decision-Making.............................................................71

3.7 Discrete Emotions and CRC Screening Intentions..............................................72

3.7.1Medical embarrassment...............................................................................73

3.7.1.1 Defining embarrassment.....................................................................73

3.7.1.2 Medical embarrassment and CRC screening......................................74

3.7.1.3 Typologies of medical embarrassment...............................................75

3.7.1.4 Gender differences in embarrassment.................................................76

3.7.2 Fear of screening........................................................................................77

3.7.2.1 Definition and measurement of fear...................................................78

3.7.2.2 The role of fear in cancer screening....................................................79

3.7.2.3 The effects of different sources of fear on cancer screening..............80

3.7.3 Disgust and CRC screening intentions.......................................................83

3.7.3.1 Definition of disgust...........................................................................83

3.7.3.2 Types and mechanisms of disgust.......................................................84

3.7.3.3 The role of disgust in CRC screening.................................................85

3.7.3.4 Gender differences in disgust..............................................................87

3.8 Integration of Cognitive and Emotion Factors in Decisions-Making.................87

3.9 Conclusion and Chapter Summary......................................................................88

CHAPTER 4: SOCIAL INFLUENCES ON CRC SCREENING INTENTION


4.2 Social Norms.......................................................................................................92

4.2.1 Classification and measurement of norms..................................................93

4.2.2 Social influence and health behaviour........................................................94

ix

4.2.3 Subjective norms and CRC screening........................................................95

4.3 Social Support ....................................................................................................97

4.3.1 Conceptualisation and typologies of social support in health research......97

4.3.1.1 Conceptualisation of social support....................................................97

4.3.1.2 Typologies of social support...............................................................98

4.3.2 Mechanisms of social support on health outcomes....................................99

4.3.3 Functional social support effects on CRC screening................................101

4.4 Chapter Summary..............................................................................................102

CHAPTER 5: INTEGRATION OF THE LITERATURE, THESIS OUTLINE, AND

AIMS AND HYPOTHESES OF STUDY 1

5.1 Chapter Overview..............................................................................................105

5.2 Rationale and Overall Aim of the Present Thesis.............................................105

5.3 Key Constructs of the Present Investigation.....................................................106

5.3.1 Summary of independent variables.......................................................106

5.3.2 Summary of dependent variables..........................................................107

5.4 Study 1: Exploring emotive, cognitive, and social variables in colorectal

cancer screening intention in a convenience sample.........................................109

5.4.1 Aims and rationale of Study 1...............................................................109

5.4.2 Study 1 hypotheses................................................................................110

Hypothesis 1. The relationships between demographic and health

variables with CRC screening intention and decisional conflict...........111

Hypothesis 2. The relationships between cognitive variables with CRC

screening intention and decisional conflict...........................................111

Hypothesis 3. The relationships between emotion variables with CRC


Hypothesis 4. The relationships between social variables with CRC


Hypothesis 5. The direct and mediational relationships between negative

emotions, self-efficacy, social factors, and screening intention............113

5.5 Summary of Partial Correlation and Mediation Hypotheses.............................115

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CHAPTER 6: STUDY 1. METHODOLOGY AND PSYCHOMETRIC PROPERTIES

OF SCALES


6.2 Method...............................................................................................................116

6.2.1 Participants............................................................................................116

6.2.2 Materials................................................................................................117

6.2.2.1 Demographic and health measures...................................................117

6.2.2.2 Cognitive measures...........................................................................118

6.2.2.3 Emotion measures.............................................................................124

6.2.2.4 Social measures.................................................................................127

6.2.2.5 Dependent measures.........................................................................128

6.2.2.6 Organisation of survey materials......................................................129

6.2.3 Procedure...............................................................................................130

6.2.3.1 Ethical approval................................................................................131

6.3 Data Preparation and Screening........................................................................132

6.3.1 Missing data...........................................................................................132

6.3.2 Outliers..................................................................................................134

6.3.3 Normality...............................................................................................135

6.3.4 Linearity and homoscedasticity.............................................................136

6.3.5 Criterion validity and intercorrelations between variables....................136

6.3.6 Scale reliability......................................................................................137

6.4 Data Preparation and Screening: Decision Summary.......................................137

CHAPTER 7: STUDY 1 ANALYSES AND RESULTS: Exploring cognitive, emotion

and social relationships with CRC screening intention and decisional conflict in a

convenience sample


7.2 Sample Characteristics......................................................................................140

7.2.1 Health and screening practices of the sample...........................................142

7.2.2 Family history of CRC.............................................................................145

7.3 Factor Analysis: Description and Development of the Confirmatory Factor

Analysis Process................................................................................................145

7.3.1 Description of processes involved in CFA...............................................146

7.3.2 Development of measurement models.....................................................146

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7.3.2.1 Assumptions......................................................................................146

7.3.2.2 Model identification..........................................................................147

7.3.2.3 Goodness-of-Fit indices....................................................................147

7.3.2.4 Fit criteria used in the present research.............................................148

7.4 Scale Development and Refinement: Measurement Models.............................150

7.4.1 Disgust ................................................................................................151

7.4.2 Medical embarrassment.......................................................................152

7.4.3 Fear of screening..................................................................................153

7.4.4 Social support.......................................................................................155

7.4.5 Social norms.........................................................................................155

7.4.6 Self-efficacy.........................................................................................156

7.4.7 Test-efficacy........................................................................................157

7.4.8 Screening bias......................................................................................159

7.4.9 Cancer worry........................................................................................160

7.4.10 Risk perception..................................................................................161

7.4.11 Screening intention............................................................................162

7.4.12 Decisional conflict.............................................................................164

7.4.13 Knowledge.........................................................................................164

7.4.14 Summary of factor analyses...............................................................165

7.5 Results: Study 1 Hypothesis Testing.................................................................168

Hypothesis 1: The relationships between demographic and health variables with

CRC screening intention and decisional conflict..............................................168

Hypothesis 2: The relationships between cognitive variables with CRC

screening intention and decisional conflict.......................................................173

Hypothesis 3: The relationships between emotion variables with CRC screening

intention and decisional conflict........................................................................176

Hypothesis 4: The relationships between social variables with CRC screening

intention and decisional conflict........................................................................179

Hypothesis 5: Mediation of social-cognitive variables on negative emotions and

screening intention.............................................................................................180

7.6 Summary of Findings........................................................................................182

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CHAPTER 8: METHODOLOGY AND PSYCHOMETRIC PROPERTIES OF

SCALES IN STUDY 2


8.2 Method...............................................................................................................183

8.2.1 Participants...............................................................................................183

8.2.2 Materials...................................................................................................183

8.2.2.1 Demographic and health measures...................................................184

8.2.2.2 Cognitive measures...........................................................................185

8.2.2.3 Emotion measures.............................................................................187

8.2.2.4 Social measures.................................................................................189

8.2.2.5 Dependent measures.........................................................................190

8.2.3 Procedure..................................................................................................191

8.2.3.1 Ethical approval................................................................................192

8.3 Data Screening and Cleaning............................................................................192

8.3.1 Data screening..........................................................................................192

8.3.2 Missing data..............................................................................................193

8.3.3 Outliers.....................................................................................................194

8.3.4 Normality, linearity, and homoscedasticity..............................................194

8.3.5 Multicollinearity.......................................................................................195

8.3.6 Scale reliability.........................................................................................195

8.4 Chapter Summary..............................................................................................196

CHAPTER 9: STUDY 2 AIMS AND HYPOTHESES


9.2 Aims and Rationale of Study 2..........................................................................198

9.3 Study 2 Hypotheses...........................................................................................199

9.3.1 Description of planned statistical analyses...............................................199

9.3.2 Hypothesis 1(a) to 1(g): Associations......................................................200

9.3.3 Hypotheses 2 to 5: Discriminant function analysis..................................202

9.3.3.1 Hypotheses 2 to 5: Discriminants of screening intention,

participation and decisional conflict......................................................202

xiii

CHAPTER 10: STUDY 2 ANALYSES AND RESULTS. An Australian community

study on CRC screening intention and behaviour: Social-cognitive and emotion

discriminants


10.2 Sample Characteristics......................................................................................204

10.3 Health and Screening Practices of the Sample..................................................207

10.4 Results in Relation to the Hypotheses...............................................................210

10.4.1 Hypothesis 1(a) to 1(g)...........................................................................210

10.4.2 Hypotheses 2 to 5: Discriminant function analysis...............................222

10.5 Chapter Summary..............................................................................................234

CHAPTER 11: GENERAL DISCUSSION


11.2 Brief Review of Project Aims...........................................................................237

11.2.1 Aims of Study 1......................................................................................237

11.2.2 Aims of Study 2......................................................................................237

11.3 Discussion of the Findings in Relation to Demographic and Health Status

with Screening Intention and Decisional Conflict.............................................237

11.3.1 CRC screening in relation to age............................................................238

11.3.2 CRC screening in relation to partnership status.....................................239

11.3.3 Health insurance, socioeconomic status, and CRC screening................240

11.3.4 Gender in CRC screening.......................................................................241

11.3.5 Past cancer screening behaviour.............................................................243

11.3.6 Gastrointestinal health............................................................................243

11.3.7 Family history and GP advice.................................................................244

11.4 Decisional Conflict and CRC Screening...............................................................245

11.5 The Role of Cognitive Variables in CRC Screening: Discussion of the Findings in

Relation to Cognitive Hypotheses.................................................................................245

11.5.1 Risk perception.......................................................................................246

11.5.2 Self-efficacy............................................................................................248

11.5.3 Test-efficacy...........................................................................................249

11.5.4 Cancer worry..........................................................................................251

11.5.5 Knowledge..............................................................................................253

11.5.5.1 Knowledge and screening bias................................................255

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11.5.6 Screening bias.........................................................................................256

11.6 The Role of Social Factors in CRC Screening......................................................259

11.6.1 Social support.........................................................................................259

11.6.2 Social norms...........................................................................................261

11.7 The Role of Discrete Emotions in CRC Screening...............................................262

11.7.1 Emotion and CRC screening..................................................................263

11.7.2 Fear and CRC screening.........................................................................264

11.7.2.1 Fear of procedural aspects............................................................264

11.7.2.2 Fear of cancer...............................................................................265

11.7.2.3 Fear of embarrassment..................................................................266

11.7.3 Disgust and CRC screening....................................................................267

11.7.4 Medical embarrassment and CRC screening..........................................268

11.7.5 The relationships between emotion, self-efficacy, and social support with

screening intention and participation.................................................................270

11.7.5.1 Negative emotion and self-efficacy.........................................270

11.7.5.2 Negative emotion and social support......................................271

11.7.6 Emotion and decisional conflict.............................................................271

11.7.7 Summary: The effect of negative emotion on CRC screening...............272

11.8 Limitations.............................................................................................................273

11.8.1 Sampling limitations...............................................................................273

11.8.2 Study design and measurement limitations............................................275

11.8.2.1 Theoretical limitations.............................................................276

11.9 Implications of the Findings..................................................................................277

11.9.1 Implications for research: measurement and theory...............................277

11.9.2 Implications for intervention..................................................................277

11.9.3 Public health and policy implications.....................................................279

11.10 Further Research..................................................................................................280

11.11 Conclusion...........................................................................................................283

REFERENCES..............................................................................................................287

APPENDICES...............................................................................................................338

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LIST OF TABLES

1.1 International Uptake of Colorectal Cancer Screening.........................................12

2.1 Cognitive Variables, their Theoretical Origins, and the Definition Applied in the

Present Investigation...........................................................................................64

3.1 An Example of Participant Reporting of Specific Barriers for each Colorectal

Cancer Screening Procedure by Emotion and Cognition....................................67

3.2 Discrete Emotion Variables and the Definition Applied in the Present

Investigation........................................................................................................91

4.1 Social Variables, their Theoretical Origin, and the Definition Applied in the

Present Investigation.........................................................................................104

5.1 Factors Under Examination in the Present Investigation of Cognitive, Emotion,

and Social Predictors of Colorectal Cancer Screening......................................108

5.2 Summary of Study 1 Hypotheses of the Relationships Between Cognitive,

Emotion, and Social Variables with Screening Intention and Decisional

Conflict..............................................................................................................114

6.1 Screening Bias Instrument.................................................................................123

6.2 Sample Means, SDs, Scale Ranges, and Cronbach’s Alphas for Variables Prior

to Factor Analysis..............................................................................................139

7.1 Sample Characteristics on Demographic Variables in Study 1 (N = 202)........141

7.2 Sample Characteristics on Health Variables in Study 1 (N = 202)...................143

7.3 Screening Intention by Gender and Age Group................................................144

7.4 Goodness-of-Fit Indices for Emotion and Social Variable Measurement Models

(CFA).................................................................................................................154

7.5 Goodness-of-Fit Indices for Cognitive Variable Measurement Models (CFA)

..........................................................................................................................158

7.6 Goodness-of-Fit Indices for Outcome Variable Measurement Models

(CFA).................................................................................................................163

7.7 Revised Reliability, Scale Items and Inter-Item Correlations for Variables Pre-

and Post-Factor Analysis...................................................................................166

7.8 Means, Standard Deviations, Observed and Possible Ranges for all Scales Post-

Factor Analysis (N = 202) ................................................................................167

7.9 Means and Standard Deviations for Demographic Variables...........................170

7.10 Means and Standard Deviations for Health Variables......................................171

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7.11 Correlations Between Demographic, Health Variables, and Screening Intentions

...........................................................................................................................172

7.12 Bivariate Pearson Correlations Between Cognitions, Screening Intentions, and

Decisional Conflict............................................................................................175

7.13 Bivariate Pearson Correlations Between Emotion and Dependent

Variables............................................................................................................178

7.14 Bivariate Pearson Correlations Between Social Support, Social Norms, and

Dependent Variables.........................................................................................179

8.1 Sample Means, SDs, Scale Ranges, and Cronbach’s Alphas for Study 2

Variables............................................................................................................197

10.1 Sample Characteristics on Demographic Variables in Study 2 (N = 240)........206

10.2 Sample Characteristics on Health Variables in Study 2 (N = 240)...................209

10.3 Correlation Matrix of Demographic and Dependent Variables in the Community

Sample...............................................................................................................211

10.4 Correlation Matrix of Health and Dependent Variables in the Community

Sample...............................................................................................................214

10.5 Correlation Matrix of Cognitive Variables, Social Support, and Dependent

Variables in the Community Sample.................................................................217

10.6 Correlation Matrix of Emotion Variables, Social Support, and Dependent

Variables in the Community Sample.................................................................221

10.7 Independent Variables Included in Discriminant Function Analyses and the

Predicted Direction of the Relationship with Outcome Variables....................223

10.8 Discriminant Function Analysis Statistics.........................................................225

10.9 Discriminant Function Analyses Pooled Within Groups Correlations..............231

10.10 Summary of the Discriminating Variables (in Order of Importance) for each

Discriminant Function (Dis)..............................................................................232

10.11 Means, SDs, and F-Statistics for each Discriminant Function Analysis and their

Discriminant Variables......................................................................................233

10.12 Summary of Significant Associations with the Outcome Variables.................235

xvii

LIST OF FIGURES

Figure 1.1 Diagram of the digestive tract (The Cancer Council, 2010).....................5

Figure 1.2 TNM staging of colorectal cancer (American Joint Committee on

Cancer [AICC], 2010)...............................................................................6

Figure 1.3 Major theorists’ model of health behaviour............................................26

Figure 5.1 Expected partial correlation between screening bias, cancer knowledge,

and screening intention..........................................................................115

Figure 5.2 Expected partial correlation between risk perception, worry, and

screening intention.................................................................................115

Figure 5.3 Expected mediation between negative emotions (fear, disgust, and

embarrassment uniquely) self-efficacy, social support, social norms, and

screening intention.................................................................................115

Figure 7.1 Final 4-factor measurement model of disgust with standardised

estimates................................................................................................151

Figure 7.2 Final second-order 3-factor measurement model of the Medical

Embarrassment Questionnaire with standardised estimates..................152

Figure 7.3 Final 3-factor measurement model of the fear scale with standardised

estimates................................................................................................153

Figure 7.4 Final unmodified measurement model of social support with

standardised estimates...........................................................................155

Figure 7.5 Final measurement model of subjective social norms with standardised

estimates................................................................................................155

Figure 7.6 Final measurement model of self-efficacy with standardised

estimates................................................................................................156

Figure 7.7 Final measurement model of test-efficacy with standardised

estimates................................................................................................157

Figure 7.8 Final measurement model of screening bias with standardised

estimates................................................................................................159

Figure 7.9 Final measurement model of cancer worry with standardised

estimates................................................................................................160

Figure 7.10 Final correlated measurement model of risk perception (with social

support), with standardised estimates....................................................161

xviii

Figure 7.11 Final measurement model of screening intention (correlated with test-

efficacy), with standardised estimates...................................................162

Figure 7.12 Final measurement model of decisional conflict with standardised

estimates................................................................................................164

Figure 7.13 Partial mediation by self-efficacy (SE) and partial mediation by social

support (SS) on the relationship between screening fear and screening

intention.................................................................................................181

Figure 7.14 Partial mediation of social support (SS) and full mediation of self-

efficacy on the relationship between medical embarrassment and

screening intention ...............................................................................181

Figure 10.1 Box plots illustrating the distribution of discriminant scores for non-

intenders of FOBt and intenders of FOBt..............................................225

Figure 10.2 Box plot illustrating the distribution of discriminant scores for non-

intenders of colonoscopy and intenders of colonoscopy.......................226

Figure 10.3 Box plot illustrating the distribution of discriminant scores for screeners

and non-screeners..................................................................................228

Figure 10.4 Box plots illustrating the distribution of discriminant scores for low

decisional conflict and high decisional conflict groups........................230

xix

LIST OF APPENDICES

Appendix A: SUHREC Ethical Clearance for Study 1 and Study 2......................338

Appendix B: List of Variables and their Reliability Coefficients from Study 1 and

Study 2 Survey Questionnaires.........................................................341

Appendix C: Study 1 Annotated Survey Questionnaire.........................................344

Appendix D: Study 1 Recruitment Advert and Promotional Materials..................362

Appendix E: Study 1 Results of Precautionary Hypotheses..................................365

Appendix F: Study 1 Correlation Matrices for all Cognitive, Social and Emotion

Variables Prior to Factor Analysis....................................................370

Appendix F.1 Correlation Matrix of Predictors with the Outcome

Variables, Intention and Decisional Conflict (Prior to Factor Analyses,

Study 1) ............................................................................................371

Appendix F.2 Correlation Matrix on Outcome Variables by Gender

(Males in Upper Quadrant, Females in Lower Quadrant) (Prior to

Factor Analysis, Study 1) .................................................................372

Appendix F.3 Correlations by Gender for the Predictor Variables

(Males in Upper Quadrant, Females in Lower Quadrant) (Prior to

Factor Analysis, Study 1) .................................................................373

Appendix G: Exploratory and Confirmatory Factor Analysis for Study 1: Full

Description of Measurement Model and Scale Refinement.............374

Appendix G.1 Measurement Model Overview.................................379

Appendix G.2 Measurement Models of Emotion Factors................379

Appendix G.3 Measurement Models of Cognitive Factors..............415

Appendix G.4 Measurement Models of Social Factors....................425

Appendix G.5 Measurement Models of Outcome Variables............427

Appendix H: Hypothesis 5 (Study 1) Mediation Analyses: Full Description........438

Appendix I: Study 1 Correlations Between All Cognitive, Emotion and Social

Variables with the Dependent Variables (Post Factor Analysis)......446

Appendix J: Study 2 Annotated Survey Questionnaire.........................................448

Appendix K: Study 2 List of Recruitment Sources and Promotional Materials....460

Appendix L: Study 2: Correlation Matrix of Cognitive, Social, Emotive, and

Dependent Variables in the Community Sample..............................465

Appendix M: Study 2 Means, SDs, and F-Statistics for the Discriminant Function

Analysis Predictors...........................................................................467

xx

LIST OF CONFERENCE PRESENTATIONS ARISING FROM THIS RESEARCH

Conference presentation

Hamilton, V., & Moore, S. (2009). Emotions and cognitively biased thinking in

patient colorectal cancer screening decisions: A pilot study. The 44th Australian

Psychological Society (APS) Annual Conference. Darwin, Northern Territory:

30 September to 4 October 2009.

Other

Invited participation at the PEALS (Policy, Ethics and Life Sciences) smartHEALTH

symposium. Newcastle-Upon-Tyne, England, 18-19 May 2009.

xxi

LIST OF ABBREVIATIONS

CRC Colorectal cancer

FOBt Faecal Occult Blood test

CS Colonoscopy

FS Flexible sigmoidoscopy

NBCSP National Bowel Cancer Screening Program (Australia)

DHA Department of Health and Ageing (Australia)

IRSD Index of Relative Socioeconomic Disadvantage

AIHW Australian Institute of Health and Welfare

ABS Australian Bureau of Statistics

FDR First degree relative

SES Socio-Economic Status

TPB / TRA Theory of Planned Behaviour / Theory of Reasoned Action

PBC Perceived behavioural control

HBM Health Belief Model

PMT Protection Motivation Theory

SCT Social Cognitive Theory

MTM “Major Theorists” Model

xxii

ABBREVIATIONS AND GLOSSARY: CONFIRMATORY FACTOR ANALYSIS

Bootstrapping Resamples the data set without violating the assumptions of

normality, and can be used to infer the significance of a

hypothesis. In this thesis, the data set was resampled 2000

times.

Confirmatory factor

analysis (CFA)

Assesses and confirms the stability of a set of hypothesised

factors

Congeneric model A single-factor model

Direct effect The path from an unobserved variable to its indicators

(measurement model)

Indicator The items of a scale (measurement model)

Indirect effect A mediated effect, where an intervening or mediating variable

conveys some of the causal effects between a primary

predicting variable and the outcome variable

Latent variable An unobservable variable, represented by observed/measured

variables

Second-order model Also referred to as a ‘higher-order’ model. Can be

hypothesised to explain strong inter-correlations between the

latent variables of a measurement model (amongst the first-

order or ‘lower-order’ factors).

Squared multiple

correlation

Proportion of variance in the dependent variable that is

accounted for by the model. It can also be used to provide an

estimate of the communality of a variable with other variables

in the model.

SRW Standardised regression weight (factor loadings)

SRCM Standardised Residual Correlation Matrix

xxiii

ABBREVIATIONS AND GLOSSARY: GOODNESS-OF-FIT INDICES

Normed chi-square

(CMIN/df)

In this thesis, values > 1.0 and < 3.0 are deemed to reflect

adequate fit.

Root Mean Square

Error of

Approximation

(RMSEA)

The RMSEA uses the noncentral chi-square distribution,

measuring the level of falseness of the null hypothesis. Values

below .08 or .10 are recommended. This thesis applies a criteria

of < .08.

Root Mean-square

Residual (RMR) and

Standardised Root

Mean-square

Residual (SRMR)

This index reflects the average value of the covariance residuals

and should be as close to zero as possible. SRMR however, is a

better reflection of fit because it is based on the average

correlation residual (which are standardized values), and

therefore values below .10 are recommended.

Comparative Fit

Index (CFI)

Similar to RMSEA, assesses the proportion of improvement in

noncentrality between the null and proposed model. Values

greater than .90 imply reasonable fit.

Tucker Lewis Index

(TLI) and Non

Normed Fit Index

(NNFI)

The TLI and NNFI compare the proposed model against a null

(or alternative) model, favouring simpler more parsimonious

models and penalizing model complexity. The NNFI value is a

converted chi-square value between 0 and 1.0.

Goodness-of-Fit

Index (GFI);

Adjusted Goodness-

of-Fit Index (AGFI)

Captures proportion of variance explained by the model. Values

close to 1.0 indicate perfect model fit, while less than .90

indicate poor fit.

Akaike Information

Criterion (AIC);

Consistent AIC

(CAIC)

Compares model fit against hypothetical replication samples of

the same size and population, with lower values indicating better

fit. CAIC is less likely to penalize model complexity than similar

alternative fit indices.

1

CHAPTER 1

COLORECTAL CANCER SCREENING INTENTIONS AND BEHAVIOUR:

BACKGROUND, DEFINITION, AND MEASUREMENT

1.1 Background to Research Area

Colorectal cancer (CRC) is the most frequently diagnosed internal, non-cutaneous

cancer, and is the second most common cause of cancer-related death in Australia

(Department of Health and Ageing [DHA], 2010), the US (Colon Cancer Alliance, 2010),

and the UK (Hewitson, Woodrow, & Austoker, 2008). Proposals for CRC population

screening were made in Australia over twenty years ago (using Faecal Occult Blood (FOB)

tests; Dent, Bartrop, Goulston, & Chapuis, 1983) however, uptake of colorectal cancer

(CRC) screening in Australia has been documented as sub-optimal since its inception in the

first phase of a piloted national program conducted between 2002 and 2004 (DHA, 2010).

Psychosocial influences on CRC screening are logical avenues of investigation to

determine population screening acceptance and the feasibility of a screening program.

A limited Australian National Bowel Cancer Screening Program (NBCSP) has been

phased in slowly since July 2008 (DHA, 2010) in an attempt to curb the morbidity and

mortality burden of CRC in Australia. The continuation of the national screening program

was recommended following the successful conclusion of phase one, The Bowel Cancer

Screening Pilot Program (DHA, 2005), and by leading commentators in colorectal

medicine (Macrae, 2005). The advent of a national program would raise the opportunity to

engage in bowel cancer screening to a level not witnessed before in Australia.

The subsequent staggered introduction of the second phase of the NBCSP is

currently on-going and a comprehensive appraisal of its feasibility and success is not yet

possible, however, in terms of public reception, the screening pilot program from 2002 to

2004 was only moderately well received within the communities it was conducted

(Mackay, Adelaide, and Melbourne), with an uptake of 45.5% (DHA, 2010). Published

participation rates in phase two of the NBCSP may experience further delay due to a six-

month stall of the screening program, however early reports imply similarly low uptake

(approximately 40.1%; Ananda et al., 2009; Wenham & Russell, 2011).

2

In Australia there is a government health focus on managing the impact of lung,

skin, cervix, breast, colorectal, and prostate cancers, and non-Hodgkin’s lymphoma

(Australian Institute of Health & Welfare [AIHW], & Australasian Association of Cancer

Registries, 2008). Cancer became the main disease burden in 2003 (outranking

cardiovascular disease, which is witnessing more rapid medical advancements), and this is

a trend predicted to continue over the next decade (Begg et al., 2007). Because of

improvements in technology and reasonably low rates of false-positive results, it has been

argued that in those cancers where pre-cancerous cells or adenomas can be detected (e.g.,

cervical or CRC), the screening test significantly reduces cancer incidences and mortalities

(Cuzick, 2004, cited in Orbell, Hagger, Brown, & Tidy, 2006; Mandel et al., 2000; Mandel,

Church, Ederer, & Bond, 1999).

Screening to detect colorectal cancer at earlier and therefore more treatable stages

will become increasingly available. The success and feasibility of any national screening

program depends largely on community acceptance and compliance with the proposed

program. The realization and success of such schemes is globally topical as CRC screening

programs have been piloted or implemented internationally, including in 25 states across

the United States (US) (Centers for Disease Control and Prevention [CDC], 2011), the

United Kingdom (UK) (National Health Service, 2010), and Japan, Italy, France, and

Spain, amongst other developed nations (Benson et al., 2008). Initial assessment of the UK

program, which is ahead of the implementation of the Australian program, suggests that

uptake has been low (between 32 and 49% in the first screening round; von Wagner et al.,

2009), and is poorer than that seen during their pilot program (Chapple, Ziebland,

Hewitson, & McPherson, 2008). Colorectal cancer screening may present more unique

challenges as a screening program due to the requirement of the participant to be actively

involved in the screening test (Weller, 2010). Based on the reviews of piloted programs

internationally, it appears there may be common social-cognitive and emotion factors

affecting uptake of CRC screening. The focus of this research will therefore be to address

the primary research question of the cognitive, emotion, and social factors affecting CRC

screening intentions and participation in an Australian sample. A feature of the research is

to develop and validate a series of questionnaires which can be used to identify the key

3

variables that are able to distinguish intenders and screeners from those who do not intend,

or decline CRC screening.

This central research question is first explored by reviewing each of the literatures

in these three domains to establish an empirical basis for cognitive, emotive and social

influences on the CRC screening decision. Two studies will then be presented. The first

preliminary study seeks to develop and identify optimal measures of the psychosocial

predictors of intention, and validate these measures by examining the relationships between

these variables and intention. In particular the goal is to establish psychometric soundness

of the selected measures of the potential predictors of intention. Fixed factors such as

demographic and health status have also been reported to strongly influence intention and

particularly participation (Clavarino et al., 2004; Denberg et al., 2005; Emmons et al.,

2008; Friedman, Webb, & Everett, 2004; McQueen et al., 2007), and are therefore explored

for their relationships with, and ability to predict screening in combination with social-

cognitive and emotion variables. A subsequent follow-up study examines these refined

measures in a community sample of Australians in the target screening age group, assessing

the combinations of emerging and currently accepted variables that best predict both

screening intenders and actual (retrospective) screening participation. Decisional conflict

associated with the screening decision was also assessed for its relationship with the

primary outcome constructs (intention and action) and its utility in discriminating screeners

and intenders from non-screeners and non-intenders, respectively.

The remainder of this chapter reports on the epidemiology and screening tests

currently available for CRC (Section 1.2); how CRC screening intentions are examined in

the health behaviour literature, and the influence of ‘fixed factors’ such as health and

demographic status (Section 1.3); the major social-cognitive health behaviour models and

their success in accounting for variance in screening intentions, and their key variables

(Section 1.5); and emerging areas of research in the investigation of CRC screening

intentions (Section 1.6).

4

1.2 Status of Colorectal Cancer Screening in Australia

1.2.1 Epidemiology and medical definition

1.2.1.1 Epidemiology. CRC is the second leading cause of cancer-related death in

Australia (DHA, 2010). Up to the age of 85 years, 10% of men and 7% of women in

Australia will be diagnosed with CRC (Cancer Council Australia [CCA], 2011). More than

4,000 cancer-related deaths in Australia in the year 2009 were attributed to CRC

(Australian Bureau of Statistics [ABS], 2011), and there are more than 13,500 new CRC

diagnoses each year (CCA, 2011). In other words, each week approximately 260 new cases

of CRC are diagnosed, while around 80 Australians die from the disease (CCA, 2011;

DHA, 2010).

The incidence of CRC is strongly related to older age, and although it can occur at

any age, less than 1% of cases develop in people under the age of 35 (AIHW, 2004;

Australian Cancer Network CRC Guidelines Revision Committee, 2005). Risk factors

include a family history of CRC, or a personal history of bowel polyps or inflammatory

bowel disease. Individuals deemed to be at high risk might be advised to commence

screening from age 40. The remaining population is considered to be at ‘average’ risk

automatically from age 50 (CCA, 2011), and is classed as those who have no family history

of bowel cancer, and a history of bowel or digestive good-health. Lifestyle factors, such as

smoking, alcohol consumption, a diet lacking in fruit and vegetables, and inadequate

physical exercise, are also known to play a role and can, to varying degrees, contribute to

vulnerability to developing CRC (Australian Cancer Network CRC Guidelines Revision

Committee, 2005). In the Australian NBCSP, the majority of the population considered to

be at average risk of CRC (that is, those over 50 years of age) declined to participate in the

bowel cancer screening program (54.5%; DHA, 2010).

1.2.1.2 Definition. CRC (or bowel cancer) is a slow developing, often

asymptomatic, disease affecting the intestinal tract (see Figure 1.1), and this is one of the

reasons national screening is argued by many expert commentators to be imperative

(Hardcastle & Chamberlain, 1996; Levin et al., 2008; Mandel et al., 1999; Tan & Seow-

Choen, 2007). Colorectal cancer begins as a benign polyp in the colon or rectum, and it can

take a long period (e.g., up to ten years; Winawer et al., 2003) before evolving into

malignancy.

Vic

Typewritten Text

Vic

Typewritten Text

Vic

Typewritten Text

Vic

Typewritten Text

5

Figure 1.1. Diagram of the digestive tract (The Cancer Council, 2010).

There are formally recognised stages of colorectal cancer in medicine that are

designed to indicate degree of invasiveness of the cancer. The four characteristic stages

of CRC according to the Dukes system of classification (Dukes, 1932) include Stage I

(tumour is confined to the intestinal wall); Stage II (tumour has begun to invade the

intestinal wall); Stage III (lymph nodes have been invaded); and Stage IV (distant

metastasis has occurred). Stages II to IV are associated with higher rates of mortality. A

more recent 10-stage system is also used (Greene & Sobin, 2008; Sobin,

Gospodarowicz, & Wittekind, 2009) and is depicted in Figure 1.2.

Late detection of bowel cancer is a significant obstacle in reducing population

mortality, at which point an individual’s recovery prognosis is often as low as 10% and

cannot be achieved on the basis of surgery alone (CCA, 2010). Given the increased

probability of metastasis in late-stage diagnoses and that the principal prognostic factor

is stage of detection (Deans, Parks, Rowlands, & Spence, 1992) it has been argued that

implementation of national screening will lead to earlier detection and subsequent

greater survival rates in existing cancers, in addition to the preventative nature of

screening by the removal of benign lesions.

Figure 1.2. TNM staging of colorectal cancer

[AJCC], 2010).

The only way to detect most cancers in their

point patients are likely to be asymptomatic. In fact, for many cancers, symptoms can be

non-specific and therefore difficult to monitor and identify (

Domenighetti, & Gelber, 2003; Khattak, Eardley, & Rooney, 2006

for colorectal cancer, where colonic symptoms may not be present or may not be good

predictors of polyps or cancer (Bafandeh

many symptoms of CRC, such as abdominal pain or bloating, can be vague or mimic

benign illness, the predictive value of such symptoms is limited (Hamilton & Sharp, 2004;

Korsgaard, Pedersen, Sørensen, & Laurberg, 2006

The symptom of rectal bleeding is thought to

CRC via FOB testing, and has been associated with shorter diagnostic delays and less

advanced stages of cancer (Korsgaard et al., 2006).

1.2.2 CRC screening tests

Screening has been defined as the process of examining asymptomatic individuals

to determine whether they have, or are at risk of having, a particular disease (

1995). It involves testing the ‘well’ person who does not necessarily exhibit any symptoms

Stage T: Primary tumour (6 phases)

(degree of invasion into intestinal wall)

Stage N: Regional lymph nodes (3 phases)

(level of lymph node invasion)

Stage M: Distant metastasis (dichotomous phase)

(presence of absence of metastasis)

TNM staging of colorectal cancer (American Joint Committee on Cancer

The only way to detect most cancers in their early stages is via screening, at which

patients are likely to be asymptomatic. In fact, for many cancers, symptoms can be

specific and therefore difficult to monitor and identify (Goldhirsch, Colleoni,

Domenighetti, & Gelber, 2003; Khattak, Eardley, & Rooney, 2006). This is especially true


predictors of polyps or cancer (Bafandeh, Khoshbaten, Sadat, & Farhang, 2008). Given that

, such as abdominal pain or bloating, can be vague or mimic

enign illness, the predictive value of such symptoms is limited (Hamilton & Sharp, 2004;

Korsgaard, Pedersen, Sørensen, & Laurberg, 2006; Majumdar, Fletcher, & Evans, 1999).

is thought to present the best opportunity for detection of

, and has been associated with shorter diagnostic delays and less

advanced stages of cancer (Korsgaard et al., 2006).


o determine whether they have, or are at risk of having, a particular disease (Marshall

the ‘well’ person who does not necessarily exhibit any symptoms

Stage T: Primary tumour (6 phases)

(degree of invasion into intestinal wall)

Stage N: Regional lymph nodes (3 phases)

(level of lymph node invasion)

Stage M: Distant metastasis (dichotomous phase)

(presence of absence of metastasis)

6

(American Joint Committee on Cancer

at which

patients are likely to be asymptomatic. In fact, for many cancers, symptoms can be

). This is especially true


2008). Given that

, such as abdominal pain or bloating, can be vague or mimic

enign illness, the predictive value of such symptoms is limited (Hamilton & Sharp, 2004;

, 1999).

ction of

, and has been associated with shorter diagnostic delays and less


Marshall,

the ‘well’ person who does not necessarily exhibit any symptoms

7

of disease. There are currently a small number of colorectal cancer screening tests available

that fall into two broad categories: structural exams and stool tests. While stool tests are

primarily aimed at identifying possible CRC, structural tests use endoscopy, which can

visually detect benign or malignant polyps (Levin et al., 2008; Vasko, 2008). The

appropriateness of which test to undergo can be dependent on personal risk factors and

medical history, but generally the stool test is recommended as an initial indication of

possible carcinoma. Four of the most common screening tests include faecal occult blood

testing (FOBt), flexible sigmoidoscopy (FS), colonoscopy (CS), and double contrast barium

enema, the latter three being structural exams.

1.2.2.1 Faecal occult blood testing. The national pilot program entailed the use of

the immunochemical Faecal Occult Blood test (FOBt) called ‘DetectTM’ (Siemens Medical;

DHA, 2011), which is designed to detect minute traces of hidden human haemoglobin in

the stool. Immunochemical tests use anti-haemoglobin antibodies to detect traces of human

blood, and because they do not react to the peroxidase in certain foods they do not require

any dietary restrictions prior to use (Castiglione, Grazzini, & Ciatto, 1992).

FOB testing has not been without major criticism. One criticism is that there are

flaws in its test sensitivity (the proportion of positive test results it can detect among people

who have the disease) and specificity (falsely detecting malignancy in healthy, cancer-free

individuals). Despite some of the misgivings expressed about the use of FOB testing, it is a

test that lends itself to mass private and non-invasive screening at relatively small cost,

which may partly negate the impact of poor specificity and limited sensitivity (Campbell et

al., 2004). A review of randomised controlled trials (RCTs) (Walsh & Terdiman, 2003)

established that screening with a FOB test reduces bowel cancer mortality from between

15% to 33%, which in Australia represents up to 27 lives saved each week. Walsh and

Terdiman (2003) note that for the prevention of one bowel cancer death, at least 217 people

would need to screen annually. This highlights the challenge of improving the moderately

low participation rates reported in screening trials and programs globally, and the

importance of understanding the psychosocial factors that may be thwarting participation.

1.2.2.2 Flexible sigmoidoscopy. Flexible sigmoidoscopy (FS) is an endoscopy that

investigates the lower colon, or ‘sigmoid’ colon and rectum, where just under half of

malignant adenomas are detected (Obrand & Gordon, 1998). As the tube does not need to

8

be manoeuvred through the larger colon (as in colonoscopy), its risk is correspondingly

lower. Procedural complications were reviewed retrospectively by Walsh and Terdiman

(2003), finding perforations to have occurred twice out of 49,501 FS procedures (0.004%).

Another study investigating complications of FS reported 31 perforations out of 35,298

procedures (0.088%) (Gatto et al., 2003).

FS has been investigated in three randomised controlled trials (RCT), with a CRC

incidence reduction between 23 and 80% and reduced mortality of 67% (Bretthauer, 2010).

Hoff, Grotmol, Skovlund, and Bretthauer (2009) report the initial results of a large-scale

population-based RCT in Norway. While control patients and FS patients did not differ in

the incidence of CRC at a seven-year follow-up, there was a non-significant trend in

reduced mortality caused by CRC (by 27%) and rectosigmoidal cancer (by 37%) in the FS

group. The authors suggest that the 7-year follow-up may be an insufficient lag-time for

exploring the efficacy of FS, given that polyps can take 10 to 15 years to develop into

cancer, and that more convincing evidence for the efficacy of FS may be found at a 15-year

follow-up.

1.2.2.3 Colonoscopy. Colonoscopy (CS) has been thought the gold-standard

screening test for colorectal cancer (Atkin, 1999; Lieberman, 2004). Repeatable every ten

years if no adenomas are found, CS is able to examine the full colon for lesions. The

procedure itself is considered reasonably painless (Ristvedt, McFarland, Weinstock, &

Thyssen, 2003), and while anaesthetic or sedation is offered to the patient (Lieberman et al.,

2000), colonoscopy can be performed without sedation, depending on patient preference

(Cataldo, 1996; Rex, Imperiale, & Portish, 1999). Risk of perforation is understandably

slightly higher than FS given the greater depth of camera insertion, and has ranged from

0.02% (Rathgaber & Wick, 2006) to 0.20% (Gatto et al., 2003), while an Australian review

of CS in a teaching hospital environment found 0.01% of colonoscopies (three patients)

over a 10 year period resulted in death (Viiala, Zimmerman, Cullen, & Hoffman, 2003).

Despite its generally respected reputation, there are no RCTs comparing CS to

control groups to evaluate the long-term efficacy of colonoscopy. Despite having greater

specificity and sensitivity than FOBt, colonoscopy efficacy has been questioned following

recent investigations into its performance indicators, exposing cancer miss rates between

3% and 5.5% (Harewood, 2007; Harris et al., 2007). Non-RCT findings reveal a reasonably

9

powerful screening test, with the decline in mortality found in FOBt RCTs attributable to

follow-up colonoscopy investigations (Mandel et al., 1999; Walsh & Terdiman, 2003).

Research conducted in Australia and the United States tends to support screening by

CS for average-risk patients (with no history of adenomas) at five- to ten-year intervals or

as a follow-up to positive FOBt results (Bolin et al., 1999; Pignone, Saha, Hoerger, &

Mandelblatt, 2002). A testament that colonoscopy is considered to be the most complete

structural exam available and remains a well-substantiated procedure despite its low risks,

is that it is considered the decisive procedure after abnormal screening results by any

alternative test.

1.2.2.4 Barium enema. A double-contrast barium enema involves the insertion of

liquid barium into the rectum, distending the colon with air, and taking an X-ray of the

colon and rectum. The test is repeatable every 5 to 10 years in patients where no adenomas

have been detected. There is generally no sedation, but pain and embarrassment are often

reported (Kim et al., 2001). In comparison to other screening tests, the barium enema is

reported as having decreased sensitivity in detecting polyps (Walsh & Terdiman, 2003).

Because FOBt and CS remain the most available and routinely advised screening

tests, only intention for these two screening tests will be examined in the present research.

It is important to study psychosocial factors underlying adherence to both types of test

because they remain the commonest procedures offered in organised screening programs. If

patients are not prepared to undergo structural exams such as colonoscopy following

abnormal faecal test results, or are not prepared to adhere to repeated stool testing on a

biennial basis, then mass screening programs will not be an effective means of cancer

prevention and detection (Levin et al., 2008).

1.2.2.5 Emerging screening tests. Current tests detect cancer or precancerous

adenomas, however some of the emerging screening tests detect early cell changes

associated with the presence of cancer (Levin et al., 2008), the genomic footprint for cancer

in those with familial CRC risk, or the detection of blood biomarkers (Quintero, Gimeno-

Garcia, & Salido, 2010). While two developments (colonography and stool-based

molecular testing) have potential for implementation in wider patient screening if large

prospective studies (currently underway) support their efficacy (Macrae, 2005; Walsh &

10

Terdiman, 2003), at present there are no plans for implementation of such tests in

population screening and they will therefore not be reviewed further.

1.2.3 Uptake of CRC screening

There is overwhelming empirical evidence that early detection of colorectal cancer

reduces morbidity and mortality. CRC is an excellent candidate for population screening,

and meets all nine of the World Health Organisation’s (WHO) criteria for national

screening implementation. As a slow-growing tumour, it can take many years for CRC to

raise clinical suspicion and discovery, emphasizing the necessity of high-sensitivity

screening tests and a comprehensive understanding of individual decision-making

processes and intentions to undergo or decline testing.

Colorectal cancer screening suffers from poor uptake and even poorer adherence

(Ananda et al., 2009) and therefore presents unique challenges in attempts to increase

population utilization. Studies of comparative screening uptake demonstrate a deep discord

between CRC screening levels in the population compared to other cancer screening

programs (such as mammography and cervical screening; AIHW, 2004; Ladabaum &

Phillips, 2006; BreastScreen Victoria Inc., 2009). However, it is difficult to directly

compare CRC screening with the higher rates seen in mammography and cervical

screening, as the latter have well-organised and supported national screening programmes

in place in Australia. It is unclear whether an on-going national bowel screening program

could transform the present levels of participation over time. There may also be distinctive

features of CRC screening which set it apart from better accepted mammography and

cervical screening programs, and which contribute to its uniquely low compliance rates.

International population screening efforts show similarly poor uptake in programs

of comparable design and stage of administration (many of which are pilot programs) (see

Table 1.1), such as in Denmark, where uptake was about 48% (Krongborg, Fenger, Olsen,

Jørgensen, & Søndergaard, 1996); in France with 42% compliance (Goulard, Boussac-

Zarebska, Ancelle-Park, & Bloch, 2008), Italy, with 46% compliance (Zorzi et al., 2009);

Japan, where participation rates are just 18% (International Cancer Screening Network

[ICSN], 2011); and the US where just 23.5% of eligible people engaged in FOB testing in

2001 (U.S. Cancer Statistics Working Group [CSWG], 2010). Interestingly, Finland has

11

exhibited some of the highest compliance rates for a piloted program, with around 71% of

the invited population accepting screening (Malila, Oivanen, & Hakama, 2008), and

reasons for this success (e.g., cultural, or related to program implementation) may merit

further investigation.

Randomised Controlled Trials of CRC screening are few, but have shown evidence

for the probable efficacy of national screening programs in reducing CRC-related morbidity

and mortality. Hardcastle and Chamberlain (1996) conducted a RCT of FOB testing in the

UK, with findings supporting the efficacy of national screening in the general population.

Their results revealed a 15% reduction in cumulative CRC mortality in the screening group,

however a notable finding was the low rate of FOBt uptake. Only 38.2% completed the

FOB tests offered during the program. Significant support for FOBt-based population

screening comes from a landmark RCT: The Minnesotta Trial, sampling 46,551 people, and

revealing a 33% mortality reduction over 18 years from annual FOBt screening (Mandel et

al., 1999). Biennial screening by FOBt reduced CRC mortality by 21%. RCT findings show

that large polyps and cancers are 4 to 5 times more likely to be detected by a combination

of FOBt and FS, than either test on its own (Rasmussen, Kronborg, Fenger, & Jørgensen,

1999), which would be expected given that positive stool tests are referred for further

investigation by structural exam, necessarily incorporating two tests in the diagnosis of

CRC.

12

Table 1.1

International Uptake of Colorectal Cancer Screening

Country Screening Program Modality Uptake

Australia National pilot program FOBt 42.9%a

Canada Regional programs FOBt, CS 30.1%b

Denmark National pilot program FOBt 48%c

England National program FOBt 58%; 52%d

Finland National program FOBt 71%e

France National program FOBt 42%f

Japan National program FOBt 18%g

USA Some organised programs FOBt, FS, BE, CS 18% (FS,CS);

24% (FOBt)h

Note. Source: based on Power, Miles, von Wagner, Robb, & Wardle, 2009. aAIHW, 2008. bSewitch, Fournier, Ciampi, & Dyachenko, 2008; and Telford, Levy, Sambrook, Zou, & Enns,

2010. cKronborg, Fenger, Olsen, Jørgensen, & Søndergaard, 1996; Frederiksen, Jørgensen, Brasso, Holten, &

Osler, 2010. dGutiérrez-Ibarluzea, Asua, & Latorre, 2008; Weller et al., 2007. eMalila et al., 2008. fGoulard et

al., 2008. gICSN, 2011. hCSWG, 2010.

The projected benefits of screening differ depending on the methods and

combination of testing. Lieberman (1995) evaluated the cost-effectiveness of five CRC

screening programs and determined that FOBt-based programs would require 80%

population compliance to be equivalent to the cost-effectiveness and decreased mortality

associated with one-time colonoscopy in 50% of the population, or annual FOBt combined

with FS at intervals in 60% of the population. Reaching these levels of compliance is a

significant issue. Current levels of participation in any one of these screening program

combinations is far below that projected to cost-effectively reduce mortality.

There has been a small amount of speculation as to the tendency for population

screening programs to increase anxiety and worry about cancer, however research suggests

that even those individuals exhibiting initial anxiety do not experience prolonged increases

13

in anxiety (Parker, Robinson, Scholefield, & Hardcastle, 2002; Robb, Miles, Campbell,

Evans, & Wardle, 2006; Wardle, Taylor, Sutton, & Atkin, 1999). Transient positive effects

have even been found as a result of having participated in colorectal cancer screening

(Wardle et al., 2003). In summary the evidence supporting national screening to detect and

prevent CRC in average-risk individuals is compelling (Levin et al., 2008) and improving

participation rates remains a research target for ensuring program success and feasibility.

1.3 Bowel Cancer Screening: Demographic and Health Factors

Demographic status, and personal and family health history predictably influence

decisions to participate in screening alongside more malleable cognitive-emotive variables.

This section will briefly review the role of ‘fixed’ factors such as health, family, and

demographic status in CRC screening intentions and participation. Fixed factors are

assessed in both Study 1 and Study 2, along with the psychosocial variables, to establish

sample characteristics, and to assess their contribution to the prediction of screening

intention and participation.

1.3.1 Health history and family history of CRC

Personal health and family history factors have emerged in a number of studies as

moderate predictors of intention to screen (or not to screen) for CRC (McQueen et al.,

2007). A history of gastrointestinal health problems, such as colorectal polyps or IBS, is

often predicted to positively correlate with intention to screen for bowel cancer. Friedman

et al. (2004) found that in a sample of 193 people, 10% had a personal history of polyps,

however, while this was not significantly associated with screening intention, it was

associated with screening participation. Conversely, in a qualitative study in a sample of

participants who had made the decision to forgo CRC screening, over a third believed that

the test was unnecessary because they felt healthy and had no history of bowel-related

illness (McCaffery et al., 2001), suggesting a history of gastrointestinal conditions may be

significant in the enactment of screening intentions, but a less deliberate influence during

the formation of intentions.

The most important risk factor for colorectal cancer is family history of the disease

(Rees, Martin, & Macrae, 2008), and individuals with two or more first-degree relatives

14

diagnosed with CRC have a three- to sixfold increased risk (St John et al., 1993). Family

history of CRC is almost always assessed in quantitative studies on bowel cancer screening.

In Friedman et al. (2004), both intentions and actual screening behaviour were evaluated

using measures of demographic and objective risk factors, and health beliefs. The second

highest correlate of screening intention (after self-efficacy) was family history of CRC,

which was reported by 12% of the sample. In accordance with these findings, family

history of CRC has also been reported as one of the most important determinants of

participation in CRC screening in an Australian sample (Weller, Owen, Hiller, Willson, &

Wilson, 1995).

While many studies measure correlates and predictors of intention to engage in

screening, few studies have directly investigated these variables in populations who have

declined screening. McCaffery and colleagues (2001) interviewed a group of 60 older

adults who had declined an offer of flexible sigmoidoscopy screening. Low perception of

risk, which was based on the absence of family history of CRC, arose as one of the most

important factors in decisions to forego screening.

History of engaging in a range of screening and preventive health behaviours has

also been associated with participation in bowel cancer screening tests, amongst other

health tests (Griffith, McGuire, Royak-Schaler, Plowden, & Steinberger, 2008; Guerrero-

Preston et al., 2008). Certain relationships have been more reliably identified; for example,

women who participate in mammography and cervical screening are more likely to have

also screened for CRC (Guerrero-Preston et al., 2008). In a sample of 4,490 male

employees in the automobile industry, 58% reported intentions to screen for CRC (Myers,

Vernon, Tilley, Lu, & Watts, 1998), and one of the strongest correlates of this intention was

past participation in screening. These patterns could be a sign of an underlying interest in

protecting one’s health, or a tendency to comply with requests from medical professionals

(Sutton et al., 2000). Prior participation in medical testing may also reduce anxieties and

demystify some of the processes involved in engaging in screening tests.

1.3.2 Demographic associations with CRC screening

Social contextual factors, such as those found in lower income populations, are

often thought to present barriers to screening for lower socioeconomic status (SES)

15

participants. An Australian study surveyed 664 urban Adelaide residents (aged 50 to 74

years) at different stages of CRC contemplation, intention and action. Against more

widespread findings the study reported, surprisingly, that lower levels of private health

insurance were associated with higher colonoscopy intention (Duncan et al., 2009). This

finding suggests that the relationship between insurance and colonoscopy screening may be

multifaceted, and potentially related to stage of contemplation. Generally, low levels of

health insurance have been associated with lower SES in Australia (Palangkaraya, Yong,

Webster, & Dawkins, 2009), and lower income (an indicator of low SES) is regularly

associated with suboptimal screening participation (Emmons et al., 2008; Friedman et al.,

2004; James et al., 2008; Phillips, Cohen, & Moses, 1999). In a US study, Denberg et al.

(2005) found that inadequate health insurance was predictive of not attending a referred

colonoscopy, where patients who were reliant on the US public health system and had more

limited income and greater morbidity, had the lowest completion rate. An assessment of the

initial impact of Australia’s NBCSP corroborates this pattern in an Australian sample, with

just 18% of patients diagnosed by the national pilot-screening program coming from the

most disadvantaged locations (Ananda et al., 2009).

Age has been associated with increased and also decreased screening participation,

and the literature remains incongruous. Duncan et al. (2009) found younger age to be

related to CRC intention, consistent with a recent Australian study where older age was

associated with declining screening participation (Janda et al., 2003). On the contrary,

younger age (in a sample ≥ 50 years old) was found to be predictive of less screening

participation in an American study (Denberg et al., 2005). Although there are equivocal

findings about the influence of age on screening (Clavarino et al., 2004), older age is more

generally associated with a greater intention to screen (Javanparast et al., 2010; Partin et al.,

2010). It is also possible that age shares a curvilinear relationship with screening, where

intentions begin to decline again in much older age groups (Ko, Kreuter, & Baldwin, 2005),

perhaps because perceptions of long-term benefits of screening may be outweighed by

more immediate short-term discomfort, risk, and costs of endoscopy (Lewis et al., 2010).

Marital status has a more consistent relationship with screening intention and

participation. Denberg et al. (2005) found marital status to be important for males, with

married men more likely to complete the colonoscopy than unmarried men, or than women

16

of any marital status. A large study based on invited British residents in the UK Flexible

Sigmoidoscopy Trial from 1996 to 1999 (van Jaarsveld, Miles, Edwards, & Wardle, 2006),

examined the influence of marital status on CRC screening intention and participation.

Intentions were assessed in 16,527 invited public, aged between 55-64 years (with 4,130

(25%) going on to participate in the trial). Findings supported the existing literature,

indicating that married or cohabiting people report higher intentions to screen and higher

screening attendance rates, than un-partnered people (Guessous et al., 2010; van Jaarsveld

et al., 2006).

1.3.3 Gender differences in CRC screening intentions

There is some ambiguity about the impact of gender differences on the uptake of

endoscopic bowel screening, with women appearing less likely to participate or adhere to

screening guidelines (Brennenstuhl, Fuller-Thomson, & Popova, 2010; Herold et al., 1997;

Meissner, Breen, Klabunde, & Vernon, 2006; Seeff et al., 2004). Meanwhile, many studies

on FOB testing present mixed participation rates for men and women (Koo & Leong,

2010). Lower CRC screening uptake among women may be historically connected to the

perception that CRC is a male disease (Donovan & Syngal, 1998; Friedemann-Sánchez,

Griffin, & Partin, 2007) and because of female preferences for a same sex

gastroenterologist (Menees, Inadomi, Korsnes, & Elta, 2005; Payne, 2007; Phillips &

Brooks, 1998; Rosenfeld & Duggan, 2008). Females’ reported preferences for a same-sex

gastroenterologist for endoscopy may be partly explicable by the nature of the physical

exam involved in endoscopic bowel screening and associated feelings of embarrassment.

Denberg et al. (2005) carried out interviews in a sample of people who had not

attended colonoscopy after referral. In line with previous findings, female gender was a

predictor of not attending a referred colonoscopy, and women more commonly reported

fear of pain, perforation, and immodesty than men. The lower rate of women participating

in CS is a multifaceted problem, part of which may be complicated by mistaken beliefs that

CRC is a lesser risk, and therefore less concerning for women, in addition to potentially

greater emotion-related avoidance, such as fear and embarrassment. Emotion barriers to

screening are discussed in more detail in Chapter 3. The following fixed factors are

assessed in the current thesis: age; gender; SES; partnership status; occupation; and health

17

status, in order to describe the sample fully in terms of their demographic and health

characteristics, and to assess whether the relationships in the literature between fixed

factors and screening intention hold for the present studies.

1.4 Intention, Decisional Conflict, and Health-Related Decisions

Intention is generally thought to be the most immediate antecedent to action (Ajzen,

2002; Morrison & Bennett, 2006), and is an indication of the end of the motivational or

pre-volitional phase of behaviour. A general definition of intention argues that it involves

the “plan, purpose, aim or belief that is oriented towards some goal” (Reber & Reber, 2001,

p. 362), while a definition derived from health psychology posits it is the effort people are

willing and planning to exert in order to perform a given health behaviour (Ajzen, 1991).

Empirically, intentions remain one of the strongest predictors of participation in screening

(Zheng, Saito, Takahashi, Ishibashi, & Kai, 2006). Difficulties in establishing whether the

optimal determinants of intention are largely identical to those that also precipitate

behaviour are well documented (Sheeran, 2002; Webb & Sheeran, 2006). One study

exploring factors associated with non-intention, intention, and actual attendance found that

most differences existed between the non-intention group versus the non-attenders (who

had intentions to screen) and attenders, suggesting bigger differences exist between

intentions about whether or not to engage in cancer screening, than differences between

those who have intentions but subsequently do or do not attend (Power et al., 2008).

It is argued that the formation of intentions to engage in health behaviour such as

cancer screening is often accompanied by decisional conflict (Dolan & Frisina, 2002).

Decisional conflict can be defined as uncertainty about health behaviour options when

those options involve risk, regret, or conflict with personal values (LeBlanc, Kenny,

O’Connor, & Légaré, 2009), and can be measured to evaluate the degree of uncertainty

about a planned course of action and the main amenable factors causing uncertainty.

Decisional conflict is thought to be particularly important in health-related decisions where

the outcome can be either desirable or undesirable (Redman, 2003). CRC screening fits this

profile, with a desirable outcome of peace of mind associated with a negative result, and an

undesirable outcome of discovering pre-cancerous or cancerous lesions and undergoing

further invasive procedures. Lower decisional conflict has been associated with better

18

acceptance of health care interventions (O’Connor, 1995), and has been linked with

increased knowledge of CRC and perceptions of higher quality screening decisions (Dolan

& Frisina, 2002). Higher decisional conflict on the other hand is considered to be an

unpleasant ambivalent state of mind (van Harreveld, Rutjens, Rotteveel, Nordgren, & van

der Plight, 2009). Decisional conflict may therefore provide insight into elements of the

decision-making process that contribute to decision delay, rejection, or acceptance.

Decisional conflict has been explored extensively in recent health behaviour

research, from euthanasia decision-making (Song & Sereika, 2006), prostate screening

treatment (Steginga, Occhipinti, Gardiner, Yaxley, & Heathcote, 2004), breast cancer

genetic testing (Green et al., 2004), diabetes treatment decisions (Weymiller, 2007), and

cross-cultural validation in Dutch (Koedoot et al., 2001) and French (Mancini, Santin,

Chabal, & Julian-Reynier, 2006) samples for palliative chemotherapy and breast cancer

gene testing, respectively. In relation to CRC screening intention, decisional conflict has

been examined in decisions comparing five different screening test options (Dolan &

Frisina, 2002), and in a number of recent studies on CRC intention in adults with low

education (Smith et al., 2010), elderly (Lewis, et al., 2010), and internet decision support

(Wilson et al., 2010). Further validation in complex health decision-making such as CRC

screening will add to the empirical base of decisional conflict and its ability to discriminate

between those with and without intent to screen for CRC.

1.5 Theoretical Models of Health Behaviour and Screening Intention

Health behaviour models assert that health beliefs and attitudes affect one’s health

intentions and performance (Becker, 1974). Cancer screening behaviour falls into a group

of health behaviours referred to as health protective (Morrison & Bennett, 2006) which

refers to behaviours enacted in order to promote or maintain health (Caltabiano, Byrne,

Martin, & Sarafino, 2002), and is the focus of a wide range of targeted and theory-based

intervention programs. Health intentions have typically been examined by value-

expectancy and motivation-based social-cognitive models, such as the Health Belief Model

(HBM), Protection Motivation Theory (PMT), Theory of Planned Behaviour (TPB),

Theory of Reasoned Action (TRA), and Social Cognitive Theory (SCT). These models are

born of cognitive theory, which states that the subjective value of an outcome and an

19

expectation about a behaviour leading to the desired outcome (value x expectancy), are the

driving forces behind behavioural motivation.

Major health behaviour models have a mixed record of predicting variance in

screening intentions and behaviours. Many applications of these theories in empirical

research have amalgamated the major factors from various approaches, rarely relying only

on constructs from a single theoretical model. While sometimes only one construct from a

model is employed within a study, there are instances where instruments have been

designed and based on preferred constructs from two or more health behaviour models.

This theoretical fusion makes it difficult to determine the unique value of each model.

Preceding a detailed investigation into the variables that will be explored in this thesis, a

brief review of these health models is appropriate.

1.5.1 Health Belief Model

Rosenstock (1966) developed the Health Belief Model (HBM) over 50 years ago in

response to low community participation rates in a number of disease screening and

vaccination programs. At the time, diseases like polio and tuberculosis were prominent and

the Public Health Service in the US was keen to increase public engagement in detection

and screening resources (Abraham & Sheeran, 2005). The origin of the HBM was to

understand and explain individual perception toward illness and the prevention of disease,

and Rosenstock argued this to be crucial to the intention and enactment of health behaviour

(1966). The HBM describes how a range of psychological factors and environmental cues

act together to predict preventive health behaviours.

The HBM encompasses five major variable groups including perception of risk;

perception of disease severity; perception of the benefits about engaging in the behaviour in

terms of self-efficacy and response-efficacy; perception of the barriers to engaging in the

behaviour; and finally, cues to action, referring to internal or external triggers, such as

physician prompting, media coverage, or personal experience with the disease.

It is particularly important to examine the adequacy of the HBM in relation to

screening, in view of the model being designed to explain preventive health behaviour in

the absence of disease. There has been mixed support for the capacity of the HBM in

predicting cancer screening and behaviour. Bish, Sutton, and Golombok (2002), when

20

looking only at the four constructs of the Health Belief Model (susceptibility, severity,

benefits, costs), found it predicted just 4% of cervical screening intentions in a sample of

142 British women, who were due to attend their routine cervical smear test at their general

practitioner, with the construct of severity having no relationship with intention. With

regard to behaviour prediction, the HBM was not found to have any significant predictive

power for subsequent routine cervical screening.

A review conducted by Tanner-Smith and Brown (2010) across 39 published

studies, assessed the utility of the HBM in the context of mammography and cervical

screening, finding that the perceived benefits and costs components of the HBM were the

most effective predictors of screening behaviour. In another review, Harrison, Mullen and

Green (1992) found weak predictive ability of the four dimensions of the HBM as

operationalised in 16 studies, with variance accounted for by the model ranging from .001

to .09. Some of these findings may be explained by the application of the HBM to cross-

sectional data, an approach that Rosenstock (1966) argued may produce inflated predictions

about the strength of HBM variables with an adopted behaviour. A recent meta-analysis of

18 studies (combined sample of 2,702) in which HBM variables were appropriately

employed to predict subsequent behaviour adoption (Carpenter, 2010), concurs that the

variables of the HBM have a weak effect on the prediction of longitudinal behaviour. In

particular, the meta-analysis revealed a small effect of severity beliefs (r = .15) and

susceptibility beliefs (r = .05) with the likelihood of adopting a behaviour, concluding that

these two variables should be avoided in direct predictions of health behaviour.

There are few published studies examining attitudes towards CRC screening within

the framework of the HBM alone. One such study by Janz, Wren, Schottenfeld, and Guire

(2003), conducted 355 telephone interviews in men and women aged 50 to 79 years in

Michigan, with fewer than 30% of the respondents reporting adherence to US CRC

screening guidelines. Analogous to the findings of Tanner-Smith and Brown (2010), this

suggested that the ‘barriers’ component of the HBM was most useful in predicting CRC

screening avoidance, including beliefs that the test is unnecessary and embarrassing.

As stated, support for the HBM is inconsistent, and some studies show moderately

good predictive power of the HBM. Wardle et al. (2000) explored the predictive power of

the Health Belief Model (HBM) as well as demographic and health variables in CRC

21

screening interest in 3,648 older adults in the UK. The authors measured interest in CRC

screening with respondents indicating whether they would probably, or definitely, take up a

screening invitation. Screening interest was predicted by a reported history of CRC in a

first degree relative, by bowel symptoms or a history of bowel disease, and by higher risk

perception and worry about bowel cancer. Subjective poor health status was linked to lower

interest in CRC screening, suggesting competing health problems may reduce interest in

screening. Participants reporting greater embarrassment, and who perceived the procedure

as ‘tempting fate’, and/or being time-consuming (which can be classed as emotional,

fatalistic, and practical barriers, respectively) were significantly less interested in screening.

Overall the HBM explained 47% of variance in screening interest. Three factors outside of

the HBM framework were found to explain additional variance, including general medical-

test attitudes, bowel symptoms, and socioeconomic status. The selective inclusion and

exclusion of particular HBM components prevents a full assessment of the power of the

HBM in predicting interest in screening in this study. Overall, available research applying a

HBM framework to investigate cancer screening uptake has found only modest support for

its total predictive value (Friedman, Webb, Richards, & Plon, 1999) however, there is

evidence for the importance of some key variables of the HBM in predicting cancer

screening behaviour, including perceived barriers, risk perception, and self-efficacy.

1.5.2 Protection Motivation Theory

Rogers’ (1975; 1983) Protection Motivation Theory (PMT) posits that threat and

coping appraisals contribute to the motivation to engage or not engage in a health protective

behaviour. Appraisals of threat are based on perceived risk and severity of the outcome

(e.g., having CRC), while coping appraisals stem from perceived self-efficacy to engage in

the behaviour, and response-efficacy of the behaviour. Provided that costs of engaging in

the behaviour are minimal, increased levels of self- and response-efficacy lead to increased

protection motivation. However, there is a paucity of studies that assess the PMT on its

own merit. Research conducted by Vadaparampil et al. (2004) applied variables from PMT

to identify the predictors of testing for prostate cancer in 82 men who were first degree

relatives of prostate cancer patients. Their findings, where 50% of the sample engaged in

testing, showed that PMT variables had only limited predictive validity for participation.

22

Against predictions, perceived vulnerability to prostate cancer and perceived severity of the

disease were not associated with testing, while the only important predictor from PMT in a

multivariate model was self-efficacy.

A review of over 65 studies pertaining to components of PMT, and reflecting

around 20 different health issues including both intentions and behaviours, found that the

model generated a moderate effect size of d = .52 (Floyd, Prentice-Dunn, & Rogers, 2000).

Its strengths include identifying increases in severity, vulnerability, response efficacy, and

self-efficacy in facilitating adaptive health intentions. The only published meta-analytic

review on the efficacy of PMT in health protective behaviours (Milne, Sheeran, & Orbell,

2000) found that the most useful component was the coping-appraisal aspect (as opposed to

the threat-appraisal feature), reflecting a belief that coping responses can effectively reduce

the threat (response-efficacy) and that the recommended behaviour can be effectively

performed (self-efficacy).

PMT has not been studied extensively in relation to cancer screening, however an

Australian study by Naito, O’Callaghan, and Morrissey (2009) compared the utility of PMT

with the Theory of Planned Behaviour in determining factors associated with

mammography intentions. A sample of 251 women aged between 37 and 69 were asked to

complete either a questionnaire followed by a control or PMT-based informational

intervention, resulting in greater prediction of mammography intentions from PMT

variables. However it is difficult to extrapolate these findings to the context of CRC

screening intentions. Protection Motivation Theory, as with other models based on rational

social-cognitive theory, may not be sufficient in understanding reluctant or avoidant CRC

screening intentions in terms of non-rational or emotively driven responses, and instead

may be best employed to assess the realisation of intentions into behaviour. However, it

does re-enforce the role of self-efficacy as an important recurring variable across health

models, and as a variable that may be more substantial in explaining intentions than social

cognitive models per se (Armitage & Conner, 2000). To date, there does not appear to be

an appraisal of the validity of PMT as a stand-alone model in predicting CRC screening

intentions or participation, however there again appear to be particular variables of strength

within the model for predicting preventive health behaviour, including response efficacy

and self efficacy.

23

1.5.3 Theory of Reasoned Action and Theory of Planned Behaviour

The Theory of Reasoned Action (TRA; Ajzen & Fishbein, 1980; Fishbein & Ajzen,

1975) and Theory of Planned Behaviour (TPB; Ajzen, 1985), argue that a sequence of

beliefs and attitudes about a behaviour, and social norms and intentions, lead to the

enactment of a particular behaviour. The TPB arose as part of a need to address criticisms

of the TRA (Armitage & Conner, 2000), and includes perceptions of behavioural control

(which is similar to the concept of self-efficacy), which influences both intentions and

behaviour. Notably, there exists an explicit assumption that motives are conscious and

reasoned.

One of the largest published meta-analyses of the effect of the TPB applied to health

behaviour across 87 studies (Godin & Kok, 1996) found that the TPB accounted for 41% of

variance in behavioural health intentions. Both attitudes toward the action, and perceptions

of behavioural control (resources or barriers to coping), were the most (equivalently)

important variables in explaining variance in intentions. Still, Conner and Armitage (1998)

propose that the TPB may be improved by extending it to include a moral norm variable

(feelings of obligation) and self-identity (relating behaviour to societal goals). Other

authors, having explored the TPB in the context of mammography intentions, suggest that

self-efficacy should be distinguished from perceived behavioural control, and included in

the TPB to improve its capacity to explain intentions and behaviour (Tolma, Reininger,

Evans, & Ureda, 2006).

The TPB has been used as a framework for measuring intentions to screen for

cervical cancer among university women (Duffett-Leger, Letourneau, & Croll, 2008).

While the subjective norms component of the TPB has been found in other research to be a

weak predictor of cervical screening intentions (Bish et al., 2000), the authors note it was

one of the strongest predictive components of the TPB in their study, in addition to

perceived behavioural control. There are few studies reporting CRC screening intentions

within a TPB framework. A recent study measuring the intentions of 2,426 men to undergo

either prostate or CRC screening was able to predict a considerable amount of variance

(49%) in CRC screening intention and uptake (Sieverding, Matterne, & Ciccarello, 2010).

While the TPB was successful in accounting for a large proportion of variance, descriptive

norms (‘normal’ behaviour in a given circumstance) explained variance in screening

24

intention beyond the conventional TPB variables, suggesting social norms are predictive of

screening intention (see Chapter 4 for a review of social variables).

1.5.4 Social Cognitive Theory

Bandura (1986) proposed that three central components (cognitive and affective,

behavioural, and environmental) comprise Social Cognitive Theory (SCT), and interact

with aspects of the target behaviour itself. These three components are all capable of being

impacted by four further elements: information, skills to self-regulate, self-efficacy, and

social support for the target behaviour. Provided there is perceived self-efficacy, perceived

control over the outcome, and minimal environmental or situational barriers, it is predicted

that the target behaviour is likely to occur. Of note is that a disproportionate amount of

variance in behaviours and intentions is usually attributable to self-efficacy, and therefore

this concept is often the prevailing focus of research within the SCT framework (Armitage

& Conner, 2000). STC itself has been argued to account for small to medium amounts of

variance in health behaviour (Resnicow et al., 1997).

1.5.5 “Major Theorists” Model

Sheeran and Abraham (1996) argue that the TPB provides superior predictive

ability for intentions and behaviour over HBM, SCT, and PMT, and this may be due in part

to its improved definition of constructs (Armitage & Conner, 2000). Armitage and Conner

suggest a more parsimonious account of intention and behaviour prediction could be

achieved by a combination of the motivational models (TPB, PMT, HBM, SCT).

Figure 1.3 depicts a model of health behaviour based on a union of eight variables

from the major models, and developed by leading theorists in the field (e.g., Bandura,

Becker (HBM), and Fishbein (TRA) (Conner & Norman, 2007, p. 19). The model reflects

an integration of some of the central constructs within models of health behaviour including

self-efficacy, risk perception, response-efficacy, and social norms, and was devised as a

result of the extensive overlap and similarity between many of the underlying constructs

(e.g., self-efficacy and perceived behavioural control) (Conner & Norman, 2007). The

middle variables reflect the three constructs that are deemed to be essential and sufficient

determinants of behaviour, while the variables on the left indicate the constructs that largely

25

influence intention, although some can influence behaviour directly. That is, there is

normative pressure to engage in the behaviour; the benefits (advantages) should be greater

than the costs (disadvantages); the behaviour is not discrepant with self-image; there is

greater positive emotion anticipated than negative emotion; and there is high self-efficacy.

Shortcomings of the model outlined by the authors include the possibility that

anticipated emotions might be better considered a subset of behavioural beliefs; that the

model fails to include perceived susceptibility and disease severity; lacks detail on the

relationships between its constructs; has not been empirically tested to date; and omits the

post-intentional (volitional) phase of health behaviour. Other possible limitations include

vague groupings (e.g., ‘Advantages and Disadvantages) within which there may be

variables that have unique influence and deserve separate analysis; and overlooking the

potential power of emotions experienced during decision-making. There is also a noticeable

absence of lower-order cognitive processes such as heuristics and their potential biases that

are inherent in many complex decisions. As the present thesis is an in-depth focus on

intentions, the lower branch is arguably the most significant, and many of the key variables

that fall within this subdivision will be tested empirically, with additional emotion and

heuristic variables (see Chapters 5-10).

Vic

Typewritten Text

26

Figure 1.3. Major theorists’ model of health behaviour (Conner & Norman, 2007, p. 19).

1.6 Do Health Behaviour Models Effectively Predict CRC Screening Intentions?

Part of the complexity of deciding to screen for CRC stems from the fact that

there is little way to accurately calculate one’s true risk of CRC, as its triggers are often

indeterminate. (Family history is associated with about 20% of CRC incidences [DHA,

2010], therefore most bowel cancers occur sporadically and without any hereditary

history.) The decision to screen therefore entails a number of negative features: the risk

of discovering cancer; undergoing what is commonly perceived to be an unpleasant test

(stool testing) or exam (colonoscopy); and small but possible physical risks associated

with structural exams. Additionally, positive results from initial stool testing can indicate

a number of benign conditions that necessitate further invasive and possibly redundant

screening by colonoscopy. Alternatively, the decision to decline screening entails the risk

of discovering an advanced and less treatable cancer in the future. Compounding this

dilemma is a range of beliefs and feelings associated with screening that can further deter

or promote participation. Watchful waiting is being advised less often in favour of active

27

screening, however deciding to participate in CRC screening continues to be a unique and

complex health decision.

Health behaviour models offer a way of explaining some of the variance in

participation in preventive health behaviour such as screening, but as discussed they have

been criticised as explaining only a small portion of variance in intention. In recent years

there has been greater investigation into complementary approaches to conventional social-

cognitive health models in explaining health behaviour and intentions.

Many qualitative studies have found that screening is sometimes rejected

immediately upon receiving the invitation to participate (DHA, 2004), suggesting that

many decision-makers are not using conventional, reasoned cost-benefit analyses proposed

by the major health behaviour and cognitive decision models. A wide range of variables

may have competing as well as positive influences in the decision process, and for this

reason it has been difficult to encapsulate such diverse variables in models of preventive

health behaviour. Some variables contribute to decisions to participate in screening and also

to decline screening. For example, in qualitative research older age has been related both to

participation and non-participation (Clavarino et al., 2004). As a motivation to screen, older

age was a good reason to look after oneself and get screened; yet for others it contributed to

fatalistic thinking (e.g., “I don’t have much longer to live anyway”) which along with other

types of ‘short-cut’ thinking have been neglected in the major models. There is wide scope

to explore factors that have yet to be thoroughly tested outside of predominant health

models, including cognitive short cuts and associated biases. While a number of important

concepts derived from the major health behaviour models will be examined in the present

investigation (self-efficacy, risk perception, test-efficacy (similar to response-efficacy),

cancer worry, knowledge, social norms, social support), a range of these cognitive biases

and shortcuts will also be explored in relation to the screening decision process. Chapter 2

presents a detailed discussion of current research on cognitions and CRC screening.

Models grounded in social-cognitive theory put forward mostly cognitive variables

as primary motivators in the engagement of health preventive behaviour, and have been

criticised for concentrating on rational processes while disregarding emotional and non-

rational influences. Emotions will therefore also be under investigation in the present thesis.

Where an emotion such as fear has been a component of a model, such as Leventhal’s Self-

28

Regulation Model, it has only been as a peripheral variable, or has been excluded from

empirical applications of the theoretical framework entirely. Emotions, while neglected in

traditional health behaviour models, feature in many descriptions of screening avoidance

(Chapple et al., 2008; de Nooijer, Lechner, & de Vries, 2001; Friedemann-Sánchez et al.,

2007; Wackerbarth, Peters, & Haist, 2005). Discussions about cancer can indubitably

summon a range of negative emotions. Cancer symbolises a diverse range of possible

disease manifestations and is one of the most dreaded illnesses (Robbins, 1962), leading to

a fear of disease as well as an anxiety about the effects of an unknown entity. Perhaps as a

result of negative emotions, studies have demonstrated that some people have a tendency to

reduce information seeking as their risk of cancer increases, or even after cancer diagnosis

(Degner & Sloan, 1992).

Another distinction of the decision process that has also been neglected is that

emotions can be source-dependent. That is, certain aspects of a screening invitation may

produce emotional responses exclusive to specific aspects of the decision. Fear, for

example, may be limited to either the procedure or screening test and the associated

potential for pain; limited to the possibility of experiencing embarrassment during the test;

or a fear of having cancer or thinking about one’s mortality. Cognitions have been

examined in this source-specific way already, and relate to one’s ability to effectively

complete the test, one’s judgement about the test’s accuracy, or one’s knowledge about the

disease. It seems appropriate to unpack emotions in this manner. This research examines

different sources of fear that may arise in screening decisions for CRC. Disgust will also be

investigated to determine types associated with and predictive of, screening reluctance or

avoidance. Finally, embarrassment associated with medical procedures (medical

embarrassment) will be reviewed and measured empirically to assess its role in accounting

for the variance in screening reluctance (detailed discussion in Chapter 3).

1.7 A Dual Process Theoretical Framework

Given that decision making requires judgement and cognitive processing of

available choices, the literature in information processing research should be directly

relevant in understanding factors that are significant in cancer screening decisions. Dual-

process theory is well established in the explanation of information processing and

29

judgement formation, and posits that information processing occurs via two distinct paths:

a peripheral, indirect route of processing; and a systematic, conscious, and deliberate route

of processing. This provides a theoretical framework for examining heuristic (cognitive

shortcut) bias in cancer screening intentions. The processes involved in peripheral

processing are associated with a greater propensity for error or bias due to their ‘shortcut’

nature, in that they bypass more deliberate, reasoned cognitive processing routes.

Prospect Theory (Kahneman & Tversky, 1979) is an attempt to model real-life

decision-making rather than simply optimal choice selection. It states that individuals are

risk-seeking when faced with potential losses, but when faced with the prospect of gain

they are risk-averse. Probability is a notoriously limited area of human reasoning (Evans &

Over, 1996), and Prospect Theory reflects real-life probability estimation where small

probabilities are often over-weighted, and medium to large probabilities are often under-

rated (Tversky & Kahneman, 1992). For low probability events, this often leads to risk

aversion for losses (and risk-seeking for gains), for example, taking out insurance to avoid

an unlikely loss (Kusev, van Schaik, Ayton, Dent, & Chater, 2009). Prospect Theory’s

primary relevance and application has been in behaviour economics, but may be useful in

explaining suboptimal health decisions. For example, it may help to explain the optimistic

bias persistent in people’s perceptions of their personal risk to a variety of hazards, by

demonstrating that people generally underestimate their risk to real dangers (real-life

probability), or the aversion to potential losses associated with screening when there is no

impetus (e.g., disease symptom) to screen, such as discovering disease, or experiencing

side-effects or complications.

Prospect theory can successfully apply to decisions involving ‘risky’ options; where

the outcome of the options is unknown or uncertain, but the probability of the available

alternatives is known. The decision to engage in bowel cancer screening encapsulates a

potential application of such an approach to uncertain decision-making, where the risky

option includes discomfort, possibly discovering unpleasant and life-changing news of

cancer, and rare complications of procedures, and the alternative involves missing out on an

early diagnosis of a treatable disease.

30

1.7.1 Evidence for dual information processing systems in decision-making

A range of dual-process theories of reasoning has evolved within a host of research

areas. For example, cognitive-experiential self-theory (CEST; Kirkpatrick & Epstein, 1992)

proposes two partially independent information processing systems: an experiential system,

which has a long evolutionary history and which automatically and rapidly processes

information; and a rational system which relies on logic and evidence. Studies of CEST

have supported this conclusion in a variety of contexts including juror decisions (e.g.,

Lieberman, 2002), gambling tasks (Denes-Raj & Epstein, 1994), responding to

unfavourable, arbitrary outcomes (Epstein, Lipson, Holstein, & Huh, 1992), and in

response to health promotion messages (Dunlop, Wakefield, & Kashima, 2010), showing

that the experiential system can explain a range of irrational choices (Kirkpatrick &

Epstein, 1992). CEST studies also provide evidence in support of two cognitive systems,

and additionally, that people can appreciate rational and commonsense options but be

intuitively drawn to a non-rational, but preferred, option.

Dual-process theory is extensively applied within different psychological

perspectives (e.g., cognitive, social, economic) and has been particularly central in attitude-

change research as part of the wider attempt to understand how people process and form

inferences from persuasive messages. This is particularly relevant as health campaigns are

usually designed to encourage (or persuade) individuals to participate in the recommended

screening, and this may necessitate some degree of attitude change on behalf of the target

group. The Elaboration Likelihood Model (ELM) incorporates the dual-processing system,

and predicts that individuals aspire to hold the most accurate beliefs and opinions because

they prove more functional (Petty & Cacioppo, 1984). The ELM supports peripheral and

central routes of information processing (a dual-processing system), and that messages

processed systematically will engender greater message-relevant thinking and subsequent

attitude change toward the target issue. Medical campaigns that are designed to persuade

the public can face controversy, as the concept of persuasion (as distinct from coercion or

manipulation) can be mistaken to imply that only positive persuasive information is

presented in a message, and potentially negative information (e.g., side effects) is omitted.

However, persuasive medical campaigns can ethically encourage and persuade individuals

31

to make informed decisions to screen, particularly as potential risks of participation

diminish.

1.7.2 Heuristic processes, biases, and health behaviour decisions

Dual process theory posits two modes of thinking and is sometimes referred to as

System 1 and System 2 (Stanovich & West, 2000), experiential and deliberative, lower

order and higher order, or as peripheral and systematic processing (Chaiken, 1980; 1987).

Whichever label is applied, they all reflect similar bundles of characteristics that represent

each mode of thinking. The first mode, referred to in the present research as the experiential

or lower-order mode, is implicit, automatic, effortless, spontaneous, associative, and rapid.

It is argued that this system is based primarily on intuitive thinking, and emotions and

feelings, which act as motivational guides to more elaborate cognition and action (Izard,

2007; Peters, Hess, Vastfjall, & Auman, 2007). Emotions assumed to be integral at the

experiential level are basic natural kinds, such as fear, anger, sadness, interest, happiness,

and disgust (Izard, 2007). By contrast, the second mode referred to in the present research

as the systematic or higher-order mode, is slower, effortful, analytical, conscious, and

reason-based. Arguably, the oft-researched cognitions from major health behaviour models

reflect the deliberative processing system. Both modes of processing present a range of

benefits and costs uniquely associated with each, and the evidence that there is proclivity to

rely on both types of information processing in complex decision-making is strong.

Experiential or heuristic processing plays an essential role in everyday behaviour

and interaction by enabling a significant amount of information to be processed rapidly and

efficiently. Systematic processing is generally reserved for more complex, or copious

amounts of information in decision making, but sometimes, because of limited processing

capacity, lack of relevant information, time-poor contexts, or novel decisions that

overwhelm the processing system, heuristics will operate to guide decision making. There

are a number of well-known heuristics that are sometimes employed to circumvent complex

and demanding cognitive effort (see Chapter 2), but a well-known trade-off for their

efficiency and speed is a potential increase in associated errors.

It is also not necessarily in one’s best interest to rely on controlled or deliberate

thinking (e.g., if being chased by a bull), and there is evidence to suggest that intuitive or

32

automatic judgements may sometimes lead to a more successful outcome (Peters, McCaul

et al., 2006; Wilson, Dunn, Kraft, & Douglas, 1989). Research suggests that by deliberating

our options too heavily we can dismiss potentially important emotional and intuitive

cognitive reactions that may provide essential information. This neglect may disconnect

attitudes from behaviour, and result in lower outcome satisfaction (Wilson et al., 1989), an

explanation that is sagacious provided automatic reactions do accurately reflect the context

and decision that achieves the best outcome, and that errors and biases are not introduced

into the decision process.

It may be that some decision cues are experientially and emotionally charged, and

that deliberate controlled evaluation of options is necessary to override poor decisions. For

example, presentation of the word ‘cancer’ in a health message might trigger experiential

thinking that interrupts cognitive elaboration, whereas more deliberate thought may lead to

a decision that cannot be reached with automatic judgement. Thus, while individuals may

achieve greater satisfaction using intuition for inconsequential decision making (i.e.,

choosing a colour when purchasing a car), higher-order systematic processing and

judgement remains integral in reaching successful and optimal complex decisions (see

Peters, McCaul et al., 2006).

Information processing routes are therefore key to which mode of cognition the

individual will be inclined to employ. These routes are increasingly being manipulated in

experimental decision studies to demonstrate the different processes involved in decision

formation (e.g., see Finucane, Alhakami, Slovic, & Johnson, 2000, for their manipulation

of risk and benefit perceptions). Information processing resources available to the

individual are therefore going to impact on complex consequential decision making,

including the decision to screen for colorectal cancer. Medical screening decisions are

complex for a variety of reasons and as a result, these types of decisions are likely to

benefit from a systematic mode of processing. Reduced or constrained processing resources

can lead one to rely more heavily on heuristic processes, possibly discouraging

participation in cancer screening. It is likely that both experiential and systematic processes

are operating in bowel cancer screening decisions.

As a framework for investigating decision making in CRC screening, this approach

may impart a context for understanding lower-order cognitive variables used in the

33

formation of screening intentions via their manifestation in biases related to screening tests

and CRC. It provides a conceptual foundation for identifying key cognitive and emotive

variables in decision-making which may not be reason-based; a foundation that has not

been systematically integrated into popular health behaviour approaches. Further research

on these heuristic processes in the emotionally charged area of CRC screening is therefore

likely to be fruitful for the development of interventions designed to improve screening

uptake. One of the aims of the present research is to examine experiential processes in CRC

screening decisions and behaviour.

1.8 Summary and Thesis Outline

1.8.1 Chapter summary

CRC screening enables both primary prevention and secondary prevention by

identifying pre-cancerous polyps as well as detecting malignancy, respectively. At present

there are few screening programs that offer the same level of protection against a particular

cancer, as many screening tests are only used for secondary prevention by detecting

abnormal cell growth (an exception is cervical screening, which also targets precancerous

changes). If left over a long period of time, polyps in the bowel can mutate into cancerous

adenomas, and screening provides the opportunity to remove benign growths, preventing

the possibility of future malignancy.

Screening for detection using the proposed method of immunochemical stool testing

has been a contentious topic. It is seen by some as problematic in that it has low specificity,

and can therefore sometimes ‘detect’ positive results where there is no underlying disease.

One aspect of the controversy is due to the expected increase in unnecessary follow-up

colonoscopy in those patients with false-positive faecal test results, however FOB testing

combined with follow-up colonoscopy is also the main benefit to bowel screening and this

combination of testing currently offers the best opportunity for cancer detection. A further

divisive issue in the employment of a national screening program is the argument that

screening for disease may increase anxiety and worry. Despite this criticism, qualitative

research shows that the majority of people, including those who experience anxiety, are

verbally supportive of screening initiatives and that feelings of anxiety are often transient

(Wardle et al., 2003). Notwithstanding criticisms, a national Australian CRC screening

34

program may be phased in following a moderately successful pilot program, in step with

international trends to conduct population screening for CRC.

CRC screening meets all nine of the WHOs criteria, which aim to determine the

suitability of population screening for a certain disease. However, public acceptance of

bowel screening needs to be addressed before a national program can attain success and

have the desired impact on population morbidity, mortality, and a reduction in burden on

the public health system. Continued understanding of the decision processes in CRC

screening remains an important research target for achieving more effective interventions.

Health behaviour models offer an incomplete picture in understanding health-related

decisions such as screening intention and action, and while particular features of these

models are integral, diverse approaches including information-processing and the input of

emotion offers a lateral approach in the investigation of screening behaviour.

1.8.2 Overview of the project

The general aims of the present investigation encompass many of the issues raised

in Sections 1.6 and 1.7. This thesis endeavours to expand on the limited understanding of

two areas thought to have the potential to influence screening intention and behaviour:

screening-related cognitive bias (hereon in referred to as ‘screening bias’); and three

discrete emotions: fear, embarrassment, and disgust. In particular, these variables

emphasise the experiential, or lower-order aspects of decision-making. Therefore, part of

this aim entails an empirical investigation of the instruments used to measure these

variables, and the relationships and predictive ability these variables have with the main

outcome variables: screening intention; decisional conflict; and screening participation. In

addition to this central aim, a second aim was to investigate a range of fixed factors, as well

as social-cognitive variables that have been widely researched and argued to influence

cancer screening intention and participation (see review in Section 1.3). These variables

encompass demographic and health variables, cognitive variables derived from leading

social-cognitive health behaviour models, and social factors.

Fixed factors have been reviewed in the present chapter. The remaining groups of

variables (cognitive, emotion, social) are reviewed in Chapters two to four.

35

The literature relating to variables that are commonly assessed in health behaviour

models, including risk perception of CRC, self-efficacy to engage in screening, beliefs

about the efficacy of tests, knowledge of CRC, cancer worry, and social factors (social

support to screen and norms surrounding screening) will be assessed to ascertain the ability

of these variables in predicting screening intention and behaviour. Chapter 2 reviews the

literature on cognitive factors (including screening bias) in decision-making, and in

particular in CRC screening decisions.

A review of the literature on emotion (affect and discrete emotions) and its

relationship to screening decisions and the judgement and decision-making literature is

presented in Chapter 3. There appear to be a range of emotional predictors of non-

participation, including fear of the screening procedure itself (Consedine, Magai,

Krivoshekova, Ryzewicz, & Neugut, 2004; Lewis & Jensen, 1996), fear of test results

(Codori, Petersen, Miglioretti, & Boyd, 2001; McCaffery et al., 2001), fear of experiencing

embarrassment during the procedure (Denberg et al., 2005), embarrassment associated with

medical experiences and settings (Codori et al., 2001; Consedine, Krivoshekova, & Harris,

2007), and feeling disgusted by the idea of bowel screening (Chapple et al., 2008).

When making complex health decisions, individuals consider information from a

variety of sources. They may recall personal experiences and memories, identify their own

beliefs and attitudes, and attempt to recognise how they feel about the choices available to

them. Attitudes may in part be derived from media, personal relationships, social networks,

and directly from health care providers, and can therefore have social origins. Social factors

are known to have an influence on CRC screening behaviour (Honda & Kagawa-Singer,

2005), and include norms about what family and friends do, what significant others will

think about their decision, and perceptions of support in a social network. Intentions are

also influenced by feelings of social support to engage in the screening behaviour (DHA,

2004a). These social factors (subjective norms, and personal and family social support) will

be reviewed in Chapter 4.

Chapters 5 to 11 present the results and discussion of two cross-sectional survey

studies examining emotions and social-cognitive relationships and cues to screening

decisions. The major dependent variables in these studies include intention as a marker of

decisions about future CRC screening, prior participation in CRC, and decisional conflict.

36

Chapters 5 to 7 report the pilot study, examining the psychometric properties of the

measures, and exploring the preliminary bivariate relationships amongst these variables.

The measures refined in this pilot study are then tested in a follow-up study described in

Chapters 8 to 10. Study 2 entails a community study with an older sample of Australians,

which explores the discriminating power of key psychosocial, demographic and health-

related variables on intentions to screen for CRC, decisional conflict about screening and

retrospective screening behaviour. Chapter 11 provides a general discussion of the findings,

as well as research limitations, future research possibilities, and recommendations for

practice.

37

CHAPTER 2

THE ROLE OF COGNITIVE FACTORS ON CRC INTENTION AND

PARTICIPATION

2.1 Chapter Overview

Subsequent to an overview of colorectal cancer and its epidemiology, and a

summary of the research problem in Chapter 1, it is appropriate to review the domain of

cognitive decision-making in health behaviour research and how it relates to CRC

screening. Over two sections, this chapter will present the empirical and theoretical

literature on cognitions in health behaviour research, with a focus on the available literature

on CRC screening intentions and behaviour.

The first section reviews the pertinent empirical literature on CRC screening

intentions and participation in relation to both familiar and widely investigated cognitions,

such as risk perception and self-efficacy. The second section expands on the cognitive

theoretical framework relevant to cancer screening intentions and health behaviour in light

of the emerging influence of biases and non-rational thought. The chapter will identify

deficits in the literature surrounding the role of cognitive bias in screening intention, and

argue the value of developing an empirical assessment of biases and non-rational features

of CRC screening reluctance. An outline of the cognitive variables under investigation in

the present research in presented in the Chapter Summary (Table 2.1).

2.2 Cognitive Paradigms and Empirical Approaches to CRC Screening

A vast amount of research has been conducted on the cognitive determinants

involved in intentions to screen for cancer, particularly those based on major health

behaviour models. Until recent years the focus has invariably been on those cognitions that

require reasoned, conscious deliberation and awareness, such as beliefs about the efficacy

of screening tests (test-efficacy), or judgements about one’s vulnerability to a specific

disease (risk perception). Despite receiving some of the most intensive consideration in

health behaviour research, a number of cognitions continue to present equivocal or

contradictory findings. For example, the most extensive published review on the role of risk

perception in cancer screening behaviour concluded that despite considerable research

38

attention, there is insufficient understanding about its effect on screening behaviour

(Vernon, 1999), although more recent research supports a positive relationship (e.g.,

Berkowitz, Hawkins, Peipins, White, & Nadel, 2008; Gimeno-Garcia, Quintero, Nicolas-

Perez, Parra-Blanco, & Jimenez-Sosa, 2009; Weinberg, Turner, Wang, Myers, & Miller,

2004). These cognitions, including risk perception, self-efficacy, test-efficacy, cancer

knowledge, and cancer worry, will be examined in the two studies described in this thesis,

and their place in the literature in relation to CRC screening is therefore reviewed in the

following sections.

2.2.1 Risk perception

Decisions about cancer screening are inherently linked with perception of risk to

that particular disease. As a central construct in most health behaviour theories, perceived

susceptibility is thought to be integral in the ultimate adoption of a specific health-

protective behaviour (Gerend, Aiken, West, & Erchull, 2004). Most studies reveal no true

physical difference between screening attendees and non-attendees in terms of their actual

risk of developing CRC (Sutton et al., 2000), suggesting that psychosocial factors such as

risk perception can play a larger role in the decision process to screen for cancer than

objective medical risk. Correspondingly, perceived risk of disease is only weakly

correlated with a person’s objective risk of disease (Weinstein & Klein, 1995), and

therefore health behaviour decisions may be motivated more by psychological factors such

as risk perception, than physical health status. Perceived risk is generally defined as an

individual’s judgement about their likelihood of developing a particular illness (Maiman &

Becker, 1974), or a belief about the likelihood of personal harm (Weinstein & Klein, 1995).

2.2.1.1 Risk perception as a predictor of CRC screening. Several studies have

found risk perception is one of the central psychosocial correlates (Palmer et al., 2007) and

predictors (McQueen, Vernon, Rothman, Norman, Myers, & Tilley, 2010) of CRC

intention and participation, with the association strengthening as perception of one’s risk

related to family history increases (Palmer et al., 2007). On the other hand, risk perception

has also been found to have no association with screening participation across a number of

cancers, including CRC screening (Brenes & Paskett, 2000; Helzlsouer, Ford, Hayward,

Midzenski, & Perry, 1994; Lipkus, Green, & Marcus, 2003; Lipkus & Klein, 2006; Macrae,

39

Hill, St John, Ambikapathy, & Garner, 1984). Risk perception will therefore be a variable

of interest in the present research, and will be reviewed for its relationship with, and

prediction of, screening intention and behaviour.

Risk perception was assessed in a study surveying 583 first-degree relatives (FDR)

of people with CRC (Codori et al., 2001). Risk perception was predicted to significantly

relate to higher screening rates, and this hypothesis was supported, where prior screening

participation was associated with perceived risk and also with the belief that CRC is a

preventable illness via screening detection. Low perceived risk might therefore act as a

barrier to screening participation. Denberg et al. (2005) explored this subject by conducting

a retrospective assessment of patients who were referred for colonoscopy but did not attend.

Information was collected via telephone interviews in a sample that had already made a

decision not to attend CRC screening (n = 52), who were asked their reasons for declining

participation. As expected, low levels of perceived risk emerged as a significant cognitive

barrier to participation.

Similarly qualified studies however, show no association between perceived risk

and screening intention or participation. A study by Friedman and colleagues (1999) in a

sample of 171 adults aged 40 and older revealed that risk perception and cancer worry

increased with the presence of a family history of bowel cancer, however risk perception

was not related to an increase in screening compliance, regardless of family history. In

another study showing no association between risk perception and screening, relatives of

CRC patients were interviewed to identify factors associated with their previous screening

behaviour (Madlensky, Esplen, Gallinger, McLaughlin, & Goel, 2003). Of the 368 FDRs,

236 (64%) had previously screened for CRC, but reported that perceived risk of CRC was

not amongst their reasons for having a colonoscopy. Instead, perceiving fewer barriers to

screening, having a family history of CRC, and discussion of CRC screening with social

groups were all associated with screening behaviour. Notably, a limitation of this study is

that participants were retrospectively examining their own behaviour, which may differ

considerably from the way an individual forms an intention for future screening.

Lipkus et al.’s (2003) longitudinal study demonstrated support that perceived

severity of CRC could be manipulated by an intervention and consequently increase

intentions to participate in a FOBt via increased risk perceptions, however perceived risk

40

did not meaningfully improve intention. There was however, a significantly higher increase

in FOB testing over 6 months in participants who received information on CRC severity.

This finding implies that the disease can be perceived as severe without necessarily

affecting personal perceptions of risk for CRC. It is unclear from their study however,

whether or not this subsequent increase in screening was related to an increase in risk

perception or fear of CRC in relation to the increased perception of disease severity.

One of the only reviews conducted on the effects of risk perception on cancer

screening behaviour was performed by Vernon (1999). While perceived risk was

commonly associated with mammography screening, data supporting its association with

CRC were inadequate and unclear, with six of the 11 studies indicating no association

between risk perception and either FOBT or sigmoidoscopy. While the manipulation of

perceived risk was found to be possible across cancers, the impact of risk perception

manipulations on subsequent CRC screening intention and participation was not clear due

largely to inconsistent research methods and outcomes. Vernon elucidated the difficulty in

determining whether perceived risk causes, or is an effect of, cancer screening behaviour or

intention, because of the majority of cross-sectional designs employed. In some studies,

risk perceptions were measured prior to screening, whilst in other studies they were

retrospectively measured. It is important to note that while over a decade of published

research exploring perceived risk of cancer has developed since Vernon’s major review

publication (1999), the function of perceived risk and the mechanisms by which it

influences cancer screening remains debatable.

2.2.1.2 Typologies of risk perception. It may be that different forms of risk

perception have differential effects on screening intention and participation. People

generally show an optimistic bias when judging their own health, believing they are at

lower risk of illness than others (Weinstein, 1987). Robb, Campbell, Evans, Miles, and

Wardle (2008) attempted to curb comparative optimism about CRC in a sample of 3,185

adults who reported both comparative optimism as well as high numeric estimates of

absolute personal risk of CRC. Participants were randomized into three groups: a control

group who received no information; a group who received a risk information leaflet; and a

group who received a risk and screening information leaflet. Although there was no

significant difference on comparative optimistic bias between the three groups as a result of

41

the intervention, there was a positive relationship between screening interest and

comparative risk perception (risk of CRC compared to peers of the same age, sex and race)

versus absolute risk perception (lifetime risk estimate of CRC).

In a sample of adults who were non-adherent to FOBt screening, similar findings

were reached by Lipkus and Klein (2006) who attempted to increase risk perception of

CRC in an effort to increase screening intentions, by informing a group of participants they

had more than the average number of risk factors (increasing their comparative risk) versus

control and absolute risk groups. Measuring both comparative and absolute risk perception,

the authors found that comparative risk perception increased screening intention, but

absolute risk perception had no impact.

Comparative risk has been found to have better predictive power in screening

participation than absolute objective risk (Blalock, DeVellis, Afifi, & Sandler, 1990), and is

also shown to be a persistent belief. Weinstein et al. (2004) found that participants

overestimated both their perceived absolute risk of developing colon cancer (chances per

1000 people) and their comparative risk (compared to their peers), and that feedback on

their actual lower physical personal risk did not significantly reduce risk perception.

Further research on comparative risk perceptions has shown that both patients and

physicians find comparative risk information easier to understand and communicate than

absolute objective risk (Klein & Stefanek, 2007). The present investigation examines both

types of risk perception as global statements of perceived risk with judgements based on

comparative risk assessments (lower or higher than average).

2.2.1.3 Measuring risk perception in the context of cancer screening. The

contentious role of perceived risk in cancer screening may be attributable to a range of

factors. CRC risk information presented in different ‘numerical language’ can induce

different risk perceptions. For example, risk perceptions are marred by a common deficit in

understanding probability (Peters, McCaul et al., 2006). Frequency information (e.g., 30

out of 100 people) appears to have greater impact than the equivalent information presented

in a percentage (e.g., 30%) (Slovic, Finucane, Peters, & MacGregor, 2004). As noted by

Weinstein et al. (2004) there is poor general understanding of the difference between

frequency and percentage, and greater ease in understanding the gist of verbal information

(e.g., “small risk”) than precise numbers (Burkell, 2004). However, providing people with

42

imprecise verbal statements of risk creates poor reliability because of subjective

interpretations of the statement.

Yamagishi (1997) presented differential mortality risk information to participants and

found that when lower risk was presented in relative frequency (1,286 out of 10,000) the

disease was rated as more dangerous than a higher level of risk presented as a probability

(24.14% of the population). Implications of these findings for presenting CRC risk

information suggests it may more effectively improve risk judgements to present mortality

rates as frequencies (e.g., 7 in 100 women develop CRC) than in probability (e.g., 7% of

women).

Measuring risk perception in relation to cancer screening introduces a number of

methodological issues. The presence of perceived risk in relation to cancer is linked to

disease treatability or severity, whereby action (or intended action) taken against the

disease should subsequently lessen the risk of developing that disease. Reducing perceived

risk is thought to be one of the main motivations to participate in screening, as put forth in

major theories of health behaviour including the HBM (Becker, 1974) and PMT (Rogers,

1983). However, the actual likelihood or probability of developing the disease is not always

diminished after screening, for example, one negative FOB test (indicating the current

absence of disease) does not imply low future CRC risk (Weinstein, 2003). Present

screening technology means that FOB testing needs to be repeated from the recommended

age, generally every one to two years. While perceptions of high risk may initially lead an

individual to screen for cancer, the receipt of negative results may not permanently

preclude them from future CRC risk. Therefore, participating in screening may only

temporarily reduce risk perceptions, and therefore its measurement may differ greatly

before, during and after screening intervals.

2.2.1.4 Section summary: risk perception. The function of risk perception in

intention formation is not clear, and it may be that it is more important in the development

of screening intention through comparative risk perception and numerical (frequency)

judgements of risk. As highlighted by Vernon (1999), difficulties arise with the inconsistent

way that risk perception has been measured. In cross-sectional study designs, Weinstein

(2003) suggests that perceived risk be measured before gathering information on screening

intention and behaviour, stating that the formulation of intentions early on in the study

43

design may inform later ratings of perceived risks and benefits. Cancer risk perception is

one of the most measured variables in cancer screening studies and is included in the

majority of health behaviour models (McQueen, Vernon, Meissner, & Rakowski, 2008),

and its impact on screening motivation requires ongoing empirical examination.

2.2.2 Self-Efficacy

According to social learning theory, self-efficacy is confidence in one’s ability to

perform a particular course of action (Bandura, Adams, & Beyer, 1977), and can thereby

increase the likelihood that a feared or difficult behaviour will be performed in order to

achieve a desired outcome (Feeley, Cooper, Foels, & Mahoney, 2009). In the context of

CRC screening, self-efficacy refers to an individual’s confidence in their ability to

successfully engage in, and complete the screening test. Self-efficacy has been consistently

and strongly associated with intention and participation in screening for bowel cancer in a

number of studies (DeVellis, Blalock, & Sandler, 1990; McQueen, Tiro, & Vernon, 2008;

McQueen et al., 2007; Seydel, Taal, & Wiegman, 1990), and is possibly one of the most

practical factors in exploring screening intentions because it is malleable (Jerant et al.,

2007; Litt, 1988).

The inclusion of self-efficacy in health behaviour models has often substantially

increased the predictive ability of the model. For example, the HBM and PMT were

examined by Seydel et al. (1990) in preventive cancer behaviour in 358 women, who found

that while risk appraisal (perceived severity and susceptibility) was not a sufficient

predictor on its own, self-efficacy significantly improved the models’ overall predictive

ability. Comparable findings are often reported in CRC screening specifically. Friedman et

al. (2004) measured self-efficacy as a predictor of CRC screening in 193 socioeconomically

disadvantaged patients and found that it was positively related to patients’ intention to

undertake a FOB test, although unrelated to screening behaviour. Other research, however,

has linked self-efficacy to CRC screening participation. Myers et al. (1994) explored a

number of psychosocial determinants of screening participation derived from the Health

Belief Model, the Theory of Reasoned Action and Social Learning Theory. Those with a

history of participating in FOBt reported greater self-efficacy, in addition to higher

44

intentions to participate in screening in the future, however it is difficult to determine

whether self-efficacy resulted from prior screening, or prompted screening.

Kremers, Mesters, Pladdet, van den Borne, and Stockbrugger (2000) compared

people who had participated in a CRC screening program (n=74) with those who had

declined to take part (n=57) on a range of psychosocial constructs including perceived risk,

response-efficacy, social support, and self-efficacy. While participants and non-participants

of screening differed on a number of variables including fear of CRC, response efficacy,

and social support, self-efficacy showed the strongest relationship with participation. This

implies screeners may have more confidence in their ability to successfully participate in

flexible sigmoidoscopy screening, and overcome or perceive fewer difficulties and

expectations of discomfort and nervousness.

2.2.2.1 Potential facilitators of screening self-efficacy. A study by Feeley and

colleagues (2009) raised some important issues in relation to self-efficacy and CRC

screening. Conducting focus groups with patients and with physicians in order to

understand the negotiation phase of CRC screening recommendation and referral, the

authors noted two main sources of efficacy that may increase both efficacy and

expectations of screening outcomes. The first was prior experience through past

participation, and physicians discussing their own prior screening experience with patients.

Secondly, vicarious experiences influenced efficacy both negatively and positively.

Negative vicarious reports from colleagues and friends increased expectations of

embarrassment and discomfort, while the presence of a family network mitigated

pessimistic vicarious reports and facilitated self-efficacy beliefs to engage in screening.

This suggests that efficacy to engage in screening may be improved by highlighting the

importance of spouses and children in one’s preventive health decisions, potentially

offsetting the impact of negative anecdotal reports.

One means by which self-efficacy may improve screening intention and

participation is through better self-reported coping in psychological preparation for an

endoscopy. Gattuso, Litt, and Fitzgerald (1992) assessed coping style in 48 men scheduled

for an endoscopy by randomly assigning them to one of four conditions: a control group; a

procedural information group; a relaxation only group; and a relaxation plus self-efficacy

group, who received efficacy feedback. The results supported the hypothesis that the

45

intervention incorporating a relaxation-training component plus self-efficacy enhancement,

would improve coping self-efficacy, and result in better tolerance of the endoscopy

procedure.

Most of the research investigating psychosocial influences on bowel screening

intention is cross-sectional and therefore there is limited data on the sequential role of these

factors as they manifest in intention, and in subsequent behaviour. One of the first studies

comparing cross-sectional correlates and prospective predictors of CRC screening was

conducted by McQueen et al. (2007) as part of a two-year intervention trial promoting CRC

and dietary awareness in automotive workers. There were several stable factors associated

with screening in both cross-sectional and prospective analyses, and among these was self-

efficacy, which was associated with previous CRC screening and also with the maintenance

of screening during the one-year trial. The authors propose that the influences on screening

(which in addition to self-efficacy, included family influence, intention, age, family history

and personal polyp history) are therefore potentially consistent and stable targets for

intervention. Self-efficacy is also a particularly useful cognition to assess in cancer

screening intentions because, in addition to being a strong proximal predictor of screening,

it is situation-specific (Bandura, 1982), involving objectives or aims toward a specific

desirable outcome.

2.2.3 Test-Efficacy

Test-efficacy reflects a person’s belief that the screening test will effectively detect

disease with certainty and accuracy (Miller, Luce, Kahn, & Conant, 2009). All screening

tests suffer flaws, be it in their ability to correctly identify a malignancy, or in the chance

that they can cause physical side effects in the patient. Psychological side effects are also

possible. False-positive rates, where a healthy person is incorrectly given a result that

indicates the presence of cancer, occur most commonly in FOB testing, putting the patient

at risk of psychological distress.

The majority of people who screen positive in their FOBt sample will not have

CRC, but will be required to attend colonoscopy to confirm or disconfirm cancer.

Colonoscopy poses a risk of bowel perforation, exposing the person to additional potential

harm as a result of screening, however this risk is minimal. In one study of older

46

participants (aged 70 to 75), major complications (stroke, serious bleeding, perforation,

heart attack) affected 0.3% (or 3 in 1000 colonoscopies performed). One US randomized

trial advised that for every 217 people screened by FOBT annually, 1 death would be

prevented (Mandel et al., 1993; Mandel et al., 1999). The benefit-to-harm ratio of CRC

screening is nevertheless thought to make population testing a worthwhile objective at a

population and an individual level.

Notwithstanding the true efficacy of screening tests, perception of test efficacy is

regularly cited as a significant influence on CRC screening decisions, and in particular

decisions to engage in endoscopic screening (Dent et al., 1983; Janz et al., 2007; Kremers,

et al., 2000). However, test-efficacy beliefs are also considered important in stool testing

participation. In a qualitative study of 488 patients aged between 50 and 74 years, Janz and

colleagues (2007) found that patients cited test-efficacy as one of the most important

factors in screening by FOBt, regardless of their screening history as users, attempters of

screening, or non-users. This finding replicates that of Rawl, Menon, Champion, Foster,

and Sugg-Skinner (2000), who conducted focus groups to gather insight into the screening

beliefs of 22 first-degree relatives (FDR) (with an average age of 44 years) of colorectal

cancer patients. Beliefs about the reliability and efficacy of the tests were the second

biggest concerns (after a lack of awareness of bowel cancer) for participants. The

specificity of the test, that is, its ability to detect only those who have bowel cancer, was

cited as the most important test quality.

Quantitative research has also highlighted the importance of measuring and

understanding the magnitude of test-efficacy beliefs in bowel screening decisions. In a

sample of 168 participants with an average age of 62 years, Shokar, Carlson, and Weller

(2010) found that both the accuracy and scientific evidence for the efficacy of the test were

the most important preferences among patients when they considered screening for bowel

cancer. With a focus on examining patient preferences about specific attributes of the tests,

rather than general patient beliefs, the researchers were able to assess varying opinion about

the attributes of four different screening tests (FOBt, FS, CS, and double-contrast barium

enema). Notably, the authors found that initially the preferred mode of testing for patients

was FOB testing (59%), however, once exposed to the specific attributes of each test, the

majority preference shifted to colonoscopy (54%). This finding is important because

47

previous research has shown that patients strongly concerned about privacy and discomfort

have a preference for home tests such as the FOBt (Greisinger, Hawley, Bettencourt, Perz,

& Vernon, 2006; Janz et al., 2007), while the colonoscopy is sometimes less favoured.

According to Shokar and colleagues, improving patient understanding of the attributes of

each screening test may contribute to better patient compliance and participation in

endoscopic procedures such as colonoscopy, which is often considered the gold standard of

bowel cancer testing (Hewett, Kahi, & Rex, 2010).

In summary, the possibility of erroneous results in testing for CRC is not outside an

acceptable margin of error for cancer screening tests, with the immunochemical tests most

often recommended in Australian screening having a relatively accurate and efficient

detection record (van Dam, Kuipers, & van Leerdam, 2010), while endoscopic procedures

like colonoscopy present only rare physical risk (Valori, Nicolaas, & de Jonge, 2010).

Given the importance of test efficacy in the decision-process of people considering CRC

screening, information about the accuracy, merits and value of both stool testing and

colonoscopy in interventions aiming to improve screening compliance may be appropriate

goals.

2.2.4 Cancer Knowledge

Numerous studies across diverse populations generally report that public knowledge

of CRC is poor (Akhtar et al., 2008; Geiger et al., 2008; Harewood et al., 2009), however

the influence of cancer knowledge on cancer screening intentions is equivocal. Public

cancer education has received substantial funding in Australia over the last two decades,

with one of the main objectives to increase awareness and knowledge (Paul et al., 2003). As

a result, skin cancer prevention, smoking cessation, and mammography screening

campaigns in Australia have witnessed large improvements in population knowledge and

awareness (Beckett, Redman, & Lee, 1990; Diamond, Fitzgerald, & Moore, 1990; Hill &

Hassard, 1999; Lower, Girgis, & Sanson-Fisher, 1998), however there are still

misperceptions about the importance of early detection in CRC morbidity and mortality

(Paul et al., 2003). Some researchers propose that a deficit of knowledge about CRC and its

various screening tests diminish one’s perception of risk to bowel cancer, is associated with

negative perceptions about screening, and also directly predicts less screening participation

48

(Brenes & Paskett, 2000; Kelly, Dickinson, DeGraffinreid, Tatum, & Paskett, 2007;

McCaffery, Wardle, & Waller, 2003; Vernon, Myers, Tilley, & Li, 2001; Wee, McCarthy,

& Phillips, 2005; Wolf et al., 2005).

Whilst educational campaigns have frequently targeted disease knowledge in an

effort to increase awareness and understanding about cancer, there appears to be mixed

evidence for a direct influence of knowledge on screening decisions (Vahabi & Gastaldo,

2003). Some researchers concur that limited knowledge of bowel cancer is a barrier to

screening, possibly by weakening the positive effects of certain psychosocial variables that

have been directly associated with screening intention, such as test-efficacy. Supporting

this line of argument are the findings that knowledge has been associated with outcomes

other than screening intention, for example, a more optimistic attitude about cancer

prevention, or education opportunities and higher SES, but not an increase in screening per

se (Brown, Potosky, Thompson, & Kessler, 1990).

2.2.4.1 Knowledge and socioeconomic status. Cancer knowledge has been

associated with higher socioeconomic status (SES) as reflected by income and education

levels (Geiger et al., 2008). Racial minority, low education levels, and limited access to

healthcare, create vulnerable groups who experience greater barriers to CRC screening

participation (Brouse et al., 2003; Geiger et al., 2008; Wee et al., 2005), and greater

incidence and mortality (Shokar, Vernon, & Weller, 2005). Interviews conducted with both

white American, Hispanic and African American participants (Shokar et al., 2005) eliciting

a range of screening beliefs, indicated that knowledge of CRC and screening was

particularly low in minority groups, within which there was little understanding of the

concept and value of screening, difficulty listing CRC screening tests, and difficulty in

understanding procedure names and simplified medical terms. Although effective in the

general population (Wardle et al., 2003), large public education campaigns may be less

effective in improving knowledge in marginalised groups and populations with limited

educational background. Many leaflets and campaigns require a reading level of grade 11

or above (according to English health communication materials; Hart, Barone, & Mayberry,

1997).

A recent intervention study examined the effects of a culturally relevant program

designed to increase knowledge and participation in FOB testing in low-income African

49

American women with limited education history (Powe, Ntekop, & Barron, 2004). Given

that the researchers wanted to increase and also maintain knowledge of CRC in their

sample over time, they collected data at baseline, 6 month, and 12 month intervals, finding

the intervention effective in improving knowledge levels within this sample. Those

participants with greater knowledge were also more likely to participate in FOBt after 12

months. These findings suggest that interventions targeted toward marginalised groups may

be effective, and that increases in CRC knowledge may be an essential predictor of

enhancing screening participation in this population. Unfortunately there are few published

studies on CRC knowledge and screening participation in Australian indigenous

communities, however a recent study suggests they experience similar disadvantages and

low levels of knowledge to engaging in CRC screening (Christou, Katzenellenbogen, &

Thompson, 2010) as cultural minority groups in other Western nations, such as the United

States.

A study by Geiger and colleagues (2008), drawing from a large dataset of 6,369

participants in a national trends survey in the United States (HINTS I), found that the best

correlates of CRC knowledge and colonoscopic screening were female gender and

education. Income level correlated positively with knowledge but showed less association

with participation in screening. The authors argue that low levels of knowledge are related

to the low use of CRC screening in the general population. It is interesting that the sample

also reported high optimistic bias, believing themselves to be at lower risk of CRC than the

general population. Thus, while knowledge may have a direct influence on screening

participation, there may also exist erroneous belief systems and cognitive biases (such as an

optimistic bias) inherent in personal risk assessments, irrespective of knowledge levels.

Using semi-structured focus-group interviews (n=39) with two age groups of

participants (aged 50 to 64 years; and 65 years or older), Weitzman, Zapka, Estabrook, and

Goins (2001) found low levels of knowledge about the prevalence and perceived risk of

CRC, and a range of misperceptions about screening were common. Some of these

misperceptions could be interpreted to underlie non-rational beliefs separate to explicit

knowledge about bowel cancer. For example, there were common beliefs that bowel cancer

is not a ‘female thing’ and thus is not a serious concern for women. While misperceptions

were related to lower levels of knowledge about CRC, they may also be a reflection of

50

heuristics related to judgements about the value of screening (discussed later in this chapter,

Section 2.3).

Some studies have revealed no association between levels of CRC knowledge with

risk perception or screening intention, and no difference in knowledge between groups of

people who screen versus those who decline (Gili, Roca, Ferrer, Obrador, & Cabeza, 2006;

Jerant et al., 2007). Weinberg and colleagues (2009) anticipated low levels of knowledge

and risk perception amongst non-compliant average-risk women in a cross-sectional survey

study. Against their predictions, the 318 female participants (aged 50 to 65 years) reported

no intention of screening for CRC in the future, yet they also demonstrated very high levels

of CRC knowledge. The authors were unable to find any coherent relationship between risk

perception, screening intention, and CRC knowledge. These findings suggest that the

emphasis placed on disseminating CRC knowledge as an integral part of public educational

interventions, while probably useful, may be insufficient at directly improving either risk

estimations or ultimately, CRC screening decisions. It may be that a certain level of CRC

knowledge is a hurdle requirement for intention formation, but beyond a fundamental level

becomes less important in subsequent decision making. Instead of targeting improvements

in knowledge, the success of an intervention may also benefit from the direct targeting of

common misperceptions and biases about screening.

2.2.5 Worry

Worry in its non-clinical definition is an everyday cognitive process that involves

intrusive thoughts and images (Fresco, Frankel, Mennin, Turk, & Heimberg, 2002; Dupuy,

Beaudoin, Rhéaume, Ladouceur, & Dugas, 2001), and is thought to be a non-pathological

transient form of distress (Hay, Buckley, & Ostroff, 2005). While some researchers loosely

define cancer-specific worry as an emotional reaction to the threat of cancer (Bowen et al.,

2003; Hay et al., 2005; McCaul, Schroeder, & Reid, 1996), a widely accepted definition is

that worry is a cognitive process which can lead to subsequent experiences of negative

emotions such as fear and anxiety (Borkovec, Robinson, Pruzinsky, & DePree, 1983).

Cancer worry is also a construct distinct from perceived risk and general distress (McCaul

& Tulloch, 1999), which have both been only moderately related to worry (Cameron &

Diefenbach, 2001).

51

Cancer worry, anxiety, embarrassment, and fear have regularly been discussed

interchangeably in the research literature, and often operationalised as though a single

construct. In a recent review (Hay et al., 2005) cancer worry represented all of these

constructs with no conceptual division, making it difficult to interpret the major findings of

the review. As a whole, it was suggested that non-specific cancer worries appear to be

consistently low, while worry about CRC specifically is also low in the general population

(Hay et al., 2005; Wardle et al., 2000; Watts, Vernon, Myers, & Tilley, 2003). Despite only

a small minority of high worriers in the population, further research as to the function of

cancer worry in CRC screening is warranted.

Worry specifically linked to cancer has been identified since the 1940s, and was

initially referred to as ‘cancerphobia’ (Hay et al., 2005), a pathological state of worry

associated specifically with developing cancer. Two decades later, research had led to more

normative views of disease worry, which has greater likelihood of being present in a large

minority of the population than would a ‘pathological’ case of cancer worry (Kirscht,

Haefner, Kegeles, & Rosenstock, 1966). One area of behavioural cancer research that has

been under intense focus in the health literature is that of mammography screening

intention and attendance. Some authors have suggested that the relationship between worry

and decisions to undergo mammography is curvilinear (low and high worriers are less

likely to screen compared to those with moderate levels of worry) (Andersen, Smith,

Meischke, Bowmen, & Urban, 2003; Lerman et al., 1991; Sutton, Bickler, Sancho-

Aldridge, & Saidi, 1994), however, higher levels of cancer worry have also been associated

with increased mammography attendance (Lagerlund, Hedin, Sparén, Thurfjell, & Lambe,

2000; McCaul, Branstetter, O’Donnell, Jacobson, & Quinlan, 1998) and increased CRC

screening and intention (Collins, Halliday, Warren, & Williamson, 2000; McCaul &

Mullens, 2003; Moser, McCaul, Peters, Nelson, & Marcus, 2007). However, to further

muddy the findings, worry has also been associated with less CRC screening (Mack et al.,

2009; Watts et al., 2003), and to have no effect on screening at all (Brenes & Paskett, 2000;

Friedman et al., 1999). Therefore, the independent effect of cancer worry on screening

behaviour and intention is in need of further clarification.

2.2.5.1 Measurement of worry. Perhaps partly because of the vague conceptual

history of worry in health behaviour research, there are few reliable, standardized

52

instruments to measure cancer worry. A common approach to the assessment of cancer

worry is to construct items for the purposes of a particular study, often leading to the

measurement of different but arguably similar constructs such as distress, or fear of cancer,

that may arise from intrusive or worrying thoughts. For example, items used to assess

‘worry’ have included, “I am afraid of having abnormal colorectal cancer screening test

results” (see Tiro, Vernon, Hyslop, & Myers, 2005) and “Thinking about breast cancer

makes me feel upset and frightened” (see McCaul, Reid, Rathge, & Martinson, 1996).

Lerman and peers (1991) developed the Lerman Breast Cancer Worry Scale, which

has been one of the most consistently used measures of cancer worry, and regularly adapted

in studies measuring worry about other cancers, such as prostate cancer (e.g., Cohen et al.,

2003). However, a criticism of the scale is its measurement of both the frequency and

impact of worry, factors that may be unique (McCaul & Goetz, 2010). A further

measurement problem has been the interchangeable use of worry and anxiety scales as

identical constructs. The lack of a distinction between the cognitive construct of worry and

the emotive construct of anxiety has limited the interpretation of research findings.

One mechanism by which cancer worry may drive to increase screening intentions

is its association with greater risk perception of CRC, encouraging the individual to engage

in an action that may reduce both perceived vulnerability as well as its related cancer-

specific worry. Both cancer worry and cancer risk perceptions have regularly been found to

share a positive relationship (Lipkus, Klein, Skinner, & Rimer, 2005), and importantly,

positive but independent associations with intentions to screen for bowel cancer (Wardle et

al., 2000).

Rawl et al. (2000) found that participants viewed one of the benefits of screening as

the reduction of worry, suggesting this may be a mechanism by which it facilitates

screening behaviours. Although different relationships between CRC screening intentions

and worry have been reported, cancer worry has been found to be more predictive of

intention to screen for cancer than risk perception (Peters, Slovic, Hibbard, & Tusler,

2006), and overall the literature suggests that non-pathological cancer worry is a motivating

psychosocial factor for intentions to screen for CRC (McCaul & Mullins, 2003; McCaul et

al., 1996).

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2.3 Dual Processing and Cognitive Bias: Recap of the Theoretical Framework

The decision-making literature is a valuable resource in the interpretation and

understanding of health behaviour and intention. Decision-making research has origins in

economic theory, and many areas within psychology have subsequently drawn from this

research. Concepts such as “bounded rationality” (that an individual has finite cognitive

resources with which to make a decision; Simon, 1955) add to the explicability of health

behaviours (along with social, cultural, motivational, and emotional aspects), particularly as

the constraints of a rational approach are being realised (Kahneman, 2003).

Tversky and Kahneman (1982; 1983; 1992) expanded on the concept of bounded

rationality to explain suboptimal decision-making in behavioural economic theory. They

have subsequently emphasized the dual cognitive processing systems defined in Section

1.7, Chapter 1, where System 1 generates impressions that are implicit and non-voluntary,

and System 2 generates judgements that are voluntary and explicit, whether or not they are

overtly expressed (Kahneman, 2003). Judgements are always generated by System 2, but

may have originated from System 1 ‘intuitive’ impressions (Kahneman, 2003). This ‘built-

in’ imprecision from the automatic, instinctive, and rapid processes of System 1 increases

the possibility for error and distortion in judgement and decision-making.

The dual-processing framework is therefore well suited to an examination of

cognitive processes in CRC screening intentions where patients face a complex preventive

health decision. These decisions may evoke careful systematic deliberation in some, and

rapid, immediate, and less contemplated responses in others, and therefore both of these

cognitive styles need to be investigated to better understand cancer screening reluctance.

2.3.1 Heuristic biases: definitions. In their seminal research in behavioural

economics (1974), Tversky and Kahneman identified three main heuristic biases affecting

decision-making: anchoring, whereby one feature of the decision becomes the focus to the

detriment of other available information; availability, where easily available information

(for example by vivid imagery, or recent occurrence or exposure) will receive more weight

than other data; and representativeness, where people draw conclusions based on

stereotypes instead of true probability. It is the latter heuristic that will be the focus of the

present investigation into screening bias because it appears to arise most often in research

54

illustrating public misperceptions of bowel cancer based on stereotypes (e.g., Chapple et

al., 2008; Denberg et al., 2005; Donovan & Syngal, 1998; Friedemann-Sánchez et al. 2007;

Wackerbarth et al., 2005). As they share precursors and other overlapping features, a brief

overview of all three heuristics follows. The present investigation explores whether

cognitive biases, in the context of health decisions, can predict CRC intenders and

screeners in addition to, or over and above, deliberative and systematic cognitive factors

traditionally employed in the prediction of health behaviour.

2.4 Heuristic Biases and their Role in Behavioural Cancer Research

Difficult decision-making caused by copious information or complex cognitive

processing may be resolved through the use of cognitive shortcuts in an effort to ease

overtaxed information processing capacities. In principle, these shortcuts or heuristics

enable one to condense complicated analytical reasoning into simpler, learned cognitive

shortcuts in order to reach a judgement or decision (Peters, McCaul et al., 2006). Heuristics

therefore provide an efficient way to make rapid decisions on a daily basis however, the

trade-off for this efficiency is the possibility of bias and judgement error.

Cancer screening presents choices to the individual that may be complex, uncertain,

and novel, and may involve choosing amongst more than two alternatives. These decisions

are characterised by being both consequential to the patient, as well as requiring them to

absorb a large nexus of medical language and information. Thus, information amount and

type of presentation format has the potential to be optimal or to overwhelm decision-

making cues. Hibbard and Peters (2003) propose that one cognitive shortcut involves an

emphasis on a single variable associated with the decision, while underrating others. The

variable most likely to be given prominence will be well-understood by the patient (for

example, scheduling or time barriers) and will receive more weight in the decision-making

process than more vague, novel, or complex factors such as the efficacy of screening tests.

Screening decisions present numerous features, and decision makers in novel and

unfamiliar settings may not always determine which factors are most important to them

(Hibbard & Peters, 2003).

Public misconceptions about CRC screening have arisen in many qualitative studies

(Zheng et al., 2006). CRC misconceptions are often explained by authors as either isolated,

Vic

Typewritten Text

55

unsystematic variables (e.g., see Busch, 2003; Chapple et al., 2008), or are explained as

part of a general set of barriers to screening (e.g., Denberg et al., 2005; Worthley et al.,

2006). Such screening misconceptions may constitute biases that are specific to screening

when they represent consistent cognitive errors related to CRC screening, such as being

founded on stereotyped beliefs or judgements, born of recent exposure to a related event, or

based on one dominating feature of the decision. In this fashion, they can be observed as a

set of variables that can be studied systematically as ‘screening biases’.

McCaffery and colleagues (2001) conducted interviews with 60 people aged

between 55 and 64 years about their reasons for declining flexible sigmoidoscopy

screening. Participant comments indicated bias relating to both the mental accessibility and

perceived salience of bowel cancer (known as ‘availability’, to be discussed later in this

chapter), and stereotyped views of bowel cancer sufferers (the ‘it can’t happen to me’

belief, known as ‘representativeness’ also discussed later in this chapter); views that

participants felt did not apply to themselves. Themes used to justify such beliefs included

the absence of symptoms and the absence of a family history of bowel cancer. In those

participants who could recall family and friend experiences, and thus had greater

availability of bowel cancer, fatalistic thinking was sometimes employed to dismiss

screening (e.g., “if you’ve got it, you don’t get cured”, so why bother screening?), and

beliefs that the “cause” of someone else’s cancer does not apply to themselves (e.g., “he

smokes and I don’t”), a possible reflection of representativeness bias. Also reported were

beliefs that avoidance of screening or thinking about illness was in some way protective in

itself (“I tend to think positive”; “…I enjoy life rather than worry about everything”). Such

research illustrates that while low risk perception, knowledge or self-efficacy can mitigate

screening intention, there are several non-rational beliefs that may have a similar effect.

In summary, while heuristics provide rapid approximations by reducing unnecessary

cognitive effort and elaboration in the formation of judgements, this cognitive advantage is

not without cost, and is often accompanied by a number of systematic errors. One of the

aims of this research is to address how biases that arise from cognitive shortcuts can

influence screening intentions; possibly by the erroneous ways people underestimate their

risk of disease. It is therefore appropriate to consider in more detail some of the main

56

heuristics and their associated biases that may be introduced during the decision process of

complex health choices.

2.4.1 Availability errors in health behaviour decisions

One of the most frequently employed heuristics is the availability heuristic, which

refers to a judgement about the probability of an event, based on the ease with which it

comes to mind (Tversky & Kahneman, 1973). Cancer is not only a relatively common

illness in developed nations, and is responsible for approximately a quarter of all deaths in

Australia (Donovan, Carter, Jalleh, & Jones, 2004), but it also receives reasonably frequent

attention from the media, exposing the public to ‘everyday’ stories about cancer suffering

or survival (or ‘beating the odds’). It could therefore be presumed that many adults in

Australia have first- or second-hand experience of cancer. Given the public profile and high

incidence of cancer, there should be a relative ease with which cancer-related memories,

beliefs, and attitudes can be brought to mind.

A landmark study on the way people judge the frequency of different causes of

death found that sources of judgement bias are embedded and resistant to modification

(Lichtenstein, Slovic, Fischhoff, Layman, & Combs, 1978). Given the sources of these

biases can be traced to disproportionate exposure, or to ‘imaginability and memorability’ of

events (Lichtenstein et al., 1978), a powerfully emotive topic such as cancer screening may

be an excellent candidate for the employment of bias in judgement and decision-making.

To learn the true frequency of an event, an individual needs to distribute attention equally

to all alternative occurrences of an outcome (Estes, 1976, cited in Lichtenstein et al., 1978),

for example, to actively consider all alternative causes of death. Because it is extremely

unlikely that an individual will learn the accurate frequencies for all possible causes of

death, such frequency judgements will have intrinsic biases. To assist frequency

judgements, individuals are likely to use cues, as well as the ease with which they can bring

to mind the event in question (Tversky & Kahneman, 1973). The ease of imagining or

envisaging an event may be influenced by persistent exposure to the event via various

forms of media representation, as well as by personal experience (Busselle & Shrum,

2003). The degree of vividness and clarity of an event will also allow it to be more easily

recalled, even if exposure to the event is less frequent (Lichtenstein et al., 1978).

57

Although it might be expected that thoughts and images of cancer can be easily

invoked (even positive accounts of overcoming cancer), there is still a common tendency to

avoid early detection screening procedures designed to reduce CRC mortality. An

alternative interpretation is that cancer-related events resist accurate or easy cognitive

recall, perhaps because of their dangerous, frightening and undesirable nature. The

availability heuristic leads to the proposition that if events were difficult to recall or

imagine, then their perceived likelihood of occurrence would be correspondingly small

(Sherman, Cialdini, Schwartzman, & Reynolds, 2002). Subsequent judgements about the

likelihood of experiencing a moderately ‘unavailable’ event would be reduced.

2.4.1.1 Availability, risk perception, and CRC. Perceptions about the frequency of

CRC are likely to impact personal risk judgements. Individuals who overestimate CRC

frequency may consider their own risk as relatively high, compared to someone who

underestimates the frequency of CRC. It is possible that the availability of CRC incidence

influences risk perceptions and screening motivation in tandem with other cognitive biases,

such as how similarly the individual fits with the stereotype of a bowel cancer patient (the

representativeness heuristic), and what kinds of numerical anchors for bowel cancer and

screening risks have arisen prior to the risk estimate (anchoring and adjustment heuristic,

discussed below).

2.4.2 Anchoring and adjustment errors in health behaviour decisions

The anchoring and adjustment heuristic refers to a disproportionately heavy reliance

on one piece of information (an ‘anchor’ or starting reference point) during uncertain

decision-making. If the reference point is implausible, the anchor is automatically adjusted

until a feasible estimate is reached. Conclusions incorrectly based on an anchor can persist

despite the acquisition of more accurate information (Senay & Kaphingst, 2009), as the first

‘ballpark’ estimate (the anchor) tends to be powerful and persistent by having set a

normative precedent (Kahneman & Miller, 2002). Thus, subsequent estimates are usually

always measured against the first estimate or anchor. Even implausible anchors have been

shown to have powerful effects on judgement and risk estimates (Strack & Mussweiler,

1997).

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The anchoring heuristic is considered to be one of the most robust events in

judgement formation and choice selection (Kahneman, 2003). Errors in an initial estimate

can stem from a number of sources including memory schemata (more peculiar events may

become more embedded in memory), attentional biases, limited familiarity with the topic,

and media coverage. Erroneous anchors are difficult to correct, and their influence on

subsequent decision-making may be detrimental and persistent. For example, incorrect

anchors may influence subjective perceptions of cancer risk, with subsequent decisions

based on an initial judgement. The receipt of accurate information may lead only to

insufficient adjustment of risk perceptions from the original anchor (Senay & Kaphingst,

2009).

An anchoring and adjustment bias in patient processing of disease-risk information

has been demonstrated in a number of studies on breast cancer risk perception. Cull et al.

(1999) measured participants’ perceptions of breast cancer risk in women with a family

history of breast cancer. They found that women overestimated their risk to breast cancer,

and that this estimate did not become more accurate, even after counselling. Women

appeared to have persistent reference points about their own breast cancer risk, which were

resistant to objective disease risk communication. This finding may have important

implications in understanding how to communicate cancer risk to the public.

2.4.3 Representativeness bias in health behaviour decisions

The representativeness heuristic occurs when people judge that a single case (e.g., a

person) belongs to a particular population (e.g., vulnerable to bowel cancer) according to

schemas, stereotypes or generalizations (Peters, McCaul et al., 2006), while less salient

characteristics of the case or context that might indicate otherwise are disregarded.

Representativeness allows a person to make rapid judgements of probability, during which

the individual is not aware that judgements of representativeness are being substituted for

judgements of probability (Yudkowsky, 2008). For example, irrelevant features or single

risk factors of cancer may receive disproportionately less attention by someone who does

not fit the criteria of a vulnerable group (e.g., family history of bowel cancer, smoker, poor

diet, or sedentary lifestyle), whilst ignoring the risk of cancer in lieu of obvious risk factors.

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The representative heuristic may affect an individual’s perceived likelihood of

susceptibility to particular cancers because of false expectations that one needs to belong to

a vulnerable group, or alternatively because of beliefs that the risk of cancer mortality is

minimal compared to more widely reported causes of death in the media (e.g., house fires

or murder). One of the central features of representativeness is that an event will be judged

as being more probable the better it represents its stereotype (Tversky & Kahneman, 1973).

Gerend and colleagues (2004) surveyed women about mammography screening,

and found that the representativeness heuristic, in combination with disease characteristic

beliefs, predicted perceived breast cancer risk over and above true medical risk. Women

estimated their own probability of developing breast cancer based on their similarity to the

‘typical’ woman who gets breast cancer, thereby basing their perceived risk on their

implicit theories of the causes of breast cancer (Gerend et al., 2004). In reality, risk factors

for many cancers may only increase risk marginally, whereas the population is at risk with

or without ‘typical’ personal characteristics that might be associated with the development

of a cancer (e.g., a non-smoker is still at average risk of CRC, despite smokers being

considered to have greater risk). Such judgements may involuntarily influence a person’s

proclivity to participate in screening for disease, and indeed this is a theme that often arises

in qualitative research findings. Denberg and colleagues (2005), for example, found that

participants most commonly cited ‘no family history’ as a reason not to schedule an advised

colonoscopy, while a number of other researchers have reported similar findings

(Wackerbarth et al., 2005; Worthley et al., 2006).

Two studies using qualitative designs asked respondents about their perceptions of

CRC (see McCaffery et al., 2001 and Weitzman et al., 2001), and their findings echo what

can be described as errors associated with heuristic use. For example, some female

respondents demonstrated errors of the representativeness heuristic by indicating that

screening is less important to them because CRC was viewed predominantly as a male

disease. Despite awareness that CRC also affects women, this erroneous reasoning was

used as the basis for not screening. This finding is analogous to a number of studies where

women have reported beliefs that CRC is mostly a man’s disease (Donovan & Syngal,

1998; Friedemann-Sánchez et al., 2007), whereas the true difference in risk is only

relatively small (Eddy, 1990) with a lifetime risk of 5.3% for men and 3.6% for women

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(DHA, 2009). This small difference may exacerbate a gender bias favouring male screening

engagement (Clavarino et al., 2004). These beliefs can therefore potentially influence

judgements about the necessity of screening. Stereotypes about illness occurring

predominantly in the opposite sex might impede perceptions about the probability of

experiencing that illness, and about the value of being screened. Such biases may partially

characterize the traditionally lower rates of female participation reported in some studies on

bowel cancer screening.

A survey of risk perception and knowledge about the importance of CRC screening

revealed that one of the reasons participants were not up-to-date with screening was due to

a lack of symptoms (Berkowitz et al., 2008). Although the authors interpret these responses

as indicating a lack of knowledge about bowel cancer, participants may have also been

invoking the representativeness heuristic. Despite being offered a screening test,

participants not belonging to a ‘symptomatic group’ may have concluded screening

unnecessary. Over 60% of the sample felt that their chance of getting bowel cancer

compared with peers of the same age was very or somewhat lower (only 7% felt that they

had a higher likelihood of getting cancer), reflecting optimistic biases toward one’s own

health and indicating that biases were operating in addition to low levels of CRC

knowledge that were reported. In accordance with the representativeness heuristic, it may

also indicate a propensity to believe that people who fit into other representative groups of

CRC sufferers (symptomatic, male, older, poorer health) are the main targets for bowel

cancer screening. Therefore, believing oneself to be more susceptible to CRC may

correspond with beliefs about sharing essential characteristics with groups that are typically

seen as vulnerable to cancer (Gerend et al., 2004). In summary, according to the

representativeness heuristic, the belief that there are similarities between oneself and the

typical person who develops CRC is likely to influence the steps one takes to prevent that

illness.

2.5 Offsetting the ‘Cost’ of Heuristic Biases

Although systematic thinking (System 2) can often detect and correct errors

associated with experiential cognitive processing (System 1), paradoxically, becoming

aware of heuristic-use can also lead to overcorrection and potential for further error

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(Kahneman, 2003). Based on the experiments of Nisbett, Krantz, Jepson, and Kunda

(1983), Zukier and Pepitone (1984) and Agnoli and Krantz (1989), Kahneman (2003)

suggests three ways that biases arising from heuristic use, or experiential processing, can be

influenced by environmental manipulations. Firstly, by increasing alertness in monitoring

activities in order to aide the detection of errors generated by System 1; secondly, by

providing stronger cues to the relevant rules (for example through wording and context),

thereby hindering a reliance on more ambiguous and irrelevant cues; and thirdly, by

extensive training in applied statistical reasoning, however even trained statisticians remain

prone to availability and representativeness biases.

Although there may be various theoretical and to some extent practical means of

achieving this end, for most decisions, such as whether or not to screen for cancer, these

aims will be unrealistic, as it is unlikely the average person will be exposed to all or any of

these suggested methods of reducing heuristic reliance. It is therefore important to develop

an understanding about the ways heuristics can impact important health decisions. For

example, people may rely on representativeness judgements that separate them from

higher-risk groups, thus decreasing their interest in screening. Kahneman (2003) argues that

overcoming the effects of heuristics by becoming aware of ones biases may be more

difficult for the representativeness heuristic, than for availability or anchoring heuristics.

Over two studies, this thesis will assess the influence of one of these common

heuristic biases: representativeness (Kahneman & Tversky, 1973). As outlined in Section

2.3.1, several studies identify barriers to screening that can be interpreted as biases

associated with the representativeness heuristic, and while there is no standardized measure

of such biases in relation to cancer screening, it presents fewer methodological obstacles

than the measurement of anchoring and availability heuristics.

Of theoretical importance is the distinction between screening biases and fatalistic

cognitive styles, with the possibility that aspects of these biases are derivative of defeatist

thinking about screening (e.g., “there is no point in screening if I feel healthy”). It is

important to address this potential theoretical issue by concurrently measuring fatalistic

thinking, and Study 1 therefore includes a brief measure of fatalism (items from the Powe

Fatalism Inventory; Powe, 1995). Screening biases based on the representativeness

heuristic will be examined for their role in explaining the variance in CRC screening

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intentions in relation to other important cognitive, emotive and social variables. For

example, as knowledge of CRC and its symptoms, diagnosis and treatment has been linked

with screening intention and participation (see Section 2.2.4), it is a worthwhile goal to

investigate whether knowledge mediates the effect (if any) of screening bias on CRC

screening.

2.6 Chapter Summary: Cognitions, Bias and Screening Intentions

This chapter reviewed the cognitive literature in relation to CRC screening in two

sections (see Table 2.1 for a summary). In the first section, widely assessed variables

including risk perception, self-efficacy, test-efficacy, cancer knowledge, and cancer worry

were appraised, suggesting there is scope for further assessment of these variables in

relation to CRC screening.

Risk perception is one of the most frequently explored cognitions and is generally

understood to predict greater screening participation. However, the research area is large

and nebulous, and several studies refute these findings. Similarly, cancer worry has been

widely measured but with conflicting findings, possibly due to methodological issues and

inconsistent operational definitions. The cognitive concept of cancer worry is usually

viewed as a motivating factor in screening uptake, with a strong association with perception

of risk, while both self-efficacy and the perceived efficacy of the screening test are also

linked with improved screening uptake and patient compliance, however both factors are

especially sensitive to the nature and invasiveness of the test being offered, as well as

patient values and preferences, and may be best examined and understood according to

whether screening is stool-based or endoscopic. Cancer knowledge is frequently associated

with screening intention, participation, and with aspects of socioeconomic status (e.g., low

levels of education). However, knowledge is likely to be unique from misperceptions about

screening, which may have origins in heuristic biases rather than in deficits of factual

knowledge and information.

Both the limits imposed by an individual’s cognitive capacity, and the mode of

information processing used in health decisions (systematic or heuristic) can be elicited by

environmental cues such as the amount of time available and the amount or complexity of

information. New technical terminology and having more than one option can overwhelm

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the capacity for processing information (Hibbard & Peters, 2003). Heuristics are often used

in quick, easy, and inconsequential decision-making, although even important decisions can

be reliant on error-prone heuristics than more cumbersome (but frequently more accurate)

systematic processing (Reisberg, 2007). This would suggest that a heuristic processing path

is at least occasionally employed in complex decisions, such as whether or not to engage in

CRC screening. This chapter put forward three ways that heuristics might operate to

influence decisions to screen for cancer (availability, anchoring and adjustment, and

representativeness), focusing on the utility of the representativeness heuristic bias in

explaining screening hesitancy.

The measurement of heuristics in health decisions may assist in illustrating how

screening intentions are formed. It might indicate that some people rely on cognitive bias to

inform a complex evaluation of their own risk to bowel cancer. The studies reviewed here

indicate that some existing literature can be re-interpreted as illustrating the effects of

heuristics in cancer screening intention and decision-making, and imply a research

imperative to understand the way heuristics affect screening choices. Many cognitive

concepts have received a large amount of research attention, and heuristics are

comparatively under-examined in studies of health intention. For these reasons, this thesis

explores the involvement of representativeness biases in screening intention more explicitly

across two cross-sectional survey studies.

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Table 2.1

Cognitive Variables, their Theoretical Origin, and the Definition Applied in the Present Investigation

Cognitive Variable Theoretical origina Definition in present research

Risk perceptionb HBM, PMT, TRA/TPB, SCT A subjective judgement about the likelihood of developing CRC

Self-efficacyc HBM, PMT, TRA/TPB, SCT,

MTM

Confidence about one’s ability to successfully participate and complete a specified

CRC screening test

Test-efficacyd HBM, PMT, SCT A belief that the screening test will effectively detect CRC with certainty and

accuracy

Cancer knowledge Public education campaigns, a

prompt (as media coverage) in

HBM

Knowledge about the contributing factors in the onset of CRC; and symptoms,

detection, and treatment options for CRC

Cancer worry Breast Cancer Worry Scale

(Lerman et al., 1991). Has been

explored within the HBM

Intrusive thoughts and images about CRC

Representativeness

(‘screening’) bias

Dual-processing theoretical

framework (Tversky &

Kahneman, 1982; 1983; 1992)

True probability of developing CRC is ignored in favour of stereotyped

(representative) information or beliefs about the likelihood of developing CRC

Note. aHBM = Health Belief Model; PMT = Protection Motivation Theory; TRA/TPB = Theory of Reasoned Action/Theory of Planned Behaviour;

SCT = Social Cognitive Theory; MTM = “Major Theorists” model. bRisk perception is also referred to as perceived risk, perceived susceptibility, and perceived vulnerability across health behaviour models. cSelf-efficacy is also referred to as Perceived Behavioural Control in the TPB. dTest-efficacy is interchangeably referred to as response efficacy in the HBM and PMT.

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CHAPTER 3

THE INFLUENCE OF EMOTION ON CRC SCREENING INTENTION AND

PARTICIPATION


For many years emotion was believed contrary to reason and rationality, and viewed

as an uninhibited response requiring management by the cognitive and rational system (Zhu

& Thagard, 2002). The view that emotion cannot contribute to logic or to reasoned and

rational thought is analogous with many early theories of decision-making. Decision-

making literature, once based primarily on a utility approach assuming rational decision-

making, is gradually incorporating reason-based choice, meaning that a decision is

considered to be a function of both how justifiable it is and how it makes one feel

(Reisberg, 2007). Instead of relying only on calculations of subjective utility (e.g., is the

option moving one closer to an overall goal?), many judgements and decisions appear to be

based at least partly on emotional precursors. In health behaviour research, there is growing

emphasis on the influence of affective factors. Increasingly, emotion is being considered

less of an appendage to social-cognitive health models, and instead, addressed and studied

as an integral component of health decisions.

This chapter will review the role of discrete emotions in intentions to screen for

CRC, firstly by presenting the relevant conceptual material from theories of emotion in

decision-making, and secondly by proposing the potential influences of particular discreet

emotions on cancer screening intention. A range of emotions is posited to be discrete,

however only anger, sadness, happiness, disgust, fear, and surprise have easily reached

consensus as discrete emotions amongst researchers in the field (Keltner & Buswell, 1997).

While embarrassment has not always been considered a discrete emotion (and instead a

variant of shame [Lewis, 1993], social anxiety [Schlenker & Leary, 1982], or an aspect of

sadness [Higgins, 1987]; Keltner & Buswell, 1997), some researchers have convincingly

argued that it is a credible discrete emotion, meeting the same criteria used to classify other

emotions as discrete (Ekman, Levenson, & Friesen, 1983; Keltner & Buswell, 1997).

Specifically, this thesis will describe and illustrate the implication of fear, disgust, and

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embarrassment as discrete emotions in the formation of uncertain and complex intentions

about screening for CRC.

3.2 How Emotions Differ from Cognitions in Explanations of Health Behaviour

Zajonc (1998) suggests that emotions differ from cognitions by providing an

approach or avoidance cue rather than the true-false distinction made by cognitive

assessments. According to Zajonc (1980) and more recent psychophysiology researchers,

emotions arise automatically and often before systematic cognitive appraisal commences,

and therefore activation of the arousal system (e.g., fear) does not always rely on conscious

awareness (Öhman, 2005; van den Hout, De Jong, & Kindt, 2000). The true-false feature of

cognitions may lead to significantly different responses for health options than would

emotions, which facilitate an individual’s action tendency to approach, or to avoid or

withdraw from a situation, and also help to drive behaviour in risky or uncertain conditions

(Loewenstein, Hsee, Weber, & Welch, 2001).

It is increasingly argued that emotion be recognized as having a direct and primary

role in motivating health behaviour (Barrett & Salovey, 2002; Clark & Fiske, 1982; Forgas,

2000; Le Doux, 1996; Zajonc, 1980). Few health models make conjoint predictions about

both cognitive and emotive influences in health decisions, despite a broad acceptance of the

presence of emotions in other areas of decision-making. However a small number of recent

health behaviour theories have begun the process of incorporating emotion into their

hypotheses (The Common Sense Model by Leventhal, Diefenbach, & Leventhal, 1992, and

the C-SHIP (Cognitive-social health information processing) model by Miller, Shoda, &

Hurley, 1996 are examples). Moreover, manipulation of emotion has been applied in many

public health programs (e.g., elements of fear, shame, and embarrassment have all been

used in various quit-smoking campaigns in Australia). A large literature on fear-appeals, for

example, illustrates that public interventions and programmes are often based on the

recognition that the inducement of fear can influence decisions to engage in or to avoid a

particular behaviour. Conversely, many major health models concentrate solely or mostly

on the cognitive processes involved in health decisions, and lack specific hypotheses about

the ways in which emotions operate during health decisions. Of the theories that do include

emotion, many posit that its role is as an anticipated outcome of a choice (for example,

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anticipated regret), while few health behaviour theories address both its anticipatory and

direct influence in the decision process (Bechara, Damasio, & Damasio, 2000).

Both cognitions and emotions may be modifiable, and even highly malleable, and

therefore make useful intervention targets for understanding and improving screening

attendance. Many emotions are also argued to considerably add to the variance already

explained by cognitions, sometimes explaining variance over and above cognitive and

demographic factors (Bowen et al., 2003; Magai, Consedine, Neugut, & Hershman, 2007).

As emotions become integral in decision-making literature, they also require further

exploration to understand their processes across preventive health behaviours such as

cancer screening. Table 3.2 summarizes findings from a qualitative study, which reports

significant emotional barriers to both FOBt and colonoscopy (Janz et al., 2003), implying

discrete emotion is as worthy as cognition in empirical assessment of screening prediction.

Table 3.1

An Example of Participant Reporting of Specific Barriers for Each Colorectal Cancer

Screening Procedure by Emotion and Cognitiona

FOBt % of patient

reporting

Colonoscopy % of patient

reporting

Do not know how (C) 25 No need/no problems (C) 40

No need/no problems (C) 25 Embarrassing (E) 35

Embarrassing (E) 22 Anxious about procedure (E) 32

Afraid of results (E) 14 Pain [i.e., fear of] (E) 28

Too unpleasant [i.e.,

disgust] (E)

10 Afraid of bleeding/tearing (E) 11

Note. Coded so that Emotive is (E) and Cognitive is (C). aSource: Janz et al. (2003).

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3.3 Affect or Emotion?

Definitions of emotion and affect vary widely. These terms have been used

interchangeably in the literature, although leading neuropsychological researchers have

stated that they prefer to avoid “vague and poorly defined concepts such as affect […]”

(e.g., Le Doux, 2000, p. 155). Watson and Clark (1994, p. 89) define emotion as integrated

psycho-physiological response structures containing three differentiable systems: (1) a

prototypic form of expression (typically facial), (2) a pattern of consistent autonomic

changes, and (3) a distinct subjective feeling state; the same response categories proposed

by Lang (1985). Emotions are thought to achieve an adaptive advantage by arising

automatically, non-consciously, and rapidly in response to affective-cognitive processes

interacting with the stimulus (Izard, 2007).

By contrast, affect is distinguished by an overall valence toward an object or event. It

occurs in response to a specific stimulus and engenders positive or negative diffuse

feelings, and which may also be viewed as a quality (e.g., specific feelings of ‘goodness’ or

‘badness’) associated with a stimulus (Peters, Lipkus, & Diefenbach, 2006, p. 46). Affect is

a feeling state that people experience, and although invariably related, it is distinct from an

incidental mood state. The diffuse feelings and the quality associated with the stimulus are

two concepts which tend to be related (Finucane et al., 2000). While affect is useful in

obtaining attitudes toward existing or emerging events and objects and may be useful in

broader health behaviour models, a discrete emotion approach can aid the analysis of

specific styles of responding to particular events. A distinction between discrete emotions is

important to begin examining the differential effects that emotions produce in screening

decisions, both in terms of the different types, and also the varying degrees, of emotion.

Drawing from the emotion literature, the argument that cancer screening intentions

necessarily evoke discreet emotions can be understood by various examples, including the

research of Berkowitz (1993: model of anger formation) and Zajonc (1980), purporting that

the measurement of distinct emotions may provide more meaningful information about

emotive influences on health intentions than valence-based affective hypotheses (i.e.,

‘positive or ‘negative’ affect). This approach is consistent with evidence that distinct

emotions of the same valence (e.g., anger and fear) can relate differentially to judgements

(Lerner & Keltner, 2000).

69

This thesis will therefore propose that emotions are integral in screening decision-

making where, in addition to an initial automatic visceral reaction, there is also an

awareness of feelings as distinct and describable emotions. This view parallels the dual-

process approach pervasive in cognitive psychology (of both automatic and deliberate

processing routes), and central to a number of psychological behavioural and cognitive

theories including the Elaboration Likelihood Model (ELM; Petty & Cacioppo, 1984) and

the heuristic-systematic approach (Kahneman & Tversky, 1973). It is an approach that is

also comparable to some of the foremost models of affect and emotion that have arisen in

experimental psychology over the last three decades. For example, Berkowitz’s model of

anger and aggression (1993) suggests a multi-path route for emotion, which is consistent

with recent work by neuropsychologists (e.g., Le Doux, 1996) and in line with other

affective models proposed by psychologists (e.g., Epstein, 1990; Leventhal, 1980; and

Zajonc, 1980). As with cognitions, processing resources and capacity are therefore also part

of the emotional driving force behind choice selection in decision-making.

3.4 Defining and Measuring Emotion

While there is no single uncontested definition of emotion, there are distinct

strategies to examining emotion, including broader dimensional systems (encompassing a

number of emotions in one or more areas), or categorical emotions with mainly separate

neural circuits or systems (Murphy, Nimmo-Smith, & Lawrence, 2003). This thesis will

apply categorical-based definitions to the exploration of disgust, embarrassment and fear as

unique emotions in CRC screening intentions. A discrete (multi-system) account of

emotion is adopted, which suggests there are categories of distinct emotions.

Multisystem models of emotion are categorical accounts which maintain that there

are a set of separate emotions mediated by central ‘affect programs’: a neural mechanism

that stores and triggers complex emotional responses quickly and often automatically, such

as anger, happiness, sadness, or surprise (Ekman, 1992, 1999; Ekman & Friesen, 1982). In

a multisystem account, it is thought that emotions are linked to neural activity or neural

systems, rather than to hemispheres (which has been suggested by broad dimensional

accounts of emotion) (Murphy et al., 2003). The basis for multimodal system models is the

universality of emotions; that the psycho-physiological responses to the emotion stimulus

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are considered to be impervious to cultural differences (although the stimulus itself may be

uniquely cultural); and can be identified by distinctive facial muscle movement and

expression (Murphy et al., 2003).

The three response systems that help to define emotion (facial expression, autonomic

responses, and a subjective feeling state) guide the complete definition of each emotion.

For example, when disgust is experienced, characteristic facial expressions form including

a raised upper lip and furrowed brow and/or nose wrinkle (Stark, Walter, Schienle, & Vaitl,

2005). Secondly, consistent autonomic changes for disgust have also been reported,

including the movement of the levator labii facial muscle (associated with characteristic

facial expressions such as lip movement) (Schienle, Stark, & Vaitl, 2001) and an increase

in electrodermal activity (Lang, Greenwald, Bradley, & Hamm, 1993). Finally, in the third

component, there are documented subjective feeling states of “repulsion”, “aversion” and

“repugnance” to disgust stimuli (Haidt, McCauley, & Rozin, 1994). Each of the three

discrete emotions investigated in the present thesis will be discussed and defined in greater

detail in sections 3.7.1 to 3.7.3.

3.5 Neuropsychological Evidence for the Role of Emotion in Decision-Making

Over recent decades, a large body of neuropsychology work has yielded

considerable evidence that decision-making, especially complex decision-making, is

severely impaired without emotional or affective input (Bechara et al., 2000). For example,

defective emotional processing or somatic signalling (encompassing musculoskeletal,

visceral and internal components) due to neurological lesions and brain damage can

severely inhibit the normal decision-making process in both personal and social spheres

(Damasio, 1996). Understanding emotional processes and their anatomical systems is

therefore imperative (Le Doux, 2000) and facilitates a comprehension of how emotions

relate to each other, and the environments and conditions in which different emotional

processes occur.

The somatic marker hypothesis (Damasio, 1994) states that there are bodily markers

that guide behavioural decisions. Damasio’s work arose from observations of brain-

lesioned patients who demonstrated a capacity to intellectually reason a decision but an

inability to associate choices and outcomes with emotion. By observing people with certain

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types of brain damage, Damasio argued that the decision-making process is degraded when

somatic markers are absent, and enhanced when somatic markers are present.

Damasio’s research demonstrated key findings about the inherent role of emotion in

decision-making. The primary argument of the somatic marker hypothesis is that emotional

signals steer the beginnings of the decision process (Bechara et al., 2000). Although this

hypothesis has not been extensively explored in various decision environments, complex

personal and social decision contexts appear to benefit most from a combination of

emotional and cognitive input (Cacioppo & Gardner, 1999; Damasio, 1996). During

decision-making, individuals are alert to emotional ‘somatic’ markers, which act either as

an incentive for the individual to respond positively to a specific choice option, or to steer

them away.

3.6 Emotional Biases and Decision-Making

Individuals sometimes resort to a ‘gut instinct’ during decision-making (Rozin,

Markwith, & Ross, 1990), which may become one of the main affective-based motivations

for selecting a particular option when decision-making is complex and processing resources

are limited. While emotions often provide reliable indicators about the benefits or dangers

of a given situation, these signals can also be misconstrued or imbued with undue

importance, leading to flaws in decisions and their behavioural outcomes. For example, fear

may lead to avoidant behaviour or avoidant coping styles such as denial or repression;

disgust may deter somebody from partaking in a healthy behaviour or consuming healthy

foods; while embarrassment may prevent a person from seeking medical advice, and

contribute to avoidant coping styles and pathological states (such as social anxiety) (Fahlen,

1998). However, there are also situations in which emotions are a component of more

deliberate processing and conscious decision-making. These emotional states may be

connected to social factors, for example, feeling sympathy for another’s suffering, feeling

angered by another’s social misdemeanour, or feeling embarrassed discussing bodily

functions with a doctor.

People are powerfully motivated to avoid negative emotions, and therefore tend to

gravitate toward options that reduce the possibility of experiencing them (van Dijk &

Zeelenberg, 2007). Despite this tendency, people do not demonstrate a strong ability to

72

predict future emotional reactions, which can consequently contribute to poor decision

outcomes (Gilbert, Morewedge, Risen, & Wilson, 2004; Reisberg, 2007). These biases

become more difficult to override when a person is also under cognitive load (Wilson,

2002, cited in Wilson and Gilbert 2003); a likely scenario when weighing up the costs and

benefits associated with CRC screening. While individuals are better equipped than they

realise to deal with negative events, this lack of awareness may contribute to avoidance

behaviours, with potentially negative outcomes. Therefore many people may not give

themselves sufficient credit about being able to handle negative events like job loss,

bereavement, divorce, and illness diagnoses (Gilbert & Ebert, 2002).

3.7 Discrete Emotions and CRC Screening Intentions

Emotions are likely to be central to preventive health behaviour, acting as an

incentive or disincentive to participate (Chapman & Coups, 2006). As preventive health

behaviours such as cancer screening tend to be repeatable, the emotions experienced at the

initial decision may also affect subsequent decisions to participate (Chapman & Coups,

2006), and may continue to play a role as a result of associative learning. Some emotions

during decision making receive more deliberate reflection, and as a result are identifiable

and measurable. Alternatively, the experience of screening may engender a sense of

confidence about participating in future tests, and mollify the emotional obstacles

associated with screening.

Exploratory research indicates that different emotional categories may impact on

health decisions differently (Magai et al., 2007), justifying a discrete emotions approach to

the investigation of emotions in screening decisions. Qualitative studies and a small number

of quantitative studies using single-item measures of fear, disgust or embarrassment have

shown that the presence of these discrete emotions is associated with CRC screening

avoidance (Consedine et al., 2004; Consedine & Moskowitz, 2007; Denberg et al., 2005;

Fyffe, Hudson, Fagan, & Brown, 2008; Goldsmith & Chiaro, 2008; Green & Kelly, 2004).

The three distinct emotions explored in the present thesis: medical embarrassment, fear, and

disgust, will all be measured comprehensively in an attempt to understand their specific

roles in relation to CRC screening decisions, and in relation to variables such as social

support and social norms, risk perception, and self-efficacy beliefs.

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3.7.1 Medical embarrassment

It is well documented that feelings of embarrassment occur during medical

examinations across a range of intimate health problems, including gynaecological and

sexual health checks (Shifren et al., 2009); breast cancer checks (Alexandraki &

Mooradian, 2010; Consedine, Magai, & Neugut, 2004; Garbers, Jessop, Foti, Uriberlarrea,

& Chiasson, 2003; Hanson, Montgomery, Bakker, & Conlon, 2009), incontinence (Truter,

2009), and visiting the dentist (Levin, 2003; Moore, Brødsgaard, & Rosenberg, 2004).

Embarrassment or a risk of experiencing embarrassment is therefore a well-known barrier

to people seeking medical assistance (Consedine et al., 2007), and almost certainly has

consequences for CRC screening. Therefore a clear corollary issue of the role of

embarrassment in preventing or delaying an individual seeking medical attention should be

an empirical investigation into its influence on CRC screening intention and participation.

The empirical cancer screening literature provides only narrow accounts of the role of

embarrassment in decisions to screen for CRC, however a small pool of published studies

indicate a negative relationship with screening attendance (e.g., Busch, 2003; Janz et al.,

2003; Jepson et al., 2000).

3.7.1.1 Defining embarrassment. Embarrassment is thought to be a social emotion,

related to a fear of negative evaluation and violations of social norms (Keltner & Buswell,

1997), and involving a combination of affective and cognitive elements (Consedine et al.,

2007). A range of non-verbal physical displays help to define embarrassment, including

gaze aversion, shifting eye positions, speech disturbances, face touches, a nervous smile,

and a rigid slouched posture (Keltner & Buswell, 1997). Blushing is also often perceived to

be an indication of embarrassment, however it is not unique to embarrassment, and can also

commonly occur with other feelings such as shame and anger (Keltner & Buswell, 1997).

While the avoidant component of embarrassment is commonly highlighted, it should

not underscore the functional perspective of embarrassment, which is that it can encourage

pro-social behaviours, and protect an individual from being rejected by their social group

(Consedine et al., 2007). It is therefore likely to co-occur with a feeling that one needs to

avoid the potentially embarrassing stimulus or anti-social behaviour (Consedine &

Moskowitz, 2007). Keltner and Buswell (1997, p. 260) provide a social evaluation account

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of embarrassment, presenting evidence for embarrassment as a distinct emotion according

to criteria that all discrete emotions possess (as opposed to other affective states), including

antecedents to embarrassment (e.g., loss of body control, failure to maintain privacy, and

awkward social interactions or undesirable attention); patterns of appraisal and experience

(e.g., evaluation, lack of control, others’ laughter); nonverbal displays; and physiological

responses.

3.7.1.2 Medical embarrassment and CRC screening. Many patients report that they

experience embarrassment discussing bowel habits and other sensitive topics with their

doctor (Smith, Pope, & Botha, 2005). The Australian NBCSP mails kits directly to the

eligible Australian public, removing the need for patients to initiate contact with their GP to

discuss participating in CRC testing. While this may reduce the need for potentially

embarrassing face-to-face discussions with a health professional, it does not address

embarrassment associated with returning the sample (even when returned by post;

Friedmann-Sánchez et al., 2007), or by having to attend follow-up appointments and

colonoscopy in those patients who test positive from a FOBt. Furthermore, there may be

people ineligible for the national screening test due to being outside the sampled screening

cohort, or outside the recommended screening age, who may experience worrying

symptoms and accompanying embarrassment about seeking medical advice.

Patient delay in consulting a medical professional for advice about a bowel-related

symptom is a significant issue in the early diagnosis of illness, and a number of studies

attribute part of this delay to embarrassment (Bleiker et al., 2005; Byles, Redman,

Hennrikus, Sanson-Fischer, & Dickinson, 1992; de Nooijer et al., 2001; Menees et al.,

2005). Patient delay refers to the interval between the recognition of an unexplained

symptom and the time that the patient requests a health check for that symptom (de Nooijer

et al., 2001).

Embarrassment has often been cited as a major deterrent not just to seeking medical

advice about specific or non-specific symptoms, but also to participation in CRC screening.

James, Campbell, and Hudson (2002) conducted focus groups and administered surveys to

397 African-American adults over the age of 50 who were participating in a health

promotion program, asking them to indicate their main barriers to participating in FOBt,

sigmoidoscopy or colonoscopy. The biggest barrier in the sample was a lack of GP

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recommendation to screen (55%), however embarrassment associated with the tests was

stated as a significant barrier by 22% of the sample, and was the fourth greatest perceived

overall barrier in screening for CRC. Similarly, Janz and colleagues (2003) conducted a

population-based study with a mixed racial sample of 355 men and women in the US and

reached similar conclusions, with embarrassment cited as the main psychological barrier to

participating in any of the bowel screening tests. Twenty-two percent of the sample

reported the FOBt as embarrassing; 37% reported the sigmoidsocopy as embarrassing, and

35% the colonoscopy as embarrassing.

Embarrassment may continue to be a screening impediment even in those people

who have previously participated in CRC screening. In a survey of 300 outpatients from a

health clinic in the United States, Harewood, Wiersema, and Melton (2002) compared

barriers to participating in colonoscopy between retrospective and prospective screeners,

and found that embarrassment was cited by both groups as one of the four main deterrents

to screening. Although this was a between-groups design, the authors found no difference

between prior or future screening in the reporting of embarrassment as a screening

deterrent, suggesting that completing a colonoscopy may not reduce embarrassment related

to future screening decisions.

Conversely, other research shows that the experience of negative emotions such as

embarrassment may help to differentiate between CRC screeners and non-screeners,

suggesting it may be less common in those who have already participated. Looking

specifically at CRC endoscopic screening, Codori et al. (2001) explored health beliefs

among FDRs of colorectal cancer patients. In a survey of 1160 respondents, they found that

of the eight reasons given for avoidance of endoscopy, only embarrassment was

significantly correlated with actual screening avoidance, and was significantly associated

with never having undergone an endoscopy for colon cancer.

3.7.1.3 Typologies of medical embarrassment. While medical embarrassment has

principally been assessed as a unidimensional concept, Consedine et al. (2007)

discriminated between two distinct factors of medical embarrassment in general health

behaviour, noting that it is insufficient simply to know that people experience

embarrassment in a particular medical setting. Instead, they argue there should be a

comprehensive measure of embarrassment that identifies the major aspects associated with

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help-seeking behaviour and patient-doctor interactions. Assessing medical embarrassment

in nine domains via the development of a 31-item instrument, the authors found that both

bodily embarrassment (e.g., embarrassment about body awkwardness, and being seen or

touched by a doctor or nurse) and concerns about being judged (e.g., concerns about being

perceived negatively) were two distinct aspects of embarrassment.

By delineating the unique aspects of medical embarrassment, there is opportunity to

formulate directional hypotheses about which features of embarrassment bring about which

specific behavioural outcomes. For example, Consedine et al. (2007) argue higher bodily

embarrassment may be associated with less contact with medical professionals. They also

found that concerns about being judged were related to more frequent psychological visits

but less frequent sex-related visits (and no association with general visit frequency), which

suggests that high judgement concern may reduce contact with health professionals for

sensitive, private symptoms and concerns (such as bowel symptoms). While a range of

health problems exist that may prompt embarrassment upon physical examination by, or

discussion with, a doctor, there is a dearth of empirical understanding about unique aspects

of embarrassment, and therefore an untapped opportunity to assess the role of these two

differential aspects of medical embarrassment in CRC screening intention, both of which

will be examined in the present thesis.

3.7.1.4 Gender differences in embarrassment. There is empirical support for the

view that women are more likely to seek help for a diverse range of health problems, while

men tend to exhibit delayed help-seeking behaviour (Galdas, Cheater & Marshall, 2005;

Mansfield, Addis, & Mahalik, 2003), so it is interesting that many studies report greater

uptake in bowel screening amongst men (Brennenstuhl et al., 2010; Donovan & Syngal,

1998; Janz et al., 2003; Weitzman et al., 2001). It has been reported that women suffer from

greater embarrassment than men (Miller, 1992), and are more likely to report

embarrassment as a reason for not taking part in CRC screening than men (Peterson, Murff,

Ness, & Dittus, 2007). As such, there may be differential gender effects depending on the

mode of screening.

Friedemann-Sánchez et al. (2007) conducted focus group interviews and noted that

embarrassment was a significant concern among women when they were considering

colonoscopy, but not FOBt. Women perceived FOBt as the least embarrassing screening

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method (because it involved mailing instead of personal contact). Conversely, in the same

study men reported more embarrassment in relation to mailing the FOB test, but less about

undergoing a colonoscopy. Females also cited affective concerns about the invasiveness of

colonoscopy, and a feeling of being “exposed” or vulnerable, however, they also reported

feeling more comfortable about the suggestion of colonoscopy after being informed that

sedation is accessible. While a small handful of studies have reported no difference in

women’s screening participation between those offered a female endoscopist and those who

were not (see Denberg et al., 2010; and Nicholson & Korman 2005), the results of

Friedemann-Sánchez and colleagues support the findings of a number of other studies

exploring gender differences in patient delay for CRC screening (e.g., Farraye et al., 2004;

Fidler, Hartnett, Cheng Man, Derbyshire, & Sheil, 2000; Menees et al., 2005; Stockwell et

al., 2003).

Menees and colleagues (2005) administered questionnaires to 202 women patients at

outpatient health facilities in the US. Nearly half of the women (43%) reported they would

prefer to be seen by a woman endoscopist, and of these women, most of them (87%) were

willing to wait more than a month while 14% were prepared to pay more, and 5% would

not undergo a colonoscopy at all unless the endoscopist was female. Nearly all of the

women who preferred a woman endoscopist (75%) cited embarrassment as the key factor.

It is important to understand whether same-sex endoscopists are important to Australian

women’s screening participation, particularly as fewer than 10% of Australian

gastroenterologists are women, and there is an even greater ratio of male to female

gastrointestinal surgeons (Rosenfeld & Duggan, 2008). Although the gender of the

endoscopist presents a potential barrier, embarrassment remains a hurdle to timely

participation in CRC screening procedures for both men and women. The present

investigation intends to quantitatively assess medical embarrassment according to the

differential but related variables of bodily embarrassment and judgement concern as

defined by Consedine et al. (2007).

3.7.2 Fear of screening

Fear is widely studied in health prevention and promotion, chiefly for its role in

campaigns aimed at discouraging hazardous behaviours like smoking (de Meyrick, 2010),

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speeding (Algie & Rossiter, 2010), and unsafe sexual practices (Witte, 1997), or in the

promotion of health behaviours, such as healthy eating (Chan, Prendergast, Grønhøj, &

Bech-Larsen, 2009). Although it is often argued that fear facilitates preventive health

behaviours, it has also been associated with a complex range of responses that can lead to

maladaptive actions (Consedine, Adjei, Ramirez, & McKiernan, 2008). Fear has been

extensively applied in health campaigns and interventions, however there are few

systematic and reliable scales available that can be used to assess the contexts and various

roles of this emotion in cancer screening.

3.7.2.1 Definition and measurement of fear. Methodological problems abound in

the measurement of fear across health behaviour research, partly because its measurement

has not been source-specific, such that fear of test results and fear of pain have not been

differentially examined; partly caused by idiosyncratic item selection and administration;

and partly because of the many similarities between fear, worry and anxiety, which

manifest in definitional overlap and shared application of measurement scales. Several

relationships have been found between fear, worry, anxiety, and screening uptake,

including positive (Stefanek & Wilcox, 1991), negative (Wackerbarth et al., 2005) and

curvilinear (Lerman et al., 1993) associations. Although fear and anxiety have sometimes

been measured as synonymous constructs, and although they may share some physiological

features during arousal, they are unique and may have distinct impacts on health behaviour

(Jones & Jakob, 1981; Whitley, 1994). Fear will therefore receive unique attention in the

current thesis.

As a discrete emotion, fear occurs in response to threatening events, creating an

aversive emotional state and motivating the individual to engage in coping-focused

behaviours including immobility, escape, or attack; each response being a physiologically

active and attentive state (Öhman, 2005, p. 953). Some of the defining physical features of

a fear response involve lips curling back or stretched mouth, widened eyes, and eyebrows

raised and furrowed (Kohler et al., 2004), while physiological changes include an increase

in autonomic nervous system activity (skin conductance, heart rate, and sweating)

(LeDoux, 2000; Zajonc, 1980; Zhu & Thagard, 2002). Lastly, there are also conscious

subjective feeling states associated with fear including nervousness and feeling scared

(Scherer, 2005; Zhu & Thagard, 2002). Negative events like cancer may be conditioned to

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arouse automatic fear responses. Van den Hout and colleagues (2000) found that activation

of the arousal system could occur by the presentation of threat words (specific phobic and

general threat) under both masked (subliminal) and unmasked conditions, supporting the

theory of pre-attentive processing and the somatic-marker hypothesis in neuropsychological

research (Van den Hout et al., 2000).

3.7.2.2 The role of fear in cancer screening. It is likely that fear is also a

cognitively laden emotion and is involved in deliberative and systematic cognitive

processing. For example, fear related to the screening procedure is likely to be

interconnected with a cognitive awareness of the risks associated with screening

procedures, involving the cognitive processes of vulnerability beliefs and perceptions of

diagnostic test efficacy. These cognitive concepts are presumably necessary in order to

discern which emotion is being experienced, where fear of procedural pain and discomfort

involves cognitively identifying possible risk factors of the procedure (e.g., colon

perforation) and having knowledge about the invasive process of the test. Consequently, the

source of the fear may indicate its type of association with different cognitive processes,

where more diffuse fear associated with mortality and cancer is aroused by an experiential

and automatic mode of processing, and specific fear linked with a cognitive risk analysis,

such as those related to beliefs about risk associated with screening procedures, occurs

within systematic cognitive appraisals.

Individuals may respond to a fearful event either by actively engaging in activities

known to reduce the threat from occurring, or by avoidance of the threatening event, and

fear may therefore be a significant emotional deterrent to the uptake of CRC screening.

Many studies support this premise, however systematic reviews examining the relationship

between fear and cancer screening have not detected a clear pattern, and have instead

identified both positive (McCaul et al., 1996) and negative relationships (Denberg et al.,

2005; Vernon, Acquavella, Yarborough, Hughes, & Thar, 1990). Olynyk, Aquilia, Fletcher,

and Dickinson (1996) examined the NBCSP in Australia to establish CRC screening

participation rates. A total of 15.5% of the 3500 people contacted took part in the study,

whereas of people who had declined the screening invitation, 30% stated lack of time or

interest as their main reason, and 13% stated fear of cancer screening as their primary

reason for declining.

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Both patients and physicians have reported that cancer fear can be a common

motivator and also a barrier to seeking medical attention. Nylenna (1984) found that

amongst 400 participants interviewed after visiting their general practitioner, 47% reported

that they had feared cancer often or sometimes, and this fear did not increase with age but

was prevalent across the age groups (and therefore not correlated with the age-related

increased incidence of cancer). Physicians have reported that they perceive their patients’

greatest CRC screening deterrent to be a fear of cancer diagnosis (Kelly, Phillips et al.,

2007), and have suggested that those patients who say they do not wish to know if they

have cancer may actually be afraid of diagnosis rather than have genuine indifference about

their health status. Supporting this, an Australian study investigated the CRC screening

beliefs of Italian-Australians aged between 50 and 78 (Severino, Wilson, Turnbull, Duncan,

& Gregory, 2009), finding the greatest barrier to participating in CRC screening was cancer

fear. Participants’ descriptions of their cancer fear as a barrier to participation was often

intertwined with the desire to avoid learning whether or not they had cancer, suggesting

that screening avoidance may have been a behavioural response to an underlying fear of

cancer.

Qualitative studies also support the hypothesis that fear acts as a barrier to screening

participation (Denberg et al., 2005; Goldsmith & Chiaro, 2008). Berkowitz and colleagues

(2008) studied a range of beliefs about CRC screening amongst 1,148 participants aged 65

to 89, finding that 22.5% of participants reported a fear of finding CRC. In a recent

Australian qualitative study investigating the uptake of FOBt in the Australian NBCSP

(with 18 screening participants and 12 screening decliners), one participant stated that the

mere receipt of the kit caused immediate fear, which prevented her from considering

participation in the program (Clavarino et al., 2004). Another participant exhibited avoidant

behaviour and rejected screening in order to avert future potential worry about cancer as a

result of participating and awaiting results, suggesting a general fear of cancer.

3.7.2.3 The effects of different sources of fear on cancer screening. To explore the

most common reasons for delaying to seek medical advice about symptoms, Smith and

colleagues (2005) conducted a qualitative synthesis of 32 international qualitative studies

representing data for over 775 individuals (of whom 712 were cancer patients). Two of the

most commonly provided reasons for delaying or avoiding professional medical counsel

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included a delay in recognizing illness, with a tendency to attribute vague or intermittent

symptoms to less significant causes (e.g., ageing, menopause, stress); and secondly,

participants with vague symptoms reported feelings of fear associated with experiencing

future embarrassment and judgement (such as being perceived as a hypochondriac or

timewaster by their GP), or about being diagnosed with a potentially fatal disease and

experiencing unpleasant treatment and painful symptoms. Overall, various reports of fear

were found to be a major barrier in 26 of the 32 studies reviewed, predominantly

manifesting as one of two types of fear: cancer fear; or a fear of future embarrassment.

Several researchers have identified an association between fear of the procedural

aspects of cancer screening (e.g., pain or discomfort) and subsequent screening intentions.

Janz et al. (2007) reported that amongst participants who had attempted to participate in

endoscopy for bowel cancer (but had failed to follow-through with the screening), fear of

pain was one of the top two factors interfering with their decision to complete the

procedure. Likewise, Green and Kelly (2004) found that among a sample of 100 African

Americans, almost half reported a perception that screening would be painful, deterring

them from participating. Similar findings were reported in a US study with rural

participants (Holmes-Rovner et al., 2002), where two major barriers were found across four

focus groups, firstly of concerns about the quality of care they would receive during and

after screening, and secondly, fear related to the potential pain during CRC screening.

Hynam et al., (1995) drew comparable conclusions when they asked 81 participants aged

between 51 and 70 years their reasons for declining to participate in a FOBt screening

program. A fear of further testing and surgery was reported by 24% of participants who did

not request a FOBt kit, and by 20% of those who had requested the kit but returned it

unused, suggesting that fear of hospital procedures and treatment is a significant factor

affecting refusal even at initial stages of screening (FOBt).

As argued by Smith et al. (2005), another type of fear experienced in relation to

CRC screening may be related to a fear of future embarrassment or humiliation. This fear

has been consistently observed across studies on cervical screening participation, indicating

that many women who fail to undergo a cervical screening test report fear of experiencing

embarrassment during the procedure (Fylan, 1998). People are usually less tolerant of

others’ bodily products than their own (Rachman, 2004), and concerns about being

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perceived negatively by medical professionals during physical examinations may further

compound a fear response to screening. A participant in one qualitative study reported that

she was concerned that her sample would be disgusting to those working in the laboratory

where her sample would be sent (Chapple et al., 2008). Concern about mailing a stool

sample back to the laboratory may be associated with such a fear about experiencing future

shame (Friedemann-Sánchez et al., 2007). In Chapple et al.’s (2008) study, where 27

female and 43 male participants were interviewed in focus groups, participants also

reported fear about embarrassing themselves while undergoing a barium enema (e.g.,

“making a mess”). The authors of the study found consistent gender themes across fear

associated with screening, with women reporting predominantly affective fears and men

reporting predominantly physical fears.

While different types of fear involved in cancer screening outcomes have been

recognized by a number of researchers (e.g., Dillard & Nabi, 2006), this approach has been

conceptually illustrated by Consedine et al. (2004), in their model of fear specificity in

relation to breast cancer screening. Consedine and colleagues identify three unique but

highly interrelated sources of fear including fear of physical aspects associated with the

screening procedure; fear of screening outcomes; and an undifferentiated and diffuse cancer

fear. They suggest that these different sources of fear may have different impacts on

screening outcomes, for example a fear of procedural aspects of screening may discourage

screening, while a general fear of cancer may promote screening participation in an effort

to reduce the emotion. This differentiation may help to delineate the various terminologies

and idiosyncratic item selection and administration in the measurement of fear that

currently dominate the health literature, rendering it difficult to establish a coherent

relationship between fear and cancer screening.

Both Smith et al.’s (2005) review and Consedine and colleagues’ (2004) model of

fear specificity are good sources of item generation and selection for use in quantitative

assessment in the present investigation, and helpful in identifying salient themes for the

appraisal of different sources of fear. Four separate aspects of fear relating to

embarrassment and five relating to fear of cancer were reported by Smith and colleagues

(2005) and adapted for quantitative assessment in the present study. Because fear has been

associated with differential effects on screening (Consedine et al., 2004), and in line with

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Consedine et al.’s model of fear specificity, measurement of fear in the present thesis will

be separated into the following three factors: fear of procedural aspects; fear of future

experiences of shame or embarrassment; and fear of cancer and mortality. In this way, the

target of the emotional response is explicit, such as the screening procedure; fearing

humiliation or embarrassment in front of others; or a more diffuse and existential fear of

cancer and mortality.

3.7.3 Disgust and CRC screening intentions

Disgust has been reported in qualitative research as a factor contributing to non-

participation in colorectal cancer screening, and in particular, faecal occult blood testing

(Chapple et al., 2008; Friedemann-Sánchez et al., 2007). A small number of focus-group

studies describe themes about the unpleasantness of FOBt and the disgust associated with

the idea of participation. Surprisingly, there is only cursory understanding of the influence

of disgust on CRC screening and other preventive health behaviours, and an absence of

empirical investigation into disgust and CRC screening. This may be because it has not

been a component of widely applied models of health behaviour, and has received

considerably less empirical attention than other emotions, such as fear or anger (Olatunji &

Sawchuck, 2005), possibly as a less appealing emotion to investigate (Miller, 1997).

3.7.3.1 Definition of disgust. While some researchers have accounted for disgust as

being a defense against disease (Curtis, Aunger, & Rabie, 2004; Davey & Bond, 2006) or

as an emotion evolved from touch and smell (Miller, 1997), most current definitions

incorporate earlier explanations presented by Darwin (1965; cited in Rozin, Haidt, &

McCauley, 2008) and Angyal (1941), which concentrated on the idea of ingesting a real or

imagined contagion, with a particular focus on oral contact (Rozin et al., 2008). Three

features that help to define disgust include facial expression and behavioural responses,

neurological and physiological changes, and subjective descriptions. Behavioural responses

are usually manifested as avoidance or withdrawal, indicating physical rejection of the

disgusting stimulus (Olatunji & Sawchuk, 2005).

Physiological changes also occur during the experience of disgust, where heart rate

is lowered (Stark et al., 2005), skin conductance increases (Lang et al., 1993; Stark et al.,

2005), and dizziness can be experienced (van Overveld, de Jong, & Peters, 2009). Nausea

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may occur (Davey & Bond, 2006; Rozin & Fallon, 1987) and be precipitated by increases

in gastrointestinal mobility (Ekman et al., 1983), although nausea is not a necessary

condition during disgust (Ekman & Friesen, 1975). Subjective feelings of disgust have also

been recorded including reactions referred to as revulsion, repugnance, and repulsion, along

with common verbal responses such as “eugh” (Rozin, Haidt, & McCauley, 1993; Stark et

al., 2005). It is this component of disgust, in relation to CRC screening, under investigation.

3.7.3.2 Types and mechanisms of disgust. Disgust can be so powerful it can

transfer to imitation objects or visually similar items (Rozin, Haidt, McCauley, Dunlop, &

Ashmore, 1999). For example, imitation faeces and mucus can invoke similar levels of

disgust to the real item (Rozin, Millman, & Nemeroff, 1986). This type of disgust, along

with food and animal-related items, has been classified as core disgust (Angyal, 1941;

Rozin et al., 1999). It has been suggested by several researchers that core disgust shares

common elicitors with a number of other disgust domains including such elicitors as poor

hygiene; “inappropriate” sexuality; and body envelope violations (e.g., surgery) and death,

which also evoke a domain of disgust referred to as animal-reminder disgust (Haidt et al.,

1994). Animal-reminder disgust reflects a human tendency to avoid contact with death and

reminders of their animal nature (Goldenberg et al., 2001; Rozin et al., 1999).

Curtis and colleagues (2004) argue that objects with potential disease threat and are

potential sources of transmissible pathogens trigger significantly more disgust than images

with little to no disease relevance. Their findings are based on a large survey run in

conjunction with a science documentary series in the UK, attracting over 77,000

respondents, with a final 40,000 completed and valid survey responses from 165 countries.

Respondents were asked to rate 20 photographs for disgust on a five-point Likert scale.

Disease salient images were matched with similar images that were not disease-relevant,

such as a louse versus a wasp; a weeping skin lesion with pus versus a dry skin lesion; a

towel with a red-yellow stain (to represent blood and bodily secretions) versus a towel with

a blue stain, and so on. Across all nine regions around the world, the disease salient images

were rated as significantly more disgusting, lending further support to the universality of

the disgust experience, and the hypothesis that disgust is an adaptive emotional response

designed to reduce the likelihood of transmissible disease. Threats of contamination and

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infection may alert an individual through evolved processes connected to disgust (Davey &

Bond, 2006).

Not all objects that provoke a disgust response may necessarily be a genuine disease

threat, and in fact individuals can perceive a disease threat from an object while

simultaneously having awareness that it is innocuous. This can occur when an object either

looks disgusting because it shares features of its appearance with a disgusting object, or

because it has been in contact with a disgusting object (Rozin et al., 1999). This

phenomenon is referred to as the sympathetic magical law of contagion (Rozin et al., 1986).

For example, an item of clean clothing previously worn by a disliked person can elicit

disgust (Rozin et al., 1999) especially when this stranger is morally tainted (e.g., has a

murder conviction) (Rozin et al., 2008), while synthetic mucus-like substances and

imitation faeces (Rozin et al., 1986) can also lead to a disgust reaction.

Disgust has also been associated with negative affect that is connected to morally or

socially unacceptable behaviours (Marzillier & Davey, 2004). Disgust associated with

culturally taboo and traditionally private parts of the body may therefore potentially impede

participation in cancer screening, which involves at least minimal social interaction

regarding a sensitive and personal topic. At the asymptomatic stage of CRC, or without any

other compelling need to undergo screening of this nature, high levels of disgust related to

bowel screening procedures may therefore become maladaptive and lead to behavioural

avoidance, ironically contradicting its fundamental adaptive role to help one avoid infection

or disease.

3.7.3.3 The role of disgust in CRC screening. There is a dearth of literature on the

role of disgust in CRC screening and related medical tests and conditions. Smith,

Loewenstein, Rozin, Sherriff, and Ubel (2007) explored trait disgust sensitivity in

predicting mental well-being in colostomy patients, finding that those patients with higher

levels of trait disgust felt more stigmatized and had correspondingly lower life satisfaction.

What is more relevant to the present thesis is that disgust sensitivity was also measured in a

non-clinical sample of individuals, where it predicted a desire to have less contact with

colostomy patients.

Qualitative studies provide further insight into the specific role of disgust in CRC

screening reluctance. Friedmann-Sánchez et al. (2007) conducted a study to determine

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gender differences in screening barriers, with both men and women reporting that they

found the collection process of stool tests disgusting, deterring them from screening.

Concordantly, a minority of participants in Chapple et al.’s (2008) qualitative study

reported finding the idea of bowel screening, and FOB testing in particular, disgusting, with

one woman claiming it took three screening invitations over six years before she overcame

her feelings of disgust and decided to participate. Another woman in the same study stated

that although her husband completed a home testing kit, she found it too disgusting and

decided not to take part, despite a reminder letter and a subsequent kit sent to her two years

later. These studies suggest that it is worthwhile exploring the effects of disgust on CRC

screening in a quantitative design, without which, these associations remain tenuous.

Given the paucity of empirical evidence for the role of disgust in bowel cancer

screening, its relevance may need to be extrapolated from its various elicitors and

evolutionary origins. Animal waste products have been argued as central to the experience

of disgust (Angyal, 1941), and the sampling of bodily waste as required in home stool

testing may increase a perception of being exposed to transmissible pathogens, which is

part of a natural disgust response adaptively aimed at helping one avoid infection or

disease. It could be argued that as humans manage their own body waste on a regular basis

this may lessen the disgust felt while obtaining a sample for a FOB test, which involves no

especially novel or complex equipment or prolonged contact with the sample. However,

humans are powerfully conditioned to find all human (and other animal) waste products

disgusting (Rozin & Fallon, 1987), and indirectly handling bodily waste via a swab may

arouse a significant level of disgust for many individuals. Additionally, the interpersonal

component required at various stages of bowel cancer screening may compound the

dilemma as the objects and events that are deemed to be disgusting tend to be culturally and

socially defined and perpetuated (Olatunji & Sawchuck, 2005; Rozin et al., 2008).

While various domains of disgust are outlined in the literature, including core,

animal-reminder, interpersonal and social/moral disgust, only one or two of them may

relate to the experience of CRC screening. However, there is potential for each domain to

be involved in bowel cancer screening, and it is premature to specify which domain is most

important. The Disgust Scale-Revised (DS-R) (Haidt et al., 1994, modified by Olatunji,

Williams et al., 2007) defines and measures seven types of disgust elicitors: animal

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reminders; contamination related to food; body products (e.g. faeces, urine); sex; hygiene;

death; and body envelope violations (surgery, organs, puncture wounds). Three, or possibly

four of the above disgust elicitors may be involved in CRC screening decisions, including

body product disgust, hygiene associated with sample swabbing, animal reminders in

response to reminders of mortality, and body envelope violations in contemplation of

possible colonoscopy or surgery. As such, it is an appropriate instrument for the

measurement of disgust in relation to CRC screening in this thesis.

3.7.3.4. Gender differences in disgust. Women consistently report significantly

more sensitivity to disgust than men (Haidt et al., 1994; Marzillier & Davey, 2004), and

this seems to be the case universally (Curtis et al., 2004). In line with these findings,

qualitative studies about CRC screening reluctance reveal that women more often report

disgust about using a home screening test (Chapple et al., 2008), which may be another

contributory factor in findings where women report less intention and participation in

bowel screening than men. In a study by Rozin et al. (1999), males went further in

‘disgusting’ tasks (touching objects such as cockroaches and watching disgusting video

clips) than females, possibly due to lower disgust sensitivity (although this may also have

been partly attributable to face-saving behaviour). It is therefore valuable to consider

gender differences in the examination of disgust and CRC screening intentions.

3.8 Integration of Cognitive and Emotion Factors in Decision-Making

Several researchers suggest that emotion is inherent in cognitive appraisals (e.g.,

Gray, 2004; Kiviniemi, Voss-Humke, & Seifert, 2007; Naqvi, Shiv, & Bechara, 2006;

Sanfey, 2007; Shiv & Fedorikhin, 1999; Slovic et al., 2004). Affect laden subjects (such as

cancer screening) may be even more prone to generating judgements that are strongly

driven by the way that one feels about the event, rather than by cognitive appraisal of the

cost and benefit of the decision. Based on widespread empirical support, emotion and

cognition are truly integrated (Gray, 2004), and it is therefore reasonable to expect that

certain emotions extend beyond an experiential state, and are instead cognitive-based (e.g.,

embarrassment) and closely tied in with systematic cognitive processes and thoughts. Such

emotions may engage higher level cognitive processing to produce complex feelings that

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require cognitive input, such as certain types of fear (e.g., fear of the screening procedure

and its side effects require a cognitive appraisal of risks, the efficacy of tests, and so on).

The effect of particular cognitions and social factors on screening intention may

also act to abate the impact of negative emotions. For example, higher levels of self-

efficacy, risk perception, social norms and social support have all been found to increase

intentions to screen for bowel cancer (Friedman et al., 2004; Palmer et al., 2007; Smith-

McLallen & Fishbein, 2008; Honda & Kagawa-Singer, 2006), and may act as covariates or

mediators of negative emotions, reducing the damaging effect of heightened fear,

embarrassment or disgust associated with screening. Conversely, it is also possible that

negative emotions have a dominant influence on screening intentions, inhibiting the

positive impact of systematic processing and cognition. As these hypotheses have not been

explored to date, it is difficult to speculate which factors will prevail in accounting for the

variance in CRC screening intentions. Based on recent empirical works (e.g., Honda &

Gorin, 2005), the hypotheses that emotion-based barriers to screening (e.g., fear) can be

explained by improving certain cognitions and social factors will therefore be explored in

the present research.

3.9 Conclusion and Chapter Summary

A reason-based choice approach in decision-making confers various benefits by

illuminating the input of emotions in a range of decision-making settings where people are

wont to make poor decisions. The affective and emotive elements of decision-making are

thought to be involuntary and instinctive processes (Bechara et al., 2000; Damasio, 1994),

accessed more quickly than cognitive material (Zajonc, 1980), and therefore likely to be

automatically and intuitively influencing judgements and subsequent decisions.

However, individuals often fall short of understanding that their emotional states

will differ between decision-formation and outcome experience (Hsee & Hastie, 2006).

Therefore, people may not be taking into consideration the positive feelings that may occur

during or after medical screening, such as feeling relieved, capable, content, proud, and so

on. Instead, anticipation and prediction of negative affect can guide behavioural responses

toward avoidance or rejection of screening.

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There is increasing research interest to delineate discrete emotion effects on

assorted health behaviours. For example, embarrassment has been reported in qualitative

research as a barrier for all types of CRC screening (Rawl, Menon, Champion, Foster, &

Sugg Skinner, 2000), as has fear (Consedine et al., 2004; Smith et al., 2005), which is

unsurprising given that cancer is more feared in Australia than heart disease and AIDS

combined (Borland, Donaghue, & Hill, 1994). Disgust is widely thought to play a role in

CRC screening decisions, however it has not been readily included in quantitative studies

investigating screening uptake factors, as such there is only anecdotal qualitative evidence

that it impairs decisions to screen for CRC. Each of these discrete emotions may be further

sub-divided to indicate the particular source (e.g., fear of procedural aspects, fear of cancer

diagnosis, core-related disgust, bodily embarrassment) that most influences the screening

decision, and the emotion variables under investigation in the present research are

presented and defined in Table 3.1.

Emotion may therefore be a significant impediment even when there is substantial

cognizance about the value of screening, potentially directing one toward maladaptive

decisions to avoid medical tests of a sensitive nature. The advantages of a discrete emotions

approach to understanding specific emotions and their influence on health decisions are

diverse. Consedine and Moskowitz (2007) have proposed that this approach can steer

research toward an understanding of the mechanisms and design of an emotion via a

systematic manner.

Despite an evident role for emotion in cancer screening, there have been few

structured or organized approaches to assessing its contribution. Any comprehensive

explanation of decision-making must address the role of emotive input during the decision

process. Leading decision and behaviour theorists have endorsed the importance of emotion

in decision-making over the last 20 years (Damasio, 1994; Loewenstein et al., 2001), but

there have also been significant allusions to the integral function of emotion in decisions as

far back as the 1970s (see Janis & Mann, 1977; Zajonc, 1980). In general, however,

research into emotive influences in decision-making prior to the 1980s was acutely quiet.

The area is now experiencing a revival across diverse domains in the decision-making

literature (Loewenstein et al., 2001; Mellers & McGraw 2001; Mellers, Schwartz, & Ritov,

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1999), including health behaviour (Consedine & Moskowitz, 2007; Dillard & Nabi, 2006;

Magai et al., 2007).

In summary, complex decision-making is contingent upon guidance from somatic

states, types of emotional triggers, and the emotional biases formed as a result of

experience and learning (Bechara et al., 2000). The relevance and magnitude of emotion in

decision-making remains unclear, particularly in a health context, but its presence in the

decision process is indisputable, and emotion therefore deserves to receive serious

concentration in the analysis of health behaviour, specifically exploring more closely the

role of discrete emotion influences in cancer screening decisions.

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Table 3.2

Discrete Emotion Variables and the Definition Applied in the Present Investigation

Emotion Variable Definition in present research

Medical embarrassment

(bodily embarrassment and

judgement concern)a

Embarrassment in relation to health or medical experiences, generally in

connection with body awkwardness or with negative social judgement, and

which may influence an individual’s willingness to engage in health behaviour.

Fear of screeningb

Fear related to the screening procedure (e.g., pain, complications), being

diagnosed with cancer, or with experiencing future shame or embarrassment.

Disgust (in relation to CRC

screening)c

An aversion to the real or imagined contagion of body products that may arise

in contemplation of or during screening for CRC. Elicitors of disgust relate to

hygiene, reminders of mortality and animal-origins, and body envelope

violations in connection with potential screening procedures.

Note. aMedical Embarrassment Questionnaire (MEQ) (Consedine, Krivoshekova, & Harris, 2007).

bTypes of fear identified in reviews conducted by Smith, Pope, and Botha (2005) and Consedine, Magai, Krivoshekova, Ryzewicz, and Neugut

(2005). cDisgust Scale-Revised (DS-R) (Haidt et al., 1994, modified by Olatunji, Williams et al., (2007).

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CHAPTER 4

SOCIAL NORMS, SOCIAL SUPPORT, AND THEIR INFLUENCE ON

COLORECTAL CANCER SCREENING


Social factors are documented to impart considerable influence on health behaviour

(Bennett & Murphy, 1997), and while they are rarely the main focus of investigations into

the psychosocial processes of cancer screening decisions, they regularly feature in studies

as significant adjuncts to the major cognitive predictors of screening intention. They have

also been important and even essential to many of the most regularly applied behavioural

decision-making models founded on social-cognitive approaches (e.g., the theory of

reasoned action; Ajzen & Fishbein, 1980). Chapters 2 and 3 described the major cognitive

and emotive elements of the bowel cancer screening decision within the framework of the

present thesis, however many of these factors have been found to correlate and interact with

a person’s perception of their social environment. In this chapter, the literature on social

factors and their influence on CRC screening intentions will be reviewed, with a focus on

subjective norms and functional social support.

4.2 Social Norms

Social norms are generated by significant others within a social network, and are

considered by many researchers to be a direct antecedent to intention (Fishbein, 2008).

Social norms have been implicated in a number of preventive health behaviours including

participation in healthy eating programs (Glanz, Kristal, Tilley, & Hirst, 1998),

mammography attendance (Tiro, Diamond et al., 2005), cervical screening (Duffett-Leger

et al., 2008), and prostate cancer screening (Smith-McLallen & Fishbein, 2008). Yet there

has also been debate as to their importance in predicting health behaviour, with only

modest agreement that they offer significant value in the understanding of health intentions

and behaviour. A number of researchers have expressed misgivings about the ability of

social norms to explain any unique variance in health behaviour intentions (Norman &

Hoyle, 2004). In a meta-analysis by Armitage and Conner (2001) subjective norm was

reported as a weak predictor of intention across a range of health behaviours and intentions

when compared with attitude and perceived behavioural control (PBC) however, the

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authors also noted that there is deficient conceptualisation of social norms and inadequate

measurement across the literature (e.g., by single-item instruments). There is still

usefulness therefore, in further empirical measurement of some of the more recent, refined

theoretical conceptualisations.

4.2.1 Classification and measurement of norms

Normative beliefs about appropriate behaviour are argued to be inherently involved

in behavioural intentions in a process that is parallel to attitudes (Ajzen & Fishbein, 1980).

As part of the Theory of Reasoned Action (TRA), Bennett and Murphy (1997, p. 34) define

social norms as: “an appraisal of the likelihood that salient others (typically friends and

family) would wish the individual to engage (or not) in the behaviour under consideration,

and the motivation to comply with these expectations”. This definition has since been

expanded to include what is purported to be as equally robust as family/friend influence is

on health behaviour: the normative influence of one’s General Practitioner (GP) (Manne et

al., 2002; Tiro, Vernon et al., 2005). Since Armitage and Conner’s review (2001), Tiro,

Diamond et al. (2005) developed and validated a subjective social norm scale in the context

of breast cancer screening, incorporating GP influence.

Sometimes referred to as “social influence” (e.g., Myers et al., 1994; Tiro, Vernon

et al., 2005; Vernon, Myers, & Tilley, 1997), social norms can be further demarcated into

two types: normative social influence and informational social influence, which reflect

conforming to positive expectations of others, or accepting information from others as

evidence about reality, respectively (Deutsch & Gerard, 1955). The first type of norm

(normative social influence), has been more recently explained as ‘injunctive norms’ or

‘subjective norms’; and defined as rules or beliefs about morally approved and disapproved

conduct, coupled with motivation to comply with those beliefs (Cialdini, Reno, & Kallgren,

1990; Rivis & Sheeran, 2003a). Similarly, current explorations of ‘descriptive norms’ are

analogous to informational social influence, and concern behaviour that is deemed to be

standard or ‘normal’ behaviour in a particular circumstance (Cialdini et al., 1990).

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4.2.2 Social influence and health behaviour

A number of authors have agreed that social norms have varying manifestations

when exerting influence on behavioural intentions (Terry, Hogg, & White, 1999), and

social norm is almost undoubtedly a multi-dimensional construct (Sheeran & Orbell, 1999).

While descriptive norms are a means of making rapid decisions about which behaviour is

acceptable within certain situations or conditions (Cialdini et al., 1990), acting as a

benchmark for appropriate behaviour, they offer a different source of motivation from the

subjective norm (Deutsch & Gerard, 1955). This distinct motivational source has been

proposed to explain some of the discrepant findings that have resulted from studies

exploring general normative influence on health behaviour intentions (Rivis & Sheeran,

2003b). Descriptive norms denote certain behaviours are motivated more by the behaviour

of significant others, than by a desire for the approval of significant others. While both

descriptive and subjective norms are often intertwined in normative beliefs and behaviour

(Deutsch & Gerard, 1955), descriptive norms have frequently been the focus of health-risk

behaviour research on the basis they are more important norms for explaining what are

potentially exciting and enjoyable risky behaviours, such as smoking and drinking

(McMillan & Conner, 2003a; Rivis & Sheeran, 2003a). McMillan and Conner (2003b)

extended the Theory of Planned Behaviour (TPB) to include descriptive norms in addition

to subjective norms in an investigation of student intentions to use illicit drugs, and found

that the addition of descriptive and moral norms (norms relating to moral or ethical

behaviour) in a regression model additionally explained between 3% to 10% of variance in

intention to use lysergic acid diethylamide (LSD), amphetamine, cannabis and ecstasy. In

comparison, preventive health and health-promoting behaviours such as eating fruits and

vegetables, exercising, or engaging in screening behaviours may be less likely to be

emulated than risky behaviour (Rivis & Sheeran, 2003a), and instead more sensitive to the

influence of subjective norms.

Subjective norms (what an individual believes they ought to do based on social

norms) have been found to be a more salient normative influence in studies of preventive

health behaviour where both types of norms have been explored and contrasted (Cialdini et

al., 1990), including studies specific to cancer-related behaviours (Smith-McLallen &

Fishbein, 2008). As subjective norms are argued to have an effect on behaviour by

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impacting health behaviour intentions (Rivis & Sheeran, 2003b), subjective norms will be

examined pertaining to CRC screening intention in the current thesis.

4.2.3 Subjective norms and CRC screening

Several studies, particularly those incorporating the conceptual framework of Social

Cognitive Theory, the Health Belief Model, or the Theory of Planned Behaviour (or TRA),

have measured subjective norms in the prediction of preventive health behaviour. These

studies have generally found subjective norms to be an important influence on bowel

screening intention and participation. According to subjective norms, beliefs about

screening (e.g., “my friends/family/GP think that I should participate in the bowel

screening test”), together with incentive to conform to such beliefs (“I am encouraged to

screen because my friends/family/GP want me to screen”), will increase an individual’s

intention to undertake the test.

Given that subjective norms are measured more often than descriptive norms in

cancer- related behaviours (usually via applications of the TRA/TPB), Smith-McLallen and

Fishbein (2008) compared subjective and descriptive norms as predictors of intention to

engage in six cancer-related behaviours, including colonoscopy, mammography, and the

Prostate Specific Antigen (PSA) test. Online questionnaires were administered to 1753

participants (847 male, 849 female), and cancer screening intention was assessed by a

single item (depending on past screening participation). Although both types of norms

significantly correlated with intention to screen for CRC, the strongest normative predictor

of screening intention was subjective norm, consistent with a surfeit of recent research on

cancer screening intentions (Kim, Park, Hong, Lee, & Kim, 2010; Palmer et al., 2007;

Sieverding et al., 2010) and adherence (Honda & Kagawa-Singer, 2006). Interestingly

Smith-McLallen and Fishbein found that the combination of descriptive norms and

subjective norms (labelled ‘perceived normative pressure’) slightly decreased the variance

in colonoscopy intentions accounted for by the model, compared to the influence of

subjective norm on its own.

A recent study investigated the function of social (subjective) norms as part of an

extension of the TPB in the intentions and subsequent uptake of CRC screening amongst

2,426 men (Sieverding et al., 2010). In the first study examining screening intention, social

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norms contributed an additional 3% of variance to that explained by traditional TPB

variables (49%). The authors also found that when subjective norm was low, descriptive

norms became more important in predicting intentions to screen, possibly because the

influence of normative peer behaviour (descriptive norms) was enhanced by not having

subjective exposure to family or partner influence, or fewer negative attitudes toward

screening. A second study provided further evidence for the role of social norms in the

participants’ subsequent screening participation, where those men who had previously

reported high subjective norms were more likely to have attended screening than those who

had initially experienced low subjective norm.

Honda and Kagawa-Singer (2006) argue the explanatory power of social norms

over and above attitudinal factors in CRC screening. They were principally interested in

which social relationships and normative influences were most important in both

sociocentric cultures as well as individualistic ones. As the greater part of English-language

cancer screening research has been conducted in samples from developed western nations,

the authors examined the influence of norms on colorectal screening in a sample of 341

older Japanese-Americans, a culture understood to be sociocentric and highly norm

orientated. Social cognitive theory and the TPB were integrated with social support and

social network concepts to provide the theoretical framework. Employing structural-

equation modelling to test the assumption that normative factors are more important than

attitudinal factors, the authors found that subjective norm about friends and family had a

direct influence on screening adherence, while emotional social support from family had an

indirect effect via subjective norm. Within this population, normative rather than attitudinal

factors appeared to be more important in screening intention. Interestingly, subjective

norms about healthcare providers did not have any impact on screening adherence.

Conventionally, an absence of GP advice, recommendation, or support has been associated

with lower screening uptake (e.g. Brenes, & Paskett, 2000; Busch, 2003; James et al., 2002;

Janz et al., 2003; Madlenskey et al., 2003; Manne et al. 2002; Taylor & Anderson, 2002;

Trauth, Ling, Weissfeld, Schoen, & Hayran, 2003; Vernon, 1997). It may be that a family-

focused culture reduces the reliance on health provider normative influences, perhaps being

less compatible with subjective social norm perspectives in sociocentric cultures grounded

in family-based decision-making and interdependency. The current study will explore the

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role of subjective norms or ‘social influence’ in CRC screening intentions, concordant with

the literature that suggests the opinions and beliefs of friends, family and GP are influential

in the decision to participate in cancer screening.

4.3 Social Support

There is widespread consensus for the value of social support in physical and mental

well-being and disease prevention, increased longevity, and quality of life for chronic

disease populations (Berkman & Syme, 1979; Bowling, 1994; Cobb, 1976; Penwell &

Larkin, 2010; Reblin & Uchino, 2008). Social support is thought to enhance health in a

variety of ways, for example by supporting immune and cardiovascular health (Kamarck,

Manuck, & Jennings, 1990; Larzelere & Jones, 2008), contributing to reduced pain

perception in chronic illnesses (Lackner et al., 2010), and augmenting recovery from

disease and injury (Kroenke, Kubzansky, Schernhammer, Holmes, & Kawachi, 2006).

Social support has also been found to facilitate compliance with medical regimens

(Caltabiano et al., 2002; DiMatteo, 2004) and participation in a variety of preventive health

and health-maintenance behaviours including exercise and increased consumption of fruit

and vegetables (Emmons, Barbeau, Gutheil, Stryker, & Stoddard, 2007), and reducing or

ceasing cigarette intake (Chouinard & Robichaud-Ekstrand, 2007).

4.3.1 Conceptualisation and typologies of social support in health research

4.3.1.1 Conceptualisation of social support. Over the last three decades there has

been growing research attention, as an extension of sociological and social science inquiry,

on the link between social support and physical and mental health (House, 1987). An

increased focus on the hypothesis that social relationships can both contribute to and

promote well-being emerged after the 1950s Joint Commission on Mental Illness and

Health in the US (Ewalt, Schwartz, Appel, Bartemeier, & Schlaifer, 1960, cited in Gottlieb,

1983), where a national survey established that a majority of the public were turning to

family, friends, neighbours, physicians and religious leaders about mental health problems,

instead of seeking more formal support from mental health professionals. While there is

broad evidence for the positive relationship between social support and improved mental

and physical health (Cohen, Underwood, & Gottlieb, 2000), there is less agreement on the

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defining features and measurement of social support. Often referred to interchangeably and

thereby imprecisely as social support, social networks, or social integration (House, 1987),

the opportunity to attribute many of the positive effects of these social relationships to the

effects of social support per se becomes increasingly limited as a result of inconsistent

conceptualisation, labeling, and measurement.

The foremost researchers of social support and its influence on health have defined

social support as “the social resources that persons perceive to be available or that are

actually provided to them by non-professionals in the context of both formal support groups

and informal helping relationships” (Cohen et al., 2000, p. 4). Cohen and colleagues also

call attention to the unintentional nature of social support, which is embedded in the natural

interactions and relationships within the social network, and can produce beneficial health

outcomes through cognitions, emotions, behaviours and biological responses. More

recently, Cohen (2004) defined social support as a “social network’s provision of

psychological and material resources intended to benefit an individual’s ability to cope with

stress” (p. 676), where the resources can be instrumental, informational, and emotional

(House & Kahn, 1985), as well as esteem-building and network/companionship based

(Cutrona, Suhr, & MacFarlane, 1990). House (1987) advises differentiation is necessary in

the understanding of social relationships and their positive effects, and has separated three

unique but related facets: the existence and quantity (social integration); the formal

structure (social networks); and the functional or behavioural content (which reflects social

support specifically). The functional or behavioural component is the indicator of social

support under investigation in the present thesis, reflecting the advice, and affective and

personal resources (e.g., accompaniment to a colonoscopy appointment) that an individual

perceives are available to them through their social network.

4.3.1.2 Typologies of social support. Several researchers have proposed

classification schemes for different aspects of social support. Earlier attempts to categorize

these typologies produced large numbers of categories (e.g., 26 categories by Gottlieb,

1978; and 28 by Levy, 1979; cited in Cohen et al., 2000), however more recently, and by

general consensus in the literature, they have been reduced to five main types: emotional,

informational, tangible/instrumental, esteem-building, and network/companionship.

Emotive support is the reassurance that one is cared for and understood (Kinney, Bloor,

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Martin, & Sandler, 2005), and has been shown to be especially significant in assisting a

person’s adjustment to cancer diagnosis, signifying both beneficial effects through

empathizing, reassurance and comfort (Helgeson & Cohen, 1996), but also detrimental

effects, such as increased psychological distress (De Leeuw et al., 2000). Informational

support is the provision of information, advice and feedback about one’s objectives and

performance, and can similarly be advantageous or unhelpful for cancer patients when

provided by health care professionals or by family and friends, respectively (Helgeson &

Cohen, 1996). Tangible support is the provision of concrete physical or material aid that

directly supports an individual within the network, while esteem support is the perception

of personal worth; that one is valued and esteemed (Cobb, 1976).

Several researchers postulate the varying outcomes that different types of social

support may produce, such as emotional support bringing about less distress in cancer

patients (Helgeson & Cohen, 1996), or informational or tangible support exerting positive

effects on participation in healthy behaviours and lifestyles (Ren, Skinner, Lee, & Kazis,

1999), and cancer survival (Kroenke et al., 2006). The emotive, informational, tangible, and

esteem-building elements of social support are encompassed in the measure of social

support utilized in the current investigation of CRC screening.

4.3.2 Mechanisms of social support on health outcomes

Different interpretations of social support as a social-psychological construct have

been common, and part of the definitional incongruity may stem from an incomplete

understanding of the mechanisms underlying social support, and how it comes to directly

and indirectly influence health behaviour and health outcomes (DiMatteo, 2004). One of

the key theories put forth to account for the underlying beneficial processes of social

support on health is the stress-buffering hypothesis.

Stress buffering was presented as the basis of early models in accounting for the

role of social support in health maintenance and improvement, arising from the notion that

social support acts as a moderator of psychological stress (Gottleib, 1983). Several

researchers have supported the stress-buffering approach (e.g., Dowd & Goldman, 2006;

Cassel, 1974; Cobb, 1976; Cohen, 1988), which is argued to be one of the primary paths

via which social support imparts protective health effects, by cushioning the individual

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from harmful psychological and physiological outcomes from stressful life events (Cobb,

1976; Cohen, 2004). Correspondingly, recent studies also suggest that loneliness and a

lack of social support may itself be a form of stress that can contribute to poor health

(Aanes, Mittelmark, & Hetland, 2010; Cacioppo, Hawkley, & Thisted, 2010; Segrin &

Passalacqua, 2010). Furthermore, one study reveals that the reciprocal relationship in social

support (both giving and receiving support) within a social network had a direct effect on

stress and its physiological symptoms, reducing blood pressure and arterial pressure (Piferi

& Lawler, 2006).

The stress buffering approach predicts that this mechanism of social support’s

influence on health is of particular importance for individuals experiencing hardship, and

will be less important when there are few burdens on the individual (Cohen, 2004). A

review by Uchino, Cacioppo and Kiecold-Glaser (1996) indicated that the association

between social support and health has other potential causal mechanisms other than stress

buffering, including more direct effects through familial and interpersonal relationships,

leading to positive affective states and health-enhancing behavioural choices.

Effects of social support on physical health through lifestyle and health promoting

behaviours was often previously considered a confounding aspect of the measurement of

social support on health (Reblin & Uchino, 2008), however it can be seen as part of the

causal mechanism (Connell, Davis, Gallant, & Sharpe, 1994). This line of argument

suggests that independent of any acute stressor, and through social reinforcement and

positive events and experiences, social support may directly encourage long-term health-

promoting behaviours and physiological responses that are beneficial for overall physical

and mental health, while a lack of support can consequently hinder participation in health

behaviours (Reblin & Uchino, 2008). Although functional aspects of social support have

been shown to have both direct effects and stress-buffering effects on health outcomes, the

behaviour change and lifestyle pathway provides the framework of social support that is the

focus of the present examination, whereby intentions and participation in CRC screening

are argued to be greater when there are higher levels of perceived functional social support.

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4.3.3 Functional social support effects on CRC screening

There is evidence that adverse health outcomes arise when there is a lack of social

support (Kroenke et al., 2006; Sultan et al., 2004). Gili and colleagues (2006) found that

social support from others (such as friends, colleagues and health professionals) was

positively associated with screening, but also that the different types of functional social

support (emotive and tangible, as well as a measure of general social support and

satisfaction with support) all predicted higher acceptance of CRC screening. Additionally,

perceptions of greater tangible support predicted adherence to CRC screening.

Unsurprisingly given the recent growth in the delineation of types of social support, a

global measure of general support only approached significance, in comparison to the

clearer influence of different types of functional support, providers of social support (e.g.,

friends, colleagues), and satisfaction with support.

The positive predictive role of emotional support was also reported by Honda and

Kagawa-Singer (2006), who found that within a structural model of bowel screening

adherence, emotional family support was indirectly increasing CRC screening adherence

via greater subjective norms, while emotional support from friends had a direct influence

on adherence. Therefore it may be inferred that an increase in intention and participation in

cancer screening is one of the constructive health outcomes of functional social support.

These findings are consistent with a meta-analysis conducted by DiMatteo (2004),

where 122 published empirical journal articles were found dating from 1948 to 2001 that

correlated structural or functional social support with adherence to medical regimens.

Adherence was defined as patient agreement and follow-through with advised healthcare

from a medical professional, while functional support was identified in the literature by

tangible, emotional or undifferentiated measures of social support. There were 29 studies

measuring tangible, practical support on adherence, where correlations ranged from -.22 to

.75, with strong overall positive correlations between practical support and adherence. The

odds of adherence was 3.6 times greater among those receiving practical support, with the

authors reporting that there would need to be over 1,800 unretrieved, unpublished, or

otherwise un-located studies showing no effect for their results in the meta-analysis to be

non-significant. Emotional support also showed an overall positive effect across the 11

retrieved studies, with r effect sizes ranging from 0.0 to .37, and the risk of nonadherence

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being 1.35 times greater for patients without emotional support. Undifferentiated support

was found in 27 studies, comprising instruments that combined the different types and

dimensions of support into a unidimensional measure. Amongst these studies, the odds of

adherence to the regime was found to be 2.35 times greater with higher levels of support,

while risk of nonadherence was 1.53 times higher amongst patients with low support. In

view of the diverse treatment regimens, disease, patient demographics and measurement

strategies across the studies included in the meta-analysis, the authors point out that the

general reliability of social support effects on adherence are notable. Furthermore, they

argue that adherence may be another important pathway between social support and

positive health outcomes. These findings suggest direct implications of functional social

support for CRC screening adherence, as well as probable implications for first-time

acceptance of CRC screening recommendations.

4.4 Chapter Summary

This chapter has presented a review of the literature on social norms and social

support, and their respective effects on health intention and behaviour. Throughout this

review it has been proposed that these social factors have important effects on intention to

engage in cancer screening, despite their discrepant history in predicting health behaviour

and their complex and ambiguous conceptual background. Relatively recent delineations of

the typologies of both social norms and social support have facilitated more precise

operationalisation of social constructs in health research, and as such may enable a clearer

understanding of the unique predictive abilities of each.

Compared with descriptive norms, subjective norms have recently been more

clearly (although often only moderately) associated with intentions to engage in cancer-

screening behaviours, including colonoscopy, mammogram, and prostate screening (Allen,

Stoddard, & Sorensen, 2008; Tolma, Reininger, Ureda, & Evans, 2003). The guidelines set

by a social network’s subjective norms about what one ought to do can be perpetuated

through friends, family, and particularly for screening behaviours, through one’s GP (Tiro,

Vernon et al., 2005). The present investigation aims to establish the influence of subjective

social norms specifically on CRC screening, and its relationship with other cognitive and

emotive variables that are posited to directly affect screening intentions.

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Functional support (emotive, tangible, informational, and esteem-building) from

friends and family can also promote healthy behaviours, including participating in cancer

screening. The influence of functional support may occur in a direct way by increasing self-

esteem, self-efficacy, acceptance of the behaviour, and motivation directly via positive

experiences. The direct effect hypothesis implies that the availability of functional social

support creates a social environment that directly supports healthy behaviours and

lifestyles, however, stress-buffering (reducing the impact of harmful mental and

physiological responses to stressful events) may be another route by which social support

brings about positive health outcomes.

Varying conceptualisation and measurement of social factors create several

difficulties in comprehensively reviewing the literature. As such, an understanding of the

influence of social support on health behaviour has been obscured by the use of either non-

specific types of social support, or by single-indicator measures that limit the reliability and

generalisability of the findings. Various studies have indicated positive, negative and no

association for social support in preventive health behaviour (Gili et al., 2006), however in

research specific to preventive cancer behaviours there is evidence for a positive

relationship with cancer screening (Gotay & Wilson, 1998; Seow, Huang, & Straughan,

2000; Smith-McCallen & Fishbein, 2008). Generally, functional social support is thought

to have a moderate protective and promoting role. For the sake of parsimony in the present

thesis, the more generic labels of social norms and social support will be used.

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Table 4.1

Social Variables, their Theoretical Origin, and the Definition Applied in the Present Investigation

Social variable Theoretical origin Definition in present research

Social norms

(subjective norm)

TRA/TPB, MTM Subjective social norms reflect an individual’s motivation to screen for CRC

based on a desire for approval from significant social others (family, friends,

GP).

Social support

(functional

support)

SCT

Functional support reflects the advice, and emotional and personal resources

(e.g., having accompaniment to a colonoscopy appointment) perceived to be

available.

Note. TRA = Theory of Reasoned Action / TPB = Theory of Planned Behaviour; MTM = “Major Theorists” model; SCT = Social cognitive model

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CHAPTER 5

THESIS OUTLINE, AIMS, AND HYPOTHESES OF STUDY 1


Critical reviews of the empirical and theoretical literature on demographic, health,

cognition, emotion and social factors involved in CRC screening intention and behaviour

were presented in Chapters 2, 3 and 4. This chapter presents the rationale and general thesis

aims (Section 5.2), followed by a description of key constructs (Section 5.3) and the aims,

rationale, and hypotheses of Study 1 (Section 5.4).

5.2 Rationale and Overall Aim of the Present Thesis

The goal in medical settings is to provide patients with accurate and comprehensive

information about the targeted medical condition, medical test, or treatment options, in

order to ensure the patient can make informed decisions regarding their own well-being and

treatment. Communication between health care providers and their patients may therefore

benefit from further understanding of the cognitive biases and emotional processes that are

elicited in decisions about cancer screening.

Across two independent but related studies, this dissertation investigates a

combination of variables from judgement and decision theory, discrete emotions literature,

major health behaviour models, and fixed factors (see Table 5.1) in an attempt to predict

their relationships with CRC screening intention. This research seeks to identify specific

psychosocial indicators of screening intention, the role of screening bias, to unpack the

effects of specific emotions on screening, and to distinguish whether some of these

relationships are partially explained by related variables. The overarching aim is to

illuminate which factors under investigation can optimally predict CRC intenders and

screeners.

This prompts the research question about the ability of discrete emotion and

screening bias to contribute substantially to the variance in screening intention and action,

potentially over and above conventional social-cognitive factors. Heuristics and negative

emotion present two salient but under-explored areas of investigation for explaining poor

community responses and low uptake of CRC screening programs that have been trialed or

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implemented around the world, and these potentially significant CRC screening predictors

have the capacity to add to the literature explaining screening reluctance.

Two studies are presented testing these variables and their relationships with CRC

screening and decisional conflict. The first study was performed to pilot a large number of

these potential predictors for use in a second, follow-up study, and to ensure the

psychometric soundness of a range of scales only recently developed or with limited

application in the empirical literature. For these reasons, it was appropriate to recruit a

convenience sample large enough for the purposes of confirmatory factor analysis.

Therefore the goals of the pilot study (Study 1) were more statistically driven and

exploratory in nature. Study 2 was designed to examine the relative roles of these variables

in a targeted CRC screening demographic. As a follow-up study, the purpose of Study 2 is

to assess the refined instruments in a shorter survey and explore the ability of the variables

to predict screeners and intenders, within a broader, older sample of Australians drawn

from the general community. The variables and hypotheses of Study 1 are outlined below,

while Study 2 is described in Chapters 8 to 10.

5.3 Key Constructs of the Present Investigation

5.3.1 Summary of independent variables

The key constructs under investigation in the present examination of CRC screening

include some of the most studied (but not necessarily well-understood) constructs that have

been aspects of several major health behaviour models to date, along with fixed factors

such as demographic and health status. These include risk perception, self-efficacy

(similarly conceptualised as perceived behavioural control in the TPB), test-efficacy

(beliefs about the accuracy and efficacy of the health exam), cancer knowledge, and cancer

worry (see literature review, Section 2.2 Chapter 2). Two social factors are also included:

social norms; and social support (see literature review, Sections 4.2 to 4.3, Chapter 4).

In addition to these frequently measured constructs, a number of variables of

growing importance in the understanding of health behaviour decisions will be investigated.

The representativeness heuristic, a form of experiential cognitive processing, is manifested

as ‘screening bias’ in relation to CRC screening (see literature review in Sections 2.3 to 2.4,

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Chapter 2) while three discrete negative emotions will be explored in terms of their various

sources or targets in relation to CRC screening. These include medical embarrassment

(bodily embarrassment and judgement concern), fear (fear of procedural aspects, fear of

cancer, and fear of future shame or embarrassment), and disgust (core, contamination, and

animal-reminder) (see Section 3.7, Chapter 3 for a review of the literature).

5.3.2 Summary of dependent variables

The independent variables will be examined as correlates and predictors of one

primary and one secondary dependent variable in Study 1: CRC Screening Intention and

Decisional Conflict, respectively. They will be investigated in Study 2 in relation to an

additional dependent variable, CRC Screening Participation. Examination of this additional

dependent variable was possible in Study 2 because the older sample allowed for greater

variance on this behaviour than the relatively young sample accessed in the pilot study. The

independent and dependent constructs are set out in Table 5.1.

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Table 5.1.

Factors Under Examination in the Present Investigation of Cognitive, Emotion, and Social

Predictors of Colorectal Cancer Screening

Variable Literature Review

Health history

History of gastrointestinal illness; CRC and general screening

history; and family history of CRC and gastrointestinal illness

Chapter 1:

Section 1.3.1

Demographics

Age; gender; marital status; socioeconomic status; private health

insurance status

Chapter 1:

Sections 1.3.2 –

1.3.3

Dependent variables

Screening intention, screening behaviour; Decisional conflict

Chapter 1:

Section 1.4

Cognitions

Systematic cognitions: risk perception; knowledge of CRC; cancer

worry; beliefs about the effectiveness of tests (test-efficacy); and self-

efficacy to participate in screening

Screening bias (representativeness bias of illness categories)

Chapter 2:

Sections 2.2.1 –

2.2.5

Sections 2.3 – 2.5

Emotions

Medical embarrassment (bodily embarrassment, judgement concern)

Fear (procedural aspects, cancer, embarrassment)

Disgust (core, contamination, animal-reminder)

Chapter 3:

Sections 3.7.1 –

3.7.3

Social and relational factors

Subjective norms of family, friends, and health provider

Social (functional) support from family and friends

Chapter 4:

Section 4.2

Section 4.3

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5.4 Study 1: Exploring emotive, cognitive and social variables in colorectal cancer

screening intention in a convenience sample

Study 1 comprised a survey of intentions to screen for colorectal cancer and the potential

predictors of this intention. As previously stated, a wide range of potential correlates was

canvassed across three umbrella categories, including cognitions (together with screening

bias), emotions, and social factors. The relationships between screening intentions and

demographic/ health variables were also examined.

5.4.1 Aims and rationale of Study 1

Based on the literature review, the main aim of Study 1 was to investigate the

magnitude of effect from three major variable categories of cognitions, emotions and social

factors in an effort to validate the theoretical relationships of a range of measures that will

subsequently be used in a community-based sample. Therefore, a methodological aim of

the study was to establish the psychometric properties of the instruments used to

operationalise the variables in these categories. Three further aims constituted the

theoretical and exploratory purposes of the study: a) to establish the strength and relevance

of a range of emotive considerations in decisions to screen for CRC – specifically levels of

disgust, medical embarrassment, and fear; b) to confirm the relationships of different

systematic cognitions (risk perception, self-efficacy, test-efficacy, knowledge, worry) with

screening intention and their relative strength in comparison with the proposed relationship

between an experiential cognition (screening bias) and intention to screen for CRC; and c)

to corroborate the role of social influences (subjective norms and social support) in

screening decisions. It was also important to investigate fixed factors as they are understood

to be related to screening intention. An important overall goal of the present study was to

identify key variables, and validate their relationships with CRC screening decisions for

further analysis in a second and refined community-based study.

The study was designed to pilot the selected instruments (6 cognitive; 4 emotive;

and 2 social), using a cross-sectional methodology with a convenience sample of

predominantly university students and staff. A convenience sample was selected for reasons

of practicality given the necessarily limited scope of this preliminary study, and was

recruited largely through a university-based research experience program for students.

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Various avenues identified for accessing a community sample were therefore reserved for

the follow-up study, for which it was planned that measures would be refined and the

survey pared down to a length more acceptable for community engagement.

Given the strong likelihood that a convenience sample would consist largely of

younger students (due to recruitment procedures), the study was designed so that

participants were asked distal intentions with respect to CRC screening (that is, to imagine

they were older, or aged 40 or more), a technique used in a number of studies to measure

decision preferences for a range of health-related scenarios (e.g., Fagerlin, Wang, & Ubel,

2005; Sudore, Schillinger, Knight, & Fried, 2010; Zikmund-Fisher, Lacey, & Fagerlin,

2008). While distal intentions can be more idealistic (Kivetz & Tyler, 2007), the chief aim

of this study was to validate the anticipated theoretical relationships and measures prior to

testing these variables in an age-targeted community sample. This age of 40 years (or older)

was selected as a suitable age for imagining CRC screening intentions for two reasons: 1)

screening guidelines recommend those at higher risk for CRC, for example because of a

family history of CRC or a medical history of polyps, commence screening from age 40;

and 2) to make it easier for students to imagine themselves being older, the minimum age at

which screening could commence for higher-risk individuals was considered more fitting

than age 50 (the age for screening commencement for individuals at average risk).

5.4.2. Study 1 hypotheses

Based on the empirical and theoretical review of the literature, and in order to

validate the measures prior to their application in an age-targeted sample, hypotheses were

formulated regarding the directional relationships of demographic variables, health history,

emotions, cognitions, and social factors with the dependent variables: screening intention

(first dependent variable) and decisional conflict (second dependent variable). To address

the aims and research questions of the study, the hypotheses are outlined below. Prior to

testing these hypotheses, four precautionary hypotheses were tested to ensure a preliminary

relationship existed between screening intention and decisional conflict; to establish that

the disparate sample sizes between men and women and the broad age ranges could each be

collapsed prior to further analysis of the sample as a whole; and to establish

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unidimensionality for screening bias (from cancer fatalism). These preliminary hypotheses

and their results are presented in full in Appendix E.

Hypothesis 1: The relationships between demographic and health variables with CRC

screening intention and decisional conflict

It was predicted that CRC screening intentions will be stronger and decisional conflict

weaker among those who 1(a) are older; 1(b) are partnered; 1(c) have private health

insurance; 1(d) are male; 1(e) have a stronger history of general screening; 1(f) have a

stronger history of CRC screening; 1(g) have a history of gastrointestinal illness; and 1(h)

have a family history of CRC and gastrointestinal illness.

These hypotheses were formed on the basis of the literature reviewed in Sections

1.3.2 to 1.3.3 (hypotheses 1a to 1d), and Section 1.3.1 (hypotheses 1e to 1h), Chapter 1.

Hypothesis 1 will be tested with Pearson product-moment correlations and t-tests.

Hypothesis 2: The relationships between cognitive variables with CRC screening

intention and decisional conflict

Given the literature summarised in Chapter 2, which suggests that a range of systematic

cognitions can positively influence intentions to screen for CRC (Section 2.2.1), and that

heuristics may negatively impact screening intentions (Section 2.4), it was hypothesised

that screening intentions will be stronger and decisional conflict weaker among those with

greater: 2(a) perceived risk of CRC (risk perception); 2(b) knowledge of CRC; 2(c) beliefs

about the efficacy of CRC screening (test-efficacy); 2(d) beliefs about their own ability to

effectively undertake screening (self-efficacy); and 2(e) worry about CRC (cancer worry).

In addition, 2(f) risk perception is predicted to strongly and positively correlate with cancer

worry, strengthening the relationship between worry and intention, and this is based on

strong evidence that higher risk perception is associated with cancer worry, which may

motivate screening intentions (in order to reduce worry and perceptions of vulnerability

(Wardle et al., 2000).

It was also predicted that having more distorted beliefs about the necessity and

value of CRC screening according to lay representations of the causes of CRC (i.e.,

screening biases) will be strongly and negatively correlated with screening intentions, and

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positively with decisional conflict 2(g). This is based on similar observations in the domain

of breast cancer screening (Gerend et al., 2004) and in relation to commonly reported

screening misperceptions (Denberg et al., 2005; Wackerbarth et al., 2005). It was

hypothesised that this relationship is not fully (but may be partly) explained (mediated) by

the relationship between CRC knowledge and screening intention, but will have a unique

relationship with intention (2(h)). Pearson product-moment correlations and partial

correlations will be used to test hypotheses 2(a) to 2(g), and 2(f) and 2(h), respectively.

Hypothesis 3: The relationships between emotion variables with CRC screening


On the basis of the literature reviewed in Chapter 3, indicating that CRC intentions

are likely to be influenced negatively by a range of emotions (Section 3.6), it was predicted

that CRC screening intention will be weaker and decisional conflict stronger among those

with higher levels of 3(a) disgust (including each subtype of disgust, being core,

contamination, animal reminder, and medical-related disgust); 3(b) medical embarrassment

(including each subtype, being bodily embarrassment and judgement concern); and 3(c)

fear of screening (including three different targets of fear, being fear of cancer, fear of

embarrassment, and fear of procedural aspects). Hypotheses 3(a) to 3(c) will be tested by

Pearson product-moment correlations.

Hypothesis 4: The relationships between social variables with CRC screening


Social (subjective) norms that are supportive of screening, and general functional

support are predicted to positively correlate with screening intention and negatively with

decisional conflict in hypotheses 4(a) and 4(b) respectively. These predictions are based on

the literature reviewed in Sections 4.2 (e.g., Honda & Kagawa-Singer, 2006; Sieverding et

al., 2010) and 4.3 in Chapter 4 (e.g., DiMatteo, 2004; Gili et al., 2006; Honda & Kagawa-

Singer, 2006), respectively, and will be tested by Pearson product-moment correlations.

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Hypothesis 5. The direct and mediational relationships between negative emotions, self-

efficacy, social factors, and screening intention

Mediation analyses in Amos will be conducted to assess the degree to which the

relationships between each of the negative emotions (disgust, fear, and medical

embarrassment) with screening intention will be partially explained by self-efficacy, social

support, and social norms (based on a review in Section 3.7), and these form hypotheses

5(a) in relation to disgust, 5(b) in relation to fear, and 5(c) in relation to medical

embarrassment. That is, higher levels of self-efficacy and social variables are anticipated to

remove or diminish (mediate) the correlation between negative emotion and screening

intention. Hypotheses 5(a) to 5(c) by mediation analysis in Amos.

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Table 5.2

Summary of Study 1 Hypotheses of the Relationships Between Cognitive, Emotion, and

Social Variables with Screening Intention and Decisional Conflict

Variable Screening

Intention

Decisional

Conflict

Risk perception + -

Test-efficacy + -

Self-efficacy + -

Cancer worry + -

Knowledge + -

Screening bias - +

Disgust (core, contamination, animal-reminder, bowel) - +

Embarrassment (judgement concern, interpersonal, bodily) - +

Fear (cancer, embarrassment, procedural aspects) - +

Social norms + -

Social support + -

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Screening Bias

Screening Intention

Cancer Knowledge

-ve +ve

-ve

Screening Intention

Risk

Perception

Cancer Worry

+ve +ve

+ve

5.5 Summary of Partial Correlation and Mediation Hypotheses about Screening

Intentions

Figure 5.1. Expected partial correlation between screening bias, cancer knowledge, and

screening intention.

Figure 5.2. Expected partial correlation between risk perception, worry, and screening

intention.

Figure 5.3. Expected mediation between negative emotions (fear, disgust, and

embarrassment uniquely), self-efficacy, social support, social norms and screening

intention.

Negative Emotions (fear,

disgust, embarrassment)

Screening Intention

Self-Efficacy Social Support Social Norms

-ve

-ve +ve

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CHAPTER 6

STUDY 1: METHODOLOGY AND PSYCHOMETRIC PROPERTIES OF SCALES


The aims of Study 1 were to investigate, within a convenience sample, the potential

correlates of CRC screening intention and decisional conflict within three major categories:

cognitions, emotions, and social factors, after examining the psychometric properties of

instruments. In this chapter, measures of demographic/health variables, emotive variables

(fear, disgust, embarrassment), cognitive variables (self-efficacy, test-efficacy, knowledge,

worry, risk perception, screening bias) and social variables (norms, support) are described

(see Appendix C for the full survey). The first part of this chapter outlines the method of

Study 1 (Section 6.2), while the second part (Section 6.3) presents information on data

screening and preparation for analysis. The reliability coefficient of each scale was

determined by Cronbach’s alpha, and is reported in Table 6.2 in Section 6.3.6 of the present

chapter. A description of the sample characteristics and hypothesis testing follow in

Chapter 7.

6.2 Method

6.2.1 Participants

A sample size of 202 participants with a mean age of 27 years (SD = 11 years) took

part in the survey entitled, Attitudes Toward Screening for Bowel Cancer. The sample

comprised predominantly university students (93%) who participated as part of a research

experience program conducted at Swinburne University of Technology in Melbourne,

Australia, while the remaining 7% of participants were recruited through word of mouth or

acquaintance with the investigator(s). Within the sample, there were 155 (77%) women

who ranged in age from 18 to 75 years (M = 28 years, SD = 12 years) and 47 (23%) men

who ranged from 19 to 56 years (M = 25 years, SD = 9 years). Consistent with the

Australian student population from which the majority of the sample was drawn, there were

154 Anglo-Australians (76% of the sample), and the majority was un-partnered (n = 128,

63%). The only two inclusion criteria for participation in the study were Australian

nationality (by birth or immigration) and being aged 18 or older.

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6.2.2 Materials

6.2.2.1 Demographic and health measures

Demographic measures. Seven standard demographic items requested information

about gender, age, marital, employment, and education status, and ethnic background.

Because CRC screening decisions may be connected to health insurance status (Duncan et

al., 2009), this was also requested.

Except for age, all response formats were categorical. For the purpose of conducting

Pearson correlations, marital status was collapsed into partnered and un-partnered, as was

ethnic background (Australian and Other) and health insurance (insurance, no insurance),

while employment and education status were analysed along a continuum of unemployment

to full-time employment, and not studying at all to full-time study, respectively.

Health measures. A set of 20 items for women (and 19 items for men) was

compiled to assess 10 variables. All items in this section entailed a “Yes”, “No”, and “Not

Sure” categorical response format, except for Peer and Subjective Health Status, which had

a 7-point Likert response format.

Peer Health Status and Subjective Health Status consisted of one item each and are

derived from CRC screening investigations that have assessed health perception (Prescott-

Clarke & Primatesta, 1996; Sutton et al., 2000; Wardle et al., 1999). The first item assessed

perception of average peer physical health (“On average, how would you rate the physical

health of other people in your age group in Australia?”), while the second measured

subjective physical health (“Overall, would you say that for someone of your age, your own

physical health is”). Items were scaled using Likert ratings from 1 = “very poor” to 7 =

“excellent”, and scores were summed on each scale, where higher scores indicate

perception of greater physical health. As reliability coefficients cannot be computed on a

single item, published reliability indicators have not been reported.

Screen Advice and Competing Medical Condition were each assessed by one item,

“”Have you ever been advised by your doctor or another health professional to get screened

for bowel cancer?” (based on an item in Harewood et al., 2002), and “Do you currently

have any serious medical condition?”, respectively. These items were treated as

dichotomous variables (yes/no).

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Gastrointestinal Health comprised 2 items, including questions about Irritable

Bowel Syndrome (IBS) and polyps in the rectum or colon. A third general item asked about

any ‘other’ bowel conditions. These items were collapsed into a dichotomous variable to

indicate gastrointestinal health condition(s) or no gastrointestinal health condition.

History of CRC Screening comprised 5 items relating to digital rectal examination,

barium enema (X-ray), FOBt, flexible sigmoidoscopy, and colonoscopy. The items were

preceded by the sentence, “Have you ever done, or had a doctor perform, any of the

following?”. These items were collapsed into a dichotomous variable reflecting a history of

CRC screening or no history of CRC screening.

History of General Screening (for other cancers) was also assessed for men and

women, including mammography, breast self-examination and cervical screening for

women, and prostate screening and testicular self-examination for men. Responses were

summed to achieve a scale of total screening history. In order to produce a separate men’s

and women’s screening history scale, women’s screening history (3 items) were summed,

divided by three, and then multiplied by two (number of men’s items) in order to be

comparable.

Three variables and seven items assessed Family Health. Response options to these

items included an additional option: “Not Sure”. Family CRC Screening History included

two items following the sentence prefix: “Has anyone in your family”, and included “Ever

been screened for bowel cancer using a stool test” and “Ever been screened for bowel

cancer by colonoscopy”. Three items measured Family Gastrointestinal Health, including

family history of polyps, IBS, and diverticulitis. Finally, Family history of CRC was

assessed by one item, “Please write down how many members of your family (if any) have

had bowel cancer”. For descriptive purposes, participants who responded as having a

family history of CRC were asked to indicate the youngest age at which a family member

was diagnosed with CRC. Each variable (except for age of family CRC diagnosis) was

collapsed into a dichotomous variable, with all of the “not sure” responses coded as “no”.

6.2.2.2 Cognitive Measures

Risk Perception. Risk perception of CRC refers to an individual’s estimate of their

personal level of risk of developing CRC. Robb, Miles, and Wardle (2007) have

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acknowledged there is no gold standard for measuring risk perception, and as such the

items used in the present study were collated from the literature as the items that are most

regularly used and accepted as risk perception measures. It was assessed in the current

study by a three-item scale adapted from two sources.

Two items were derived from Cameron and Diefenbach (2001) (r = .80). Wording

of the original items was altered in the current research to cater to a younger sample by

asking them about their perception of future risk: “the chance that I will get bowel cancer at

some point in my life is”, “the chance that I will get bowel cancer when I am over 50 is”.

The third item, adopted from Kremers, Mesters, Pladet, van den Borne, and Stockbrügger’s

(2000) measure of global risk perception about CRC (α = .73), assesses perception of

general CRC risk and again, this item was altered to suit the anticipated youthful age group

of the predominantly student sample (“the chance that anyone over 50 will get bowel

cancer is”). Risk ratings ranged from 1 = “low chance” to 4 = “high chance”, and scores

were summed to form a scale, with higher scores depicting higher perceived risk of CRC.

Self-Efficacy. Self-efficacy was assessed to measure participants’ confidence in

being able to effectively complete either FOBt or colonoscopy. Because self-efficacy is

situation-specific, the 9-item Endoscopy Confidence Questionnaire (ECQ; Gattuso et al.,

1992) was considered appropriate to specifically adapt to CRC screening efficacy.

Endoscopy refers to an invasive technique involving a flexible camera tube, and can be

conducted in many different parts of the body. The ECQ estimates confidence in one’s

ability to successfully cope with test participation, and was modified so that ‘colonoscopy’

(6 items) or ‘stool test’ (3 items) were substituted for ‘endoscopy’. Population means for

the ECQ range from 3.8 to 4.8 with higher scores indicating higher self-efficacy. The scale

has shown excellent internal consistency, with a Cronbach’s alpha of .90 and .92 (Gattuso

et al., 1992), however further empirical application of the ECQ will help to confirm good

psychometric properties. To date the ECQ has not been applied in studies assessing

endoscopic self-efficacy related to CRC screening.

Items modified (in italics) for the current study include, for example: “Overall, how

confident are you that you could get a colonoscopy without any difficulty?” with responses

on a 7-point Likert scale from 1 = “not at all confident” to 7 = “very confident”; and “How

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easily do you think you would follow the instructions of a stool test at home?” with

responses from 1 = “not at all easily” to 7 = “extremely easily”. It was important to phrase

items according to explicit tests (i.e., structural versus stool), as self-efficacy is frequently

used to assess confidence in relation to a specific action as opposed to wide-ranging

behaviours, for example ‘screening’ (Flight, Wilson, McGillivray, & Myers, 2010). As

might be expected, comparison of scores on FOBt-efficacy with colonoscopy-efficacy

(paired-sample comparison) showed that people scored significantly lower confidence to

screen by colonoscopy (M = 4.20, SD = 1.08), t = 27.82(201), p < .001, than they did for

FOBt (M = 4.71, SD = 0.87), although both correlated significantly (r = .55, p < .001).

Therefore self-efficacy specific to each screening test will be explored in Chapter 7 in

addition to total self-efficacy scores.

Test-Efficacy. Test-Efficacy refers to participants’ perceived effectiveness of CRC

screening tests, and perceptions that engaging in the test effectively reduces disease-threat

(Rogers, 1975). The scale comprises five items and is developed from two sources. Items 1

to 4 were adapted from a scale by Myers and colleagues (1996) in a study of annual

prostate screening. After re-wording for CRC, items included, “the stool test effectively

detects bowel polyps”, “colonoscopy effectively detects bowel cancer”, “overall the results

of the stool test are accurate”, “overall the results of colonoscopy are accurate”, and “when

colorectal polyps are found and removed during screening, cancer can be prevented”.

Participants responded on a 5-point Likert scale where 1 = “strongly disagree” and 5 =

“strongly agree”, and responses were summed so that higher scores indicated greater beliefs

about the efficacy of CRC screening tests.

Myers and colleagues (1996) found that belief in the efficacy of screening was

strongly and positively related to intention to get annual prostate screening (odds ratio [OR]

= 9.3, p < .001), however scale reliability was not reported. The fifth item was adopted for

its focus on the preventative aspects of screening “when colorectal polyps are found and

removed during screening, cancer can be prevented” (Tiro, Vernon et al., 2005).

Cancer Knowledge. The 12-item bowel cancer knowledge scale was adapted from

an existing knowledge scale for prostate cancer (PROCASE knowledge index; Radosevich

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et al., 2004) and where appropriate, the original wording was substituted with relevant

bowel cancer terms. It asks that participants respond ‘True’ or ‘False’ to statements,

including the following: “Bowel cancer is one of the most common cancers in adults”,

“People are more likely to die because of bowel cancer than because of heart disease” and,

“Abdominal cramping is one possible symptom of bowel cancer”. Incorrect responses were

coded as zero, while correct responses accrued a score of one. Scores were then summed so

that higher scores reflected higher levels of CRC knowledge.

The PKI has shown good construct and criterion validity (Radosevich et al., 2004),

with an average item-total correlation of .14 and adequate reliability according to the

Kuder-Richardson 20 (.68). Published reliability coefficients are generally within an

adequate range (e.g., α = .66; Hevey et al., 2009). It draws from three domains: natural

history of the cancer and its risk factors; test accuracy and diagnostic tests; and treatment

efficacy and complications. A total summed score is used to reflect overall knowledge.

Adaptation of the scale ensured that these domains were retained while items were also

relevant to CRC screening.

Cancer Worry. Cancer Worry was measured by a set of 6 items. With no standard

measure of bowel cancer worry, items 1 to 4 were adapted for CRC screening from the

four-item Breast Cancer Worry Scale (BCWS, Lerman et al., 1991), which has reported

Cronbach’s alphas of .86 (Bennett, Parsons, Brain, & Hood, 2010; Brain et al., 2002) and

.76 (Royak-Schaler et al., 2002). Response options range from 1 = “not at all” to 4 = “a

lot”, where higher scores indicate greater worry about CRC. The items (modified for CRC

worry) include “how much would it make you worry to think about your risk of developing

bowel cancer some day?”, “how much would worries about bowel cancer impact your

mood?”, “how much would worries about bowel cancer impact your daily activities?”, and

“what would be your level of concern about the results of future bowel cancer screening

tests?”. The remaining 2 items were adapted from McCaul, Schroeder et al. (1996) and

refer to worrying thoughts. Item 5 reads, “how often would you think about your own

chances of getting bowel cancer?” and item 6 as a more general item of bowel cancer worry

reads, “overall, how worried would you be about getting bowel cancer some day?” The

response format of the worry scale used in McCaul, Schroeder et al. (1996) was different

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for each of the items hence, for consistency, it was changed in the present study to match

that of the BCWS.

All items were expressed in future tense to correctly suffix the sentence stem, “If

you were at screening age (40 years+), please indicate how much you believe you would

think about bowel cancer”. The BCWS is one of the most widely administered worry scales

in the health literature. However there are recommendations that further construct

validation of the BCWS may be necessary (Hay et al., 2005) and that a standard scale of

cancer worry would benefit the empirical assessment of worry across health and

psychosocial studies.

Screening Bias. A 10-item screening bias measure was implemented which was

based on cognitive bias about the perceived likelihood of belonging to specific groups and

categories of cancer vulnerability (the representativeness heuristic). This refers to the

distortions in thinking that typically occur when individuals make rapid judgements of

probability, for example, assess the probability of themselves or others of particular ages,

gender, lifestyle practices or health-histories developing CRC. Screening bias items

measured participants’ level of stereotyping about age (1 item), gender (1 item), family

history (2 items), the usefulness or worth of testing (3 items) and absence of symptoms (3

items). Declining a screening test based on feeling too young, being female, having no

family history, and the absence of symptoms, are all interpreted to represent some level of

distorted or biased thinking, as the value of screening remains important in all of these

groups (i.e., all adults > 50, males and females, asymptomatic and symptomatic patients,

and those with or without a family history).

Seven of the 10 items were adapted from four scales previously used to measure

screening fallacies, misconceptions or ‘barriers’. Items 1 and 4 (Harewood et al., 2002), and

item 3 (Schnoll et al., 2003), reflect symptom absence and usefulness of testing. Items 2, 5

and 7 reflect the worth of testing (Perez-Stable, Sabogal, Otero-Sabogal, Hiatt, & McPhee,

1992; and item 7 from Busch, 2003). Items 6 and 10 (Hynam et al., 1995) represent family

history stereotype about cancer vulnerability. All of the original scales from these studies

showed reasonable reliability. The remaining 2 items were developed for the purposes of

the present study to address gender and age. Participants were asked to rate their agreement

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or disagreement to items on a 5-point Likert scale from 1 = “strongly disagree” to 5 =

“strongly agree” after the following sentence stem, “If I were considering being screened

for bowel cancer, I would be LESS LIKELY to screen:”. Scores on the items were

summed, and higher scores suggested stronger biases in relation to CRC screening. Item

examples, item origin, and category of bias are presented in Table 6.1.

Table 6.1

Screening Bias Instrument

Bias category Author of original item Example item

Feel too healthy

(asymptomatic) (3 items)

Harewood et al. 2002 If I felt too healthy to have

bowel cancer (item 1)

Limited value of screening

(3 items)

Perez-Stable et al. 1992 Because there is very little one

can do to prevent cancer (item

5)

No Family History (2

items)

Hynam et al. 1995 If I have no family history of

bowel cancer (item 6)

Gender (1 item) Developed for the present

study

Because my gender reduces

my risk of bowel cancer (item

8)

Age (1 item) Developed for the present

study

You have to be at least 60

years old to be at any real risk

(item 9)a

aPhrased for use in a predominantly student sample (M = 27 years).

Cancer Fatalism. A short measure of fatalistic thinking (3 items) was included as a

precautionary measure for checking the singularity of screening bias (see Section 2.5,

Chapter 2 for justification). This variable is not included in hypothesis testing, and was

only assessed to confirm screening bias as a unique construct, and not an aspect of fatalistic

cognitive styles. Two items are from the 15-item Powe Fatalism Inventory (PFI; Powe,

1995), which was considered too lengthy to use in its published form; and a third item is

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from Perez-Stable et al. (1992) because it strongly reflected fatalistic thinking. Items on the

PFI emphasize a unitary construct of inevitability of death, pessimism, and

predetermination (Powe, Daniels, & Finnie, 2005).

The items are: “no matter what, if someone gets cancer they should prepare for

death”, “I believe that if someone is diagnosed with cancer they will end up dying from it”,

and “I would rather not know if I had an incurable cancer”. Although the PFI uses a binary

response format, a 5-point response format (1 = “strongly disagree” to 5 = “strongly agree”)

was used to be consistent with the present survey, with higher summed total scores

indicating higher cancer fatalism. The PFI has demonstrated moderately good internal

consistency ranging from .80 to .89 (Powe, 1995; Powe et al., 2005; Powe & Weinrich,

1999).

6.2.2.3 Emotion measures

Screening Fear. Fear of screening was measured by 19 items representing three

distinct source-related fear responses based on Consedine and Moskowitz’s theoretical

model of fear specificity (2007) and Smith et al.’s (2005) review: Fear of Procedural

Aspects; Fear of Embarrassment; and Fear of Cancer/Mortality. All items were prefaced

by the sentence stem, “How much would you be afraid of the following if you were

thinking about getting screened for bowel cancer?” All response options across the

subscales ranged from 1 = “not at all frightened” to 6 = “very much frightened” so that

higher scores indicated higher levels of screening fear.

The 9-item Fear of Procedural Aspects subscale was derived from the Colon

Cancer Screening Survey (Harewood et al., 2002) and from Hynam et al. (1995), including

fear of: “the discomfort associated with colonoscopy”, “risks associated with colonoscopy”,

and “experiencing pain during a colonoscopy”. The 8 items from Harewood et al. (2002)

related directly to features of the procedure, expectations of pain and discomfort during

procedures, preparation aspects, and possible future testing. Scale properties were not

reported. The ninth item related to fear of additional screening procedures (“having to get

further tests if there is suspicion of cancer”) (Hynam et al., 1995).

The 5-item Fear of Embarrassment subscale was adapted from a review of

qualitative studies about CRC screening (Smith et al., 2005) and modified in the present

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study to specifically reflect CRC-related fear. Items included “being seen as exaggerating

mild or nil symptoms”, “being embarrassed about discussing my rectum or bowel” and,

“that it will appear weak of me to be concerned or interested about bowel cancer”. Smith

and colleagues (2005) reviewed international publications from 1994 to 2004 for common

themes related to diagnostic delay across a range of cancers (with three studies specific to

CRC). Using meta-ethnography to identify shared concepts and new insights across 32

published papers, they reported that 18 of the studies stated fear of embarrassment as a

major screening barrier contributing to delay in diagnosis. For the purposes of the present

study, the items adapted from the review were modified to first-person language and

formatted to a 6-point Likert scale.

Fear of Cancer was measured by 5 items modified from Smith et al.’s (2005)

review of qualitative data on diagnostic delay for cancer. Smith and colleagues identified

fear of cancer as the second emotive factor contributing to delay in cancer diagnosis,

including for example, “finding you have a fatal incurable disease” and “having cancer with

serious and painful symptoms”. The authors note that this type of diagnostic delay

attributable to fear, is compounded by the experience of “vague or non-specific initial

symptoms” (p. 827), which are characteristic of the early stages of CRC and therefore

suggestive of particular relevance in bowel screening avoidance or delay. The 5 items were

adjusted to first-person language and formatted to a 6-point Likert scale.

Disgust. Disgust was measured using the Disgust Scale–Revised (DS-R, Haidt,

McCauley, & Rozin, 1994, modified by Olatunji, Williams et al., 2007). The DS-R has

been validated in establishing disgust thresholds across three sub-scales (reduced from

eight in the original Disgust Scale, Haidt et al., 1994) to appraise different types of disgust

elicitors: Core, Animal Reminder, and Contamination Disgust. This revised three-factor

model has been supported by structural modelling, and overall internal consistency has

been reported as moderately good at .84 (Olatunji, Williams et al., 2007). Internal

consistency for each sub-scale is satisfactory: Core (.74), Animal Reminder (.78), and

Contamination (.61), and it has demonstrated good construct validity (Olatunji, Williams et

al., 2007). The DS-R has not been applied in any published CRC screening investigations

to date.

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The DS-R is split into two sections. In section 1, the 5 response options range from

0 = “strongly disagree” or “very untrue about me” to 4 = “strongly agree’ or “very true

about me”. Items in this section include “It bothers me to hear someone clear a throat full of

mucous”, “I never let any part of my body touch a public toilet seat”, and “If I see someone

vomit, it makes me sick to my stomach”. Section 2 of the DS-R is rated from 0 = “not

disgusting at all” to 4 = “extremely disgusting”. In response to the instruction “How

disgusting would you find each of the following experiences?” example items include,

“You see a man with his intestines exposed after an accident”, “A friend offers you a piece

of chocolate shaped like dog poo”, and “You are walking barefoot on concrete, and you

step on an earthworm”. For consistency, and to match the format of other scales in the

survey, the response format ranged from 1 – 5, but was converted back to its original scale

of 0 – 4 for analysis and interpretation. Higher scores reflect higher responsiveness to

disgust elicitors.

Medical Disgust. In addition to the DS-R, 7 items specific to medical settings were

designed for use in the present study in order to measure disgust that relates exclusively to

illness or medical examinations such as CRC screening. The instructions (as above)

preceded items such as, “describing the appearance of your stool to your doctor”, and

“taking a stool sample with a swab”. Items were rated in the same scoring format as Section

2 of the DS-R and were analysed as an additional factor of Disgust.

Medical Embarrassment. The Medical Embarrassment Questionnaire (MEQ)

(Consedine et al., 2007) is a recently developed 31-item inventory measuring bodily

embarrassment and judgement concern as two separable factors. The authors established

content and face validity during questionnaire development using inter-rater identification

of relevant constructs, producing an initial inventory of 53 items. The authors then

performed principle components analysis, reducing the scale to 31 items and ascertaining a

two-component structure: bodily embarrassment and judgement concern (embarrassment

about being judged). Reliability, as indicated by Cronbach’s alpha coefficients, for bodily

embarrassment and judgement concern were excellent at .96 and .86, respectively.

Instructions to the scale were, “Some people have reported that the following

scenarios can be uncomfortable or humiliating. Please rate your agreement or disagreement

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to the situations below”. Response options range from 1 = “strongly disagree” to 5 =

“strongly agree”, and higher scores are interpreted to reflect higher levels of medical

embarrassment. The following items are 4 examples, “showing my body to a stranger, even

to a doctor, is humiliating”, “I am uncomfortable when a doctor has to examine my sexual

organs or rectum”, “I feel stupid when a doctor tells me that my symptoms are not as

serious as I thought they were” and, “being naked in front of the doctor or a nurse is

embarrassing”. To date, only one empirical investigation of the MEQ (but no other

established dispositional or situational embarrassment inventory) has been published in

relation to the prediction of CRC screeners or screening intenders (see Consedine, Ladwig,

Reddig, & Broadbent, 2011).

6.2.2.4 Social measures

Social Norms. Subjective social norm beliefs about family, friends, and regular GP

was obtained in relation to CRC screening using four items. Three items originally derive

from a scale measuring perceived beliefs about the attitudes of key people, and desire to

comply with these norms (Vernon, Myers, & Tilley, 1997). DiMatteo et al. (1993)

originally developed the measure as a 3-item Subjective Norm scale during the validation

of the 38-item Adherence Determinants Questionnaire (ADQ), achieving a Cronbach’s

alpha of .85. Administration of the scale has led to varying reliability, for example, Vernon

and colleagues (1997) reported a relatively low alpha of .57, however only two items of the

scale were assessed. An additional item was included in subsequent research to recognise

the importance of GP influence (Tiro, Vernon et al., 2005), and this revised 4-item scale

has been shown to have acceptable construct validity and reliability (Tiro, Diamond et al.,

2005). Tiro, Vernon et al. (2005) reported an alpha of .61, while Tiro, Diamond et al.

(2005) reported a Cronbach’s alpha of .84 for the 4-items. In response to the sentence stem

“When it comes to screening for bowel cancer using a stool test or colonoscopy”, items

include, “I want to do what members of my immediate family think I should do”, “I want to

do what my friends think I should do”, “I want to do what my regular doctor thinks I should

do”, and “I want to do what people important to me would do”. Responses on a 5-point

Likert scale range from 1 = “strongly disagree” to 5 = “strongly agree”, with higher scores

reflecting a perception of stronger norms in relation to engaging in CRC screening.

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Social Support. The social support measure was modified from a binary response

Social Support scale administered in a study by Kremers et al. (2000) to a 5-point Likert

scale for uniformity with the present survey format. In addition, the four items were

adapted from second-person into first-person narrative to maintain consistency with the

survey. Kremers and colleagues (2000) found significant differences in social support

between participants who accepted screening and those who declined, indicating predictive

validity, however internal consistency was not reported.

In the present study, the 4 items were used to measure functional social support

(from family and friends), including “I believe people in my social network would suggest I

get screened”, “I have someone in my social network who could accompany me to a

colonoscopy”, “I believe people in my social network would understand my feelings about

bowel screening”, and “I know someone who has been screened for bowel cancer”.

Responses on a 5-point Likert scale range from 1 = “strongly disagree” to 5 = “strongly

agree” to the same sentence prefix as for Subjective Norms, and scores were summed so

that higher scores indicated perceptions of greater social support.

6.2.2.5 Dependent measures

Screening Intention. The main dependent measure in the survey was Screening

Intention, which was assessed by three items. With no standard published instrument for

assessing cancer screening intention, the items were developed after a review of the

literature and various measures of intention, including Ajzen (2002); Emmons et al. (2008);

and McCaffery et al. (2003). To cater to an anticipated youthful sample, the items were

worded to reflect future preferences, and were prefaced with the sentence stem “If you are

(or imagine you are) 40 years or older” to the items, “would you want to be screened for

bowel cancer?”, “would you want to be screened using a stool kit?” and, “would you want

to be screened by colonoscopy?”. All items used the response scale format, 1 = “definitely

not’ to 5 = “definitely yes”. Responses to these items were summed to form a single

screening intention scale with higher scores indicating stronger screening intentions.

General intention, and intention unique to the FOB test and colonoscopy, are also assessed

in the descriptive analyses as single items, and are generated as sample frequencies for

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three groups: participants who definitely or probably have no intention to screen; are

undecided; or definitely or probably intend to screen.

Decisional Conflict. A secondary dependent variable was Decisional Conflict,

measured by the Decisional Conflict Scale (DCS; O’Connor, 1995). This inventory can

help to ascertain whether an individual has made a decision that they intend to see through;

are satisfied with; is informed; is consistent with values; and is effective. It identifies

whether there are information deficits, emotional distress, and uncertainty about the

decision. The 16-item DCS includes the following items “the decision is easy for me to

make”, “it’s clear what choice is best for me”, “my decision shows what is important to

me” and, “I am satisfied with my decision”. There are five subscales measuring: 1)

uncertainty, 2) unclear values, 3) feeling uninformed, 4) feeling unsupported in the

decision, and 5) decision quality. All items are scored on a 5-point response scale from 1 =

“strongly disagree” to 5 = “strongly agree”, with scores summed and averaged to give a

total score from 0 – 100. Higher scores reflect decisional conflict while lower scores reflect

decisional certainty, with scores < 25 associated with implementation of the decision or

intention (Hollinghurst et al., 2010).

The psychometric properties of the DCS have been acceptable. O’Connor (1995)

administered the DCS to 909 participants and found that the scale significantly

discriminated between people with strong intentions to accept or decline influenza

vaccination or breast cancer screening, and those with uncertain intentions. Its test-retest

coefficient was .81, and Cronbach’s alpha ranged from .78 to .92 for uncertainty, and .77 to

.84 for the effective decision-making subscale, suggesting moderate to strong internal

consistency. The DCS has also demonstrated cross-cultural validity in both Dutch palliative

chemotherapy samples and French breast cancer gene testing samples, with coefficients

ranging from .74 to .83 (Koedoot et al., 2001), and .56 to .87 (Mancini et al., 2006),

respectively.

6.2.2.6. Organisation of survey materials

Part One of the survey booklet contained 4 scales about demographic, health, and

subjective health status; and CRC knowledge. Part Two of the booklet began with a brief

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description of two of the main tests for bowel screening: stool testing (FOBt) and

colonoscopy (see Appendix C). The descriptions contained enough information to describe

the essential details of the test and procedure, including the location of the test, any

physical risks, how the test is conducted and what materials are involved (e.g., a swab in

FOBt, or a camera tube in colonoscopy), and what the test is looking for (lesions in a

structural exam; or blood markers in a stool test). This information was provided to ensure

that participants had a reasonable understanding of the behaviour for which they were

reporting intentions. The remaining sections of Part Two contained inventories on social

norms and support, screening bias and fatalism, beliefs about the efficacy of the screening

tests, perceived risk of CRC, and CRC worry.

Part Three measured the emotion variables of fear (fear of cancer, embarrassment,

and procedural aspects of screening); disgust (core, contamination, and animal-reminder

disgust); and medical embarrassment (bodily embarrassment and judgement concern).

Part Four assessed screening intentions, decisional conflict, and self-efficacy. Self-

efficacy was assessed subsequent to measuring screening decisions. This was because

responding to explicit statements about the colonoscopy exam, such as being able to relax

during the procedure, may influence decisions about screening that are made after

considering one’s self-efficacy. Response format and internal consistency for each

instrument is summarised in Table 6.2 at the end of the chapter.

6.2.3 Procedure

Participants were invited to voluntarily participate in a study on “attitudes toward

bowel cancer screening” over a five-month period during 2008. The study was advertised

as part of a research experience program conducted at Swinburne University of Technology

with first-year psychology students, and also advertised on a university student Web page

and online staff newsletter. Students were also verbally invited to participate in the study at

undergraduate psychology lectures. It was optional to complete the survey in a paper-and-

pencil format, or an online research website powered by Opinio Survey SoftwareTM

(ObjectPlanet, 1998-2011). Because of the large student demographic, participants were

advised to respond to social norms about CRC screening, CRC screening bias, CRC worry,

and screening intentions as though they were aged 40 or older, “Thinking about screening

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for bowel cancer, and imagining you are aged 40 or older, please rate how much you agree

or disagree with the following statements”.

The survey questionnaire took an average of 27 minutes (SD = 9 minutes) to

complete according to 83% of surveys containing this information via the online software.

Fifty hard-copy surveys were distributed at lectures and tutorials, and 35 of these were

returned (a response rate of 70%). The online software recorded 293 guests to the survey,

however it is not possible to determine a true overall response rate using an anonymous

online format because some participants may have attempted the survey more than once

before submitting a completed survey. Multiple attempts of the survey by one person would

have been logged as a unique participant on each occasion, thereby inflating the true

number of survey visitors.

Incomplete or unfinished surveys may relate to the length of the survey, but also to

technical aspects of using an online survey, with two participants contacting the

investigator to report ‘freezing’ of the survey upon clicking ‘Finish’. Although only two

participants made contact with the researcher, there may have been others who experienced

similar technical faults with the program without reporting it. Incomplete online surveys

could also be due to a number of other reasons, including withdrawal because of the nature

of the survey or inadequate time available to complete the survey.

6.2.3.1 Ethical approval. Swinburne’s Human Research Ethics Committee

(SUHREC) provided ethical approval for the procedure (see Appendix A). No contact

information was collected, and Internet Cookies (which gather information about the site’s

visitors) was disabled so that complete anonymity could be assured. Therefore, consent was

implicit by completion and submission of the survey. A consent information statement was

provided at the beginning of the survey, briefly describing the nature of the survey, the

types of questions asked, and a caveat that the survey could be discomforting for some

participants.

A final item measuring discomfort related to the survey was attached to the self-

efficacy scale in an identical response-option format ranging from 1 = “not at all

confronting” to 7 = “extremely confronting”. This item was included to satisfy concerns of

the ethics committee, and was monitored throughout data collection so that the survey

could be re-evaluated and moderated if necessary. Results were then included in the annual

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report for the Swinburne human ethics committee. The average response was that the

survey was not considered to be confronting (M = 3.33, SD = 1.81).

6.3 Data Preparation and Screening

Prior to hypothesis testing, all variables were examined for missing values, outliers,

and normality of distribution using SPSS for Mac, version 17 (SPSS Inc., 2008) (see

Section 6.3.1). Of 363 surveys distributed, 293 were returned (80.7%) and explored for

suitability in data analysis. Survey responses with no variability (for example, those who

selected only “strongly agree” response options) throughout the survey were not used in

subsequent screening (n = 2), leaving a sample of N = 291. Data ranges were checked for

legitimate responses on each variable. Responses outside of valid parameters occurred in

three cases, and were subsequently crosschecked with original data and substituted for true

scores. Before further deletion of cases, a missing values analysis was conducted on N =

291 to ensure that data were missing completely at random. Data range, normality and

outliers were then examined by graphical assessment and skewness and kurtosis statistics.

This process is described below.

6.3.1 Missing data

Missing values can potentially obscure data interpretation if values are not missing

at random and are systematically different from cases without missing values. A series of

procedures was undertaken prior to a missing values analysis (MVA). Fifty-four

participants withdrew after completing only demographic data (missing over 50% of the

data). These cases were removed (leaving N = 237).

Overall, 31% of the cases had incomplete data in the questionnaire. Means were

imputed for cases with a single missing value on a scale (0.8% of survey). MVA using

SPSS was then conducted on remaining data where 5% or more values were missing on any

one scale. Ten per cent of participants (n = 24) withdrew from the survey approximately

halfway to completion (prior to some cognitive and any emotion scales), and 5% (n = 10)

withdrew immediately before completing the emotion scales. Patterns of missing data were

explored prior to removing these 34 cases from the data set.

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The greater response rate for cognitive variables (where between 3.8% to 18.6% of

participants omitted to answer items on a given scale) over emotion variables (where 14.3%

to 21% of the sample had missing responses on a given scale) may be explained by the

positioning of most cognitive scales in the beginning of the survey, and because attrition

can be high in surveys of this length. This explanation fits with the finding that a greater

number of participants (18.6%) omitted responses on the self-efficacy scale compared with

cognitive scales presented at the beginning of the survey (such as knowledge, 3.8%).

Medical embarrassment and decisional conflict had the greatest number of cases

with missing values (21% and 20%, respectively), while knowledge (3.8%), norms and

support (10%), and screening bias (10.5%) had the fewest. This was explored further by

producing tabulated pattern tables, revealing disgust and embarrassment factors were often

missing together. Again, this is likely explained by the fact that these two variables were

grouped together at the end of the survey and measured by lengthy scales, resulting in

greater attrition. This interpretation is supported by the pattern that most cases with missing

data were spread across the final eight scales (25 cases) while the next largest group of

cases (7) were spread only across the last four scales (emotion variables), where these

participants discontinued survey participation. An alternative explanation may be that

emotion scales are more likely to result in non-responding. In Study 2 (see Chapter 8) the

presentation of scales was reversed to mitigate order effects, thus testing whether

participants are less willing to respond to emotion than cognitive scales.

Little’s Missing Completely At Random (MCAR) test was performed to confirm

that the above missing data pattern was occurring completely at random and that these

differences were not attributable to some other reason. The results of Little’s MCAR test

revealed a non-significant p-value of .13 (χ2(119) = 136.66, n.s), indicating that cases with

missing data do not systematically differ from those with valid data on the variables in this

data set. Therefore it can be concluded that missing data in this sample were MCAR. As a

result, cases missing > 30% of a scale could be deleted (n = 34) without incurring costs to

data interpretation. The expectation maximisation (EM) algorithm provides the best

estimate of randomly missing data (Tabachnick & Fidel, 2007) and was employed to

estimate means in remaining missing data. Data cleaning thus resulted in a sample size of N

= 203 for screening of the dataset.

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6.3.2 Outliers

Univariate outliers represent cases that are atypical or extreme by being distant from

other cases on a single variable (Schumacker & Lomax, 2004). Histograms, stem-and-leaf

graphs, box plots, and normal probability plots were inspected, identifying 24 extreme

values on the demographic variable Age (values > 43), between 1 and 10 outliers on 2

further demographic variables, 3 cognitive variables (Knowledge, Test-Efficacy, Self-

Efficacy), Decisional Conflict, and both Norms and Support. As it was expected that

extreme age values would arise in what was largely a student sample, with a minority of

non-student participants, this variable was unaltered. With only a small number of

univariate outliers across other variables, transformations were not performed because they

did not considerably improve the distribution’s normality, and could distort interpretation.

Notably, a single case occurred as a univariate outlier on seven scales. It was retained at

this stage to inspect in tests for multivariate outliers.

Multivariate outliers are divergent from other cases on a combination of variables

(Kline, 2005) and can be detected by Mahalanobis distance (Mahalanobis D2). Mahalanobis

D2 determines the distance of the score from the centroid of remaining cases, where the

centroid is the point created by the means of all the independent variables (Tabachnick &

Fidell, 2007). Using a χ2 distribution critical value of 20.52(5), at α = .001, there were no

multivariate outliers on emotion variables, with minimum and maximum Mahalanobis

values of .75 to 18.79.

Two multivariate outliers were identified across the following 6 cognitive and social

variables (Knowledge, Test Efficacy, Screening Bias, Norms, Support, and Worry), with

extreme Mahalanobis distances of 26.15 and 40.66, using a χ2 distribution critical value of

24.32(7), at α = .001. It is not surprising to have a few outlying cases within a moderately

large data set (Coakes & Steed, 2007), so it was determined that the single weak

multivariate outlier should be retained. However, as the strong multivariate outlier was the

same case presenting as a recurring univariate outlier, it was removed from the data set,

leaving a total sample of N = 202. This decision was supported by a regression showing

that this multivariate outlier was found to significantly differ from all other cases on Self-

Efficacy, Disgust, Fear, Worry, and Knowledge.

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6.3.3 Normality

Because the sample was greater than 100, a graphical assessment of normality was

conducted by inspecting histograms, and normal and detrended quantile-quantile plots (Q-

Q plots). Statistical tests for samples sizes larger than 100 cases are often considered too

sensitive in detecting violations of normality (e.g., the Shapiro-Wilks test) (Coakes &

Steed, 2007). Graphical methods of inspection were conducted to determine if residuals

were symmetrically and randomly distributed around a mean of zero (that is, that there is no

systematic pattern to the error). Inspection of the normal Q-Q and detrended Q-Q plots

suggested that Knowledge, Social Support, and Worry might not be normally distributed.

There were no suspicions of non-normality across emotion variables.

In medium sized samples (N = 100 to 300), skewness and kurtosis indices can

provide good indications of normality violations. Applying the ratio of the unstandardised

skew or kurtosis index over its standard error is an appropriate method for small to medium

sized samples (Tabachnick & Fidell, 2007). This suggests that Z scores larger than 3.29, or

below -3.29 signify normality violations at p < .001 (Field, 2005). Although there is general

consensus that skewness values > 3 indicate a departure from normality, there is

disagreement about acceptable values of the kurtosis index, where values ranging between

8 and 20 have been deemed extreme (Kline, 2005). For current purposes, a conservative

kurtosis limit of 10 was adopted (Kline, 2005).

There was some evidence of slight skew in the distribution of Norms (-3.47),

Support (-3.47), Test-Efficacy (-3.5), and Worry (3.68), which were assessed using Zskew

and Zkurtosis scores. Log and square root transformations were computed, but did not

effectively improve normality of skewed distributions, except for Worry. As skew values

were close to conservative levels for moderate-sized samples, and larger N can be overly

sensitive to detections of normality using Z-indices (Kline, 2005; Tabachnick & Fidell,

2007), it was considered appropriate to leave all scales untransformed. A square root

transformation produced a better distribution for Worry (from 3.68 to a Zskew of 1.98),

however there is reduced benefit in transforming a single variable. Knowledge had a

negative skew of -6.54, which is not unusual given there were high levels of cancer

knowledge in the sample. No skew was detected amongst emotion variables, and no

kurtosis was detected across the entire sample. While health variables indicated skew, this

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most likely reflects a young, generally healthy sample that has not yet experienced

significant health concerns.

Multivariate normality is a requirement for many inferential statistics (Schumacker

& Lomax, 2004), and indicates that all variables and all combinations of variables are

normally distributed. Evidence of univariate normality does not imply that bivariate or

multivariate normality will result. Multivariate normality is assessed on each measurement

model by Mardia’s multivariate kurtosis coefficient (see Chapter 7 and Appendix G).

6.3.4 Linearity and homoscedasticity

Univariate linearity was assessed by scatterplot inspection of each potential

predictor variable against Screening Intention and Decisional Conflict. Graphical inspection

showed some linearity or at minimum, shapeless patterns, which indicate that assumptions

of linearity and homogeneity of variance are defensible (Kinnear & Gray, 2009). To assist

graphical inspection, an ANOVA Test of Linearity was computed on total predictor

variables (i.e., not subscales) (Garson, 2008a). For many variables, there was no significant

linear or non-linear relationship with the major criterion (Screening Intention), while Self-

Efficacy had a significant linear relationship only. Although linearity with the criterion did

not reach significance, there was no concern about violation of this assumption due to an

absence of significant non-linear relationships. The presence of a non-linear relationship is

ignorable provided there are no known theoretical implications (Tabachnick & Fidell,

2007).

Homoscedasticity assumes that the variability in the criterion variable has about the

same spread over all levels of the predictor variable in a regression analysis (Field, 2005).

For ungrouped data, heteroscedasticity (failure of homoscedasticity) is not critical to

analysis (Tabachnick & Fidell, 2007), however scatterplots were inspected showing that

linear relationships could be surmised from most plots.

6.3.5 Criterion validity and intercorrelations between variables

Correlations between independent and dependent variables were checked for

theoretically expected associations, indicating no concerns about criterion validity (see

Appendix F for the correlation matrix of all variables prior to factor analysis of scales).

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If multicollinearity can be precluded, then measures can be said to be unique

constructs and independent of one another (Tabachnick & Fidell, 2007). Correlations were

examined for pairwise colinearity, revealing low colinearity between cognitive and social

variables (Screening Bias, Perceived Risk, Self Efficacy, Test Efficacy, Worry, Knowledge,

Norms, and Support). A low to moderate degree of positive correlation between all emotion

variables was found, although fear was markedly correlated with bodily embarrassment (r =

.69), suggesting caution should be used in predictions using both variables. These initial

checks confirmed there were no unexpected relationships, or concerns about

multicollinearity (intercorrelations all < .70).

6.3.6 Scale reliability

Internal reliability demonstrates the internal consistency of a scale designed to

assess a common construct, and is usually measured by reliability coefficients (Mullen et

al., 2006) such as Cronbach’s coefficient alpha (α). Cronbach’s alpha was computed in the

present study to ensure reliability of published scales generalised to the present sample.

Adequate reliability for psychological constructs is generally thought to range from .6 to .7,

while .7 to .8 reflects good reliability, and coefficients larger than .8 indicate excellent

reliability (Kline, 1999, cited in Field, 2005). Nearly all reliability coefficients in the

present sample were average to excellent, and are presented in Table 6.2.

Confirmatory analysis in Amos was inappropriate for assessing the knowledge scale

as it was not measured along a continuous response format. Cronbach’s alpha was therefore

computed for the 12-item knowledge scale (α = .45), with three items consecutively

removed to obtain the highest possible reliability (α = .53), implying an unstable scale (see

Section 7.4.13, Chapter 7). The absence of a ceiling effect (mean correct response ≤ 90%)

is supported by the current sample scoring a mean correct response of 77%.

6.4 Data Preparation and Screening: Decision Summary

A number of decisions were made about the data set as a result of screening

observations. Participants who completed only demographic information (n = 54), or had

no variability across responses (n = 2), were removed from the sample. Remaining missing

data were explored by MVA and Little’s MCAR test, which confirmed data were missing

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completely at random. Thus, an additional 34 cases missing more than 30% of data were

removed, and a further single outlying case removed. A final N = 202 is considered fair for

the statistical purposes of the present study (Tabachnick & Fidell, 2007).

Normality was explored by graphical assessment, suggesting that Knowledge, Social

Support, and Worry may not be normally distributed. Skew values suggested distorted

distributions in Norms, Support, Test-Efficacy, and Worry, but these were close to

conservative cut-offs, with raw scales retained for clearer interpretation of multivariate

analyses. Health variables indicated skew, most likely reflecting a predominantly youthful

sample. No serious concerns were raised about linearity, heteroscedasticity or

multicollinearity, and scale reliability is reported in Table 6.2.

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Table 6.2

Sample Means, SDs, Scale Ranges, and Cronbach’s Alphas for Variables Prior to Factor

Analysis

Variable M SD Observed range

Possible range

α Number of items

Cognitive

Risk perception 7.22 2.18 3-12 3-12 .78 3

Self-efficacy 40.95 9.32 9-63 9-63 .82 9

Self-efficacya 3.54 1.03 0-6 0-8 .82 9

Test efficacy 17.98 2.98 6-25 5-25 .77 5

Knowledgeb 9.25 1.79 1-12 0-12 .45 12

Worry 10.7 3.87 6-22 6-24 .88 6

Screening bias 30.65 10.94 13-59 13-65 .92 13

Fatalism 6.78 2.89 3-15 3-15 .72 3

Emotion

Fear total 62.00 21.65 19-114 19-114 .94 19

Fear procedural 30.22 11.63 9-54 9-54 .92 9

Fear embarrassment 12.86 6.51 5-30 5-30 .89 5

Fear cancer 18.91 6.57 5-30 5-30 .84 5

DS-R totala 3.04 .66 1.6-4.6 1-5 .89 25

Disgust: corea 3.26 .69 1.7-4.7 1-5 .79 12

Disgust: contaminationa 2.45 .78 1.0-4.6 1-5 .63 5

Disgust: animal remindera

3.11 .83 1.5-5.0 1-5 .79 8

Disgust: medicala 2.39 .87 1.0-4.7 1-5 .87 7

MEQ total 78.51 28.15 31-144 31-155 .97 31

Bodily embarrassment 53.40 20.45 19-95 19-95 .97 19

Judgement concern 25.11 10.00 12-51 12-60 .92 12

Social

Subjective norms 14.15 3.36 5-20 4-20 .79 4

Social support 14.35 3.33 4-20 4-20 .60 4

Outcome Variables

Screening intention 10.42 2.53 3-15 3-15 .69 3

Decisional conflict 56.91 12.30 16-80 16-80 .95 16

Note. DS-R = Disgust Scale-Revised; MEQ = Medical Embarrassment Questionnaire. aRepresents mean scale scores for comparison with population norms. All other scale scores indicate the average, total cumulative score. bRaised to .53 by removal of three items.

N = 202.

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CHAPTER 7

STUDY 1 ANALYSES AND RESULTS

Exploring cognitive, emotion and social relationships with CRC screening intention

and decisional conflict in a convenience sample


This chapter presents the analyses and findings of a preliminary investigation,

aiming to inform a second study of CRC screening intention and behaviour in the

Australian community (see Study 2, Chapters 8 to 10). In Chapter 6, psychometric

properties were discussed and internal consistency of the original scales was presented.

The first part of this chapter involves a description of sample characteristics

(Section 7.2). A description of the processes involved in confirmatory factor analysis is

then presented (Section 7.3), followed by a summary description of the development of the

scales (by factor analysis), which was conducted to maximise their psychometric integrity

prior to scale validation (Section 7.4). (Full descriptions of the factor analyses are presented

in Appendix G.) Results are then presented in relation to hypotheses in terms of the

directionality of relationships amongst the variables (Section 7.5). Section 7.6 provides a

chapter summary.

7.2 Sample Characteristics

A summary of the demographic characteristics of the sample is presented in Table

7.1. The sample comprised 47 men and 155 women in the age range of 18 to 75 years (M =

27, SD = 11). The age range was characteristic of an Australian university psychology-

student population.

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Table 7.1

Sample Characteristics on Demographic Variables in Study 1 (N = 202)

Demographic Variable n %

Age (M=27, SD=11)

18-25 124 61.4

26-29 25 12.4

30-39 27 13.4

40+ 26 12.9

Gender

Male 47 23.3

Female 155 76.7

Cultural Background

Australia 154 76.2

Asia 20 9.9

English-speaking (England, USA) 19 9.4

Continental Europe 9 4.5

Marital Status

Single 128 63.4

de facto 39 19.3

Married 29 14.4

Separated/Divorced 6 3.0

Employment Status

Full-time 41 20.4

Part-time 62 30.8

Casual 63 31.2

Other (unemployed/retired/parent) 35 17.4

Student Status

Full-time tertiary 139 68.8

Part-time tertiary 46 22.8

Not studying 17 8.4

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7.2.1 Health and screening practices of the sample

A summary of the health characteristics of the sample is presented in Table 7.2. It

was most common to hold some type of health insurance (61% had some level of private

health insurance), similar to Australia’s average where 51% are insured (Australian Bureau

of Statistics [ABS], 2006). Still, over one-third of the sample had no private health

insurance, which may reflect the socio-economic status of a majority sample of full-time

students, many of whom are only in casual or part-time employment.

Women reported poorer bowel health, with 28% of females reporting some kind of

gastrointestinal condition, compared to 19% of males. Irritable Bowel Syndrome (IBS) was

the most commonly reported condition. Of the total sample, just over one-quarter reported a

history of gastrointestinal conditions.

Because of the young age of this sample, few participants were expected to have

had a screening test for CRC. As expected, CRC screening was uncommon, with stool tests

being most usual (16%), followed by both colonoscopy (12%) and digital rectal

examination (DRE) (12%). Participants with a history of bowel screening had a mean age

of 32 years (SD = 13.67). The majority of participants had no history of bowel screening,

however, all 26 participants age 40 or older had some type of screening, and most had

screened for CRC (n = 16; 61.5%). While descriptive analysis revealed moderately low

rates of any screening behaviour, it remains important to identify the relationships these

variables have with future CRC screening intentions.

The majority of the sample expressed an intention to probably or definitely screen

for CRC, and this pattern held across gender and age groups (see Appendix E for results of

the precautionary hypotheses).

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Table 7.2

Sample Characteristics on Health Variables in Study 1 (N = 202)

Health Variable n %

Health Insurance

None 79 39.1

Hospital 25 12.4

Extras 5 2.5

Hospital and extras 93 46.0

Bowel Screening

DRE 24 11.9

Barium Enema 11 5.4

FOBt 32 15.8

Sigmoidoscopy 8 4.0

Colonoscopy 25 12.4

Female Screening

Mammography 27 17.4

Cervical screening 94 60.6

Breast self-exam 103 66.5

Male Screening

Prostate screening 4 8.5

Testicular self-exam 13 27.7

Gastrointestinal Health History

IBS 34 16.8

Bowel polyps 6 3

Other 23 11.4

Family Gastrointestinal Health

Diverticulitis 22 10.9

IBS 44 21.8

Polyps 23 11.4

Family with CRC (age of onset M=61, SD=12) 45 22.3

Screening Intention (irrespective of test type)

Intend 142 70.3

Do not intend 26 12.9

Undecided 34 16.8

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Gender differences in screening practices and intentions. Unsurprisingly in a

youthful sample, only one man and 18 women had been advised to screen for CRC.

However, nearly three-quarters of both men and women reported general intentions to

screen for CRC. Men’s intentions did not differ by test-type, with 49% of men indicating

intentions to screen by FOBt (n = 24) and colonoscopy (n = 23). Fewer women reported

intentions to screen by colonoscopy (n = 65; 42%) than by FOBt (n = 93; 60%). Intentions

to screen by gender and age group are presented in Table 7.3.

Table 7.3

Screening Intention by Gender and Age Group

Screening intention (irrespective of test type)

Group (n) Intend

n (%)

Do not intend

n (%)

Undecided

n (%)

Men (47) 33 (70.2) 7 (14.9) 7 (14.9)

Women (155) 109 (70.3) 19 (12.3) 27 (17.4)

Younger: 18-30 years (154) 107 (69.5) 19 (12.3) 28 (18.2)

Older: 31-75 years (48) 35 (72.9) 7 (14.6) 6 (12.5)

Other screening practices were more common among women, with most reporting

they had engaged in mammography, breast-self-examination, and/or cervical screening.

Only a third of men reported engaging in prostate screening or testicular self-examination.

This is not surprising given that women become used to screening at an earlier age through

cervical screening programs in Australia. In terms of self-examination practices, two-thirds

of women had self-checked their breast health while only just over one quarter of men had

practiced testicular self-examination. As expected, prostate screening and mammography

were much less utilised in this younger sample.

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7.2.2 Family history of CRC

Most participants reported no family history of gastrointestinal illness by blood

relation (that they were aware of). The remainder reported a history of IBS, polyps, or

diverticulitis in their family with almost one-quarter of participants reporting a family

history of CRC. Of participants reporting family history of CRC, they reported a combined

total of 63 CRC sufferers (approximately 1.4 family members per person reporting family

history). The average age of diagnosis of CRC in family members was 61 years (SD = 12),

and age of cancer diagnosis ranged from 35 to 83 years.

7.3 Factor Analysis: Description and Development of the Confirmatory Factor

Analysis Process

Scales modified for the present study, or scales that are not well established, were

factor analysed in order to achieve optimal scale structure for further analysis. Exploratory

factor analyses (EFA) were used to assess the factorability of instruments, while

confirmatory factor analyses (CFA), which enable multiple indicator measurement (Kline,

2005), were used to assess fit of instruments, and establish their reliability and validity.

CFAs assess model fit by evaluating either a measurement model (where factors are

presumed to covary) or a congeneric (single-factor) model in which a single underlying

latent variable is analysed for model fit (Kline, 2005). ‘Indicators’ are simply the items of

the scale, while ‘congeneric’ models have only a single factor and its indicators. Latent

variables refer to the underlying, unobservable variables, which can be captured by one or

more indicators (items of the scale) (Tabachnick & Fidell, 2007). CFA on each

measurement model can establish the importance of indicators to a latent variable by

demonstrating that the items reflect the construct they have been designed to measure, and

this outcome is reached by examining multi-factorial measurement models (Cunningham,

2008). When models are not supported by the data then they can be rejected or re-specified,

and such decisions need to be directed by modification indices (which are suggested by the

software program) as well as theoretical support and evidence (Rust & Golombok, 2009).

EFA was conducted to understand and consolidate the nature of the underlying

processes of unstandardised instruments or poorly fitting confirmatory models, however in

most cases, CFA was more appropriate to test and confirm the underlying latent processes

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as described in the literature (Tabachnick & Fidell, 2007).

A brief description of the key procedures involved in CFA, and the recommended

indices of model fit applied in the present research are provided, followed by the series of

factor analyses in Section 7.4 (see Appendix G for a more detailed description of CFA).

Validation of the theoretical relationships by Pearson product-moment correlations is

presented in the results section (Section 7.5).

7.3.1 Description of processes involved in CFA

CFA using Amos 17.0 was employed to analyse the adequacy of the measures

underlying cognitive, emotive, and social variables. Specification of the measurement

model allows the error variance of the construct to be estimated and states that the error

terms are uncorrelated except in certain designs (for example, repeated measures) (Kline,

2005). Once specified, the measurement model can be analysed by adjudging model fit and

assessing significant relationships.

Each subscale of a factor was first treated as a single-factor measurement model and

was evaluated by Maximum Likelihood CFA and subsequently examined in a measurement

model for the latent variable it was representing. For example, each factor of disgust (core,

animal-reminder, contamination, and medical) was tested for fit before being tested in the

full measurement model for the latent variable, Disgust. This process led to scales that most

accurately reflected the variables they represented. Measurement model analysis is

extensive, and the detailed process is presented in Appendix G. A briefer description is

presented below in Section 7.3.2, while final measurement models are presented in Section

7.4 of the present chapter.

7.3.2 Development of measurement models

7.3.2.1 Assumptions. Missing data, outliers, linearity, and univariate normality were

addressed in Chapter 6, Section 6.3. In addition to these assumptions, Amos produces a

statistic for the measurement model to indicate an approximate departure from multivariate

normality: Mardia’s coefficient of multivariate kurtosis, where values closer to zero

indicate normality (Raykov & Marcoulides, 2006). The criteria accepted in the present

investigation to signify adequate normality is a critical ratio < 3.00 (Wothke, 1993). While

147

this statistic is to be used as a guide, this cut-off will be used to infer non-normality in the

present research (i.e., non-normality will be determined by a Mardia’s statistic > 3.00). This

cut-off justifies the use of the Bollen-Stine p, which is a robust method of calculating the p-

value by correcting for non-normality in the dataset.

7.3.2.2 Model Identification. Proper identification of a measurement model is

important for enabling the most accurate interpretation of results (Tabachnick & Fidell,

2007). Misspecified models will likely result in an inadmissible solution, which may stem

from the exclusion of important parameters (or the inclusion of unnecessary parameters)

(Raykov & Marcoulides, 2006). A further important reason for lack of model convergence

is insufficient model identification (Raykov & Marcoulides, 2006). If a greater number of

parameters are included in the model than there are equations (information from the sample

data), then there are too many ‘unknown’ values, which can lead to an infinite number of

solutions, and there must therefore be an equal or greater number of observations (observed

variables) than there are free parameters (Kline, 2005). Free parameters are unfixed

parameters, and are estimated by the computer program using simple data, whereas fixed

parameters are constant values specified and held constant by the researcher (Kline, 2005).

A further condition necessary for identification includes that each construct has an observed

indicator that is scaled (Raykov & Marcoulides, 2006).

7.3.2.3 Goodness of Fit Indices. The researcher’s goal is to obtain support for the

hypothesised model, which is that the sample model (the researcher’s dataset) does not

differ from the model implied (the implied covariance matrix) (Kline, 2005). If there is

statistical significance (the chi-square p-value < .05) then it can be concluded that the

model implied is significantly different from the researcher’s model, and reasons for this

lack of fit should then be explored. However, the model chi-square test (χ2 or CMIN) is not

an infallible method on which to base the acceptance or rejection of a model (Tabachnick &

Fidell, 2007). There are two main problems associated with χ2: one is that large correlations

in the model can lead to higher values of χ2 (which can hamper model fit), and second,

large sample sizes may lead to false rejection of the model, although the normed chi-square

(CMIN/df) is sometimes used to offset this sensitivity (Kline, 2005). Still, χ2 is the main

determinant and most frequently reported test of fit in the literature.

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A variety of other indices exist (many of which are based on the χ2) that are widely

used and reported in the literature as indications of absolute, comparative and parsimonious

model fit. However, fit indices can also be problematic in determining best model fit. Kline

(2005) outlines the limitations of fit indices: (1) they show overall fit, and ignore that some

parts of the model may be poorly fitting; (2) a single index on its own is not reliable, and a

number of indices should be checked; (3) indices may not indicate theoretical significance

or sense; and (4) indices do not take into account large disturbances (residuals) in the

model, which lessens predictive validity among variables. Overall, evaluation of model fit

should involve an appreciation that alternative models of the variables may also be

specified and attain good fit (Raykov & Marcoulides, 2006). That is, achieving good fit

does not imply that other relationships cannot explain the data equally well.

7.3.2.4. Fit criteria used in the present research. With this in mind, the two main

criteria used to assess reasonable model fit in the present research are a normed chi-square

(χ2) below 3.0 and above 1.0 (Kline, 2005), and a root mean square error of approximation

(RMSEA) below .08 (Browne & Cudeck, 1993; Kline, 2005). Additional commonly used

fit indices will also be indicated to infer model fit. These criteria for model fit are briefly

outlined below.

Absolute Fit Indices

Root Mean Square Error of Approximation (RMSEA). The RMSEA favours

simpler, parsimonious models over those that are more complex, using the noncentral chi-

square distribution (Kline, 2005). This index reveals poor fit (values > .08 or .10) by

measuring the level of falseness of the null hypothesis (Kline, 2005).

Root Mean-square Residual (RMR); Standardised Root Mean-square Residual

(SRMR). The root mean-square residual (RMR) reflects the average value of the covariance

residuals, and should therefore be as close to 0 as possible (Kline, 2005). Because the RMR

computes average residuals using the unstandardised variables, the scales of the observed

variables can have an undue influence on its range and therefore interpretation is difficult

(Kline, 2005). The transformation of the sample covariance matrix and the predicted

covariance matrix into correlation matrices can instead be performed by computing

standardised root mean-square residual (SRMR), which reflects the average correlation

residual (difference between the observed and predicted correlations). The SRMR will be

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reported in the present research (Hu & Bentler, 1999). Values < .10 are recommended (Hu

& Bentler, 1999).

Incremental or comparative fit indices

Comparative Fit Index (CFI). The CFI, like the RMSEA, is also obtained via the

noncentrality parameter, and is the proportion of improvement in noncentrality between the

null and proposed model (Raykov & Marcoulides, 2006). Values > .90 may imply

reasonable fit by way of the researcher’s model chi-square value being less than the degrees

of freedom for that model (Kline, 2005).

Tucker Lewis Index (TLI); Non Normed Fit Index (NNFI). The TLI evaluates the

researcher’s proposed model against a null model (or alternative model) (Schumacker &

Lomax, 2004). Similarly, the NNFI compares against null models, and favours simpler,

more parsimonious models while penalising model complexity. The NNFI converts the chi-

square into a value between 0 and 1.0, taking into account the degrees of freedom (as an

indicator of model complexity) (Raykov & Marcoulides, 2006).

Goodness-of-Fit Index (GFI); Adjusted Goodness-of-Fit Index (AGFI). The GFI

captures the proportion of variance explained by the model. Values closer to 1.0 indicate

perfect model fit while those < .90 suggest poor fit. The adjusted GFI aims to correct the

lower GFI values often produced by very complex models. The GFI and adjusted GFI are

becoming increasingly contentious measures of model fit (Kline, 2005) however in line

with current practice in the literature (Cunningham, 2008; Tabachnick & Fidell, 2007), they

will be reported in the present study.

Parsimonious or predictive fit indices

Akaike Information Criterion (AIC) and Consistent Akaike Information Criterion

(CAIC). The AIC attempts to account for both the degree of fit as well as model complexity

by comparing fit against hypothetical replication samples of the same size and population

(Kline, 2005). Lower values are thought to be better indicators of fit (Raykov &

Marcoulides, 2006), and can be used to select the best model among a range of competing

models (Kline, 2005). In addition to the function of the AIC, the CAIC also takes into

account the sample size, and penalises model complexity less than other similar predictive

indices (Kline, 2005).

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7.4 Scale Development and Refinement: Measurement Models

The final measurement models and their goodness-of-fit indices are presented below,

while the full process for each measurement model is described in detail in Appendix G. As

previously stated, the two main criteria for assessing model fit in the present research is by

a normed chi-square < 3.0 and a RMSEA value < .08 (Browne & Cudeck, 1993; Kline,

2005). Bollen-Stine p was used where necessary to offset non-normality of the dataset.

In CFA, factor items are prevented from cross loading on factors (whereas EFA

allows items to load on all factors in the scale) therefore confirmatory models require the

methodological and theoretical literature to justify the factor structure and specification of a

model. All confirmatory models were estimated using Maximum Likelihood (ML)

estimation and were conducted on the following nine variables: Social Support, Social

Norms, Fear, Self-Efficacy, Test-Efficacy, Screening Bias, Cancer Worry, Risk Perception,

and Screening Intention (while Knowledge was modified via Cronbach’s alpha reliability).

EFA was deemed necessary after an unsatisfactory CFA on three variables, in order

to reach stable factor structure. EFAs were performed in SPSS for Mac, Version 17, on the

following three variables: Disgust, Medical Embarrassment, and Decisional Conflict. This

process, leading to the final measurement models by CFA, is described in Appendix G.

Only the final measurement models (for all variables) are presented below, where latent

variables are represented by ovals, and items of the scales are represented by rectangles.

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7.4.1 Disgust

The initial 3-factor CFA measurement model showed inadequate fit and lack of

discriminant validity. The final factors, with medical-disgust, show adequate fit (see indices

presented in Table 7.4, and Figure 7.1). This final 4-factor (18-item) measure of disgust,

developed from confirmatory and exploratory factor analyses, was subsequently used in

hypothesis testing in Study 1. However, as a result of the generally poor fit of the DS-R, a

new measure will be sought to assess disgust in Study 2. The processes leading to this final

model are documented in Appendix G.

Figure 7.1. Final 4-factor measurement model of disgust with standardised estimates.

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7.4.2. Medical embarrassment

A 3-factor solution for the MEQ was obtained and achieved adequate fit in a

second-order measurement model. The steps leading to this final model are described in

detail in Appendix G. The final measurement model is presented in Figure 7.2, and

goodness-of-fit statistics for the final model are presented in Table 7.4. This final measure

includes 3 factors with 17 items (from an original 2-factor scale of 31 items). This refined

scale is subsequently used in both Study 1 and Study 2 hypothesis testing.

Figure 7.2. Final second-order 3-factor measurement model of the Medical Embarrassment

Questionnaire with standardised estimates.

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7.4.3 Fear of screening

Fear subscales (Fear of Procedural Aspects, Fear of Cancer, and Fear of

Embarrassment), totalling 19 items in the original scale, were first explored in congeneric

models in Amos, and adequate fit was achieved in each. The final 3-factor model of Fear

with a total of 13 items, and which is used in both Study 1 and Study 2 hypothesis testing,

is presented in Figure 7.3 showing the items loading on each subscale. The model reached

adequate fit, χ2(62) = 144.60, Bollen-Stine p = .010. Appendix G details the factor analytic

process of this re-specification, and final goodness-of-fit indices are presented in Table 7.4.

Figure 7.3. Final 3-factor measurement model of the fear scale with standardized estimates.

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Table 7.4

Goodness-of-Fit Indices for Emotion and Social Variable Measurement Models (CFA)

Fit Indices and Ideal Values

Model χ2 df Bollen-Stine p

χ2/dfa RSMEAa 90%CI SRMR CFI GFI AGFI TLI CAIC

>.05 <3.0 <.08 <.10 >.95 >.90 >.90 >.95 Lower values

Disgust Final 241.30 131 .002b 1.84 .06 .052, .077 .06 .91 .89 .85 .90 493.64

MEQ Final 229.00 116 .06b 1.97 .07 .056, .083 .05 .95 .87 .85 .95 462.07

Screening Fear

Final 144.60 62 .01b 2.33 .08 .064, .099 .06 .95 .90 .86 .94 327.54

Social Supportc

Initial 2.90 2 .23 .145 .05 .000, .156 .03 .99 .99 .96 .98 53.37

Social Normsc

Initial 9.06 2 .11b 4.53 .13 .054, .225 .03 .98 .98 .90 .94 59.53

Note. MEQ = Medical Embarrassment Questionnaire. aMain criteria used to determine model fit in the present research. bBollen-Stine p-value which is used when non-normality is indicated by a Mardia’s coefficient > 3.0. cInitial model achieved adequate fit.

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7.4.4 Social support

The 4-item Social Support scale required no modifications and achieved adequate fit

in its unidimensional structure in Amos. Figure 7.4 depicts the four items of social support

used in all subsequent hypothesis testing in Study 1 and 2, and its fit indices are presented

in Table 7.4).

Figure 7.4. Final unmodified measurement model of social support with standardized

estimates.

7.4.5 Social norms

Subjective social norms achieved adequate fit as a unidimensional scale (see Figure

7.5 and Table 7.4) and no modifications were made. This unmodified, 4-item scale is used

in subsequent hypothesis testing in Study 1.

Figure 7.5. Final measurement model of subjective social norms with standardized

estimates.

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7.4.6 Self-efficacy

The 9 items of self-efficacy (the Endoscopy Confidence Questionnaire, ECQ) were

assessed by CFA. A final three-factor model reflects self-efficacy related to FOBt, CS

screening, and CS preparation, with reliability coefficients of .74, .77, and .92, respectively.

The three factors are illustrated in Figure 7.6. This scale is used in hypothesis testing in

Study 1, and will be used as a single unidimensional scale, as its chi-square fit was less than

satisfactory (see fit indices in Table 7.5). The full scale achieved a good reliability

coefficient of .84. Appendix G describes the processes in reaching this measurement model.

Figure 7.6. Final measurement model of self-efficacy with standardised estimates.

157

7.4.7 Test-efficacy

The 5-item test-efficacy scale was modelled as a unidimensional scale and assessed

by confirmatory factor analysis. The model showed adequate fit after the removal of 1 item,

leaving a final 4-item scale. In subsequent analyses, the measure of perceived test efficacy

was analysed as the 4-item measure depicted in Figure 7.7, with fit indices presented in

Table 7.5. The revised Cronbach’s alpha coefficient was .76 (from an initial alpha of .77).

Details of the factor analysis process are presented in Appendix G.

Figure 7.7. Final measurement model of test-efficacy with standardised estimates.

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Table 7.5

Goodness-of-Fit Indices for Cognitive Variable Measurement Models (CFA)


Model χ2 df p-valueb χ2/dfa RSMEAa 90%CI SRMR CFI GFI AGFI TLI CAIC

>.05 <3.0 <.08 <.10 >.95 >.90 >.90 >.95 Lower

values

Self-Efficacy Final 2nd

order 38.49 17 .02b

2.26 .08 .046, .113 .05 .97 .96 .91 .95 158.34

Test-Efficacy Final 2.36 2 1.00 b 1.18 .03 .000, .147 .03 .10 .99 .97 .10 52.82

Screening

Bias Final 9.35 5 .74b 1.87 .07 .000, .130 .03 .99 .98 .95 .98 72.43

Worry Final 3.10 2 .21b 1.55 .05 .000, .159 .02 1.00 .99 .96 .99 53.57

Risk

Perceptionc Initial 24.94 14 .04 1.78 .06 .016, .101 .07 .97 .96 .93 .96 113.26

aMain criteria used to determine model fit in the present research. bBollen-Stine p-value which is used when non-normality is indicated by a Mardia’s coefficient > 3.0. cInitial model achieved adequate fit; Correlated with Social Support.

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7.4.8 Screening bias

Screening bias was originally a 10-item scale, however this was reduced to a 5-item

scale during CFA, and is depicted in Figure 7.8. The five items represent a single

unidimensional scale, which is the scale employed in further Study 1 analyses, and the basis

of the 6-item scale used in Study 2. Appendix G.4 provides a full description of the CFA

process. Goodness-of-fit indices are presented in Table 7.5.

Figure 7.8. Final measurement model of screening bias with standardised estimates.

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7.4.9 Cancer worry

The adaptation of the Breast Cancer Worry Scale (Lerman et al., 1991; on which the

current worry scale is based) warranted CFA. As the only variable with a response format

with fewer than 5-points, the worry scale was estimated using its polychoric correlations,

calculated in SAS, Version 9.1.2. This was because response categories below 5-points

should not be treated as continuous (Cunningham, 2008) and therefore the standard

correlation and covariance matrices could not be analysed in their original 4-item response

format.

A final 4-item model was found to have adequate fit (see Table 7.5), and this scale,

depicted in Figure 7.9, was used in subsequent analyses in Study 1 as a single-factor scale

and for the basis of the Study 2 worry scale (see Appendix G for a description of the CFA

process).

Figure 7.9. Final measurement model of cancer worry with standardised estimates.

161


A variable with just 3 items cannot be tested in a single (congeneric) measurement

model; alternatively, it can be assessed in a model where it is correlated with another

unique variable (D. Meyer, personal communication, April 22, 2009). Therefore, risk

perception was correlated with the social support scale. The error term for item 2 was

constrained to zero as a result of producing a negative error variance (the process leading to

this decision is discussed in Appendix G, and based on Byrne, 2001). See Figure 7.10 for

the final measurement model, and Table 7.5 for goodness-of-fit indices. The 3-item scale

was used in its original format for further hypothesis testing in Study 1.

Figure 7.10. Final correlated measurement model of risk perception (with social support),

with standardised estimates.

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7.4.11 Screening intention

Intention to screen for CRC was also measured by a three-item scale and therefore

required similar treatment to that described for risk perception. Screening intention was

correlated with test-efficacy, achieving adequate model fit (see Table 7.6). The error term

for item 1 was forced to zero to counter negative variance, the process of which is

discussed in Appendix G. The final model is depicted in Figure 7.11, and the original 3-

item screening intention scale remained unchanged for further analyses in Study 1.

Figure 7.11. Final measurement model of screening intention (correlated with test-

efficacy), with standardised estimates

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Table 7.6

Goodness-of-Fit Indices for Outcome Variable Measurement Models (CFA)


Model χ2 df p-valueb χ2/dfa RSMEAa 90%CI SRMR CFI GFI AGFI TLI CAIC

>.05 <3.0 <.08 <.10 >.95 >.90 >.90 >.95 Lower values

Screening Intentionc

Initial 17.75 14 .79b 1.27 .04 .000, .082 .05 .99 .97 .95 .99 106.07

Decisional Conflict

Final 7.94 5 .62b 1.59 .05 .000, .121 .01 .10 .98 .95 .99 71.02

aMain criteria used to determine model fit in the present research. bBollen-Stine p-value which is used when non-normality is indicated by a Mardia’s coefficient > 3.0. cInitial model achieved adequate fit; no modification.

164

7.4.12 Decisional conflict

The five subscales of the Decisional Conflict Scale (DCS) (totalling 16 items in the

original scale) underwent an EFA in SPSS after poor fit in an initial measurement model.

Five solutions were produced before reaching a final model, where 8 items of the DCS

were retained as a unidimensional factor.

A CFA was subsequently conducted to confirm the new 8-item scale, leading to the

deletion of 3 further items (4, 5, 12). The final 5-item unidimensional scale of Decisional

Conflict is depicted in Figure 7.12, and this scale is used in subsequent hypothesis testing in

Study 1. Goodness-of-fit indices are shown in Table 7.6, and appendix G details the

processes in reaching this final model of decisional conflict.

Figure 7.12. Final measurement model of decisional conflict with standardised estimates.

7.4.13 Knowledge

The original 12-item knowledge scale was reduced to a 9-item scale by assessing

Cronbach’s alpha, and removing the items that would improve the scale’s internal

consistency. The three items that were removed (2, 3, 4) improved Cronbach’s alpha from

.45 for the 12-item scale to .53 for the final 9-item scale used in subsequent analyses in

Study 1 (see Appendix G.4 for details about this process).

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7.4.14 Summary of factor analyses

Table 7.7 presents scale properties before and after factor analysis, indicating the

type of factor analysis performed, Cronbach’s alpha coefficients, and number of items

remaining. Table 7.8 presents the new means, standard deviations and observed and

theoretical ranges of the revised scales. Of the dependent variables, screening intention

remained unchanged after CFA while the Decisional Conflict Scale was altered from a 16-

item to a 5-item scale. One scale (the DS-R) was deemed unacceptable for use in Study 2,

as adequate factor structure could not be achieved even after extensive exploration of its

factor structure.

For parsimony, factor analyses and the precise steps undertaken to reach decisions

about scale structure are described in full in Appendix G. All hypothesis testing is

performed using the refined scales in Section 7.5.

166

Table 7.7

Revised Reliability, Scale Items and Inter-Item Correlations for Variables Pre- and Post-Factor Analysis

Pre–factor analysis Post – factor analysis Variable (type of factor analysis) α Number of

items α Number

of items Items retained M inter-

item r Cognitive Variables Screening bias (EFA/CFA) .92 10 .87 5 1, 6, 7, 8, 10 .56 Test-efficacy (CFA) .77 5 .76 4 2, 3, 4, 5 .45 Worry (CFA) .88 6 .85 4 1, 4, 5, 6 .59 Self-efficacy (CFA) .82 9 .84 8 1, 2, 3, 4, 6, 7, 8, 9 .39 Knowledge .45 12 .53 9 1, 5, 6, 7, 8, 9, 10, 11,12 .12 Risk Perception (CFA)a .78 3 N/A N/A 1, 2, 3 .54 Fear (CFA) .94 19 .91 13 .43 Fear procedural .92 9 .88 5 1, 2, 3, 4, 6 .59 Fear embarrassment .89 5 .91 4 1, 2, 3, 5 .70 Fear cancer .84 5 .85 4 1, 2, 3, 4 .59 Medical Embarrassment (EFA/CFA) .97 31 .95 17 .50 Judgement concernc .92 12 .86 6 4, 5, 6, 8, 11, 12 .50 Bodily embarrassmentc .97 19 .93 6 1, 4, 7, 9, 11, 16 .69 Interpersonal embarrassmentb N/A N/A .93 5 6, 8, 15, 18, 19 .73 Disgust (EFA/CFA) .92 25 .85 14 .28 Core disgust .79 12 .73 5 20, 22, 23, 25, 27 .35 Contamination disgust .63 5 .64 3 4, 13, 18 .37 Animal reminder disgust .79 8 .78 6 2, 7, 8, 14, 15, 21 .38 Bowel disgust .87 7 .86 4 1, 2, 3, 7 .60 Social Variables Social support (CFA)a .60 4 N/A N/A 1, 2, 3, 4 .31 Social norms (CFA)a .79 4 N/A N/A 1, 2, 3, 4 .45 Dependent Variables Decisional conflict (CFA) .94 16 .93 5 6, 7, 8, 9, 16 .71 Screening intention (CFA)a .70 3 N/A N/A 1, 2, 3 .44

aDenotes an unchanged factor after factor analysis; bDenotes a new factor identified in factor analysis on bodily embarrassment;

cItems numbered from original subscales.

167

Table 7.8

Means, Standard Deviations, Observed and Possible Ranges for all Scales Post- Factor-Analysis (N = 202)

Variable M SD Observed range Possible range Cognitive Variables Screening bias 12.44 5.04 5 – 23 5 – 25 Test-efficacy 14.75 2.48 5 – 20 5 – 20 Worry 7.64 2.76 4 – 16 4 – 16 Self-efficacy 36.53 8.73 8 – 56 8 – 56 Knowledge 7.43 1.55 0 – 9 0 – 9 Risk perceptiona 7.31 2.16 3 – 12 3 – 15 Fear 43.63 14.85 13 – 78 13 – 78 Fear procedural 17.47 6.79 5 – 30 5 – 30 Fear embarrassment 9.77 5.34 4 – 24 4 – 24 Fear cancer 16.11 5.55 4 – 24 4 – 24 Medical Embarrassment 48.89 15.56 17 – 78 17 – 85 Judgement concern 12.22 5.26 6 – 25 6 – 30 Bodily embarrassment 16.68 6.54 6 – 30 6 – 30 Interpersonal embarrassmentb 12.98 5.97 5 – 25 5 – 25 Disgust 28.77 10.47 4 – 54 0 – 56 Core disgust 9.92 4.19 1 – 20 0 – 20 Contamination disgust 4.84 2.98 0 – 11 0 – 12 Animal reminder disgust 14.00 5.66 1 – 24 0 – 24 Bowel disgust 5.62 3.88 0 – 16 0 – 16 Social Variables Social supporta 14.35 3.33 4 – 20 4 – 20 Social normsa 14.11 3.35 5 – 20 4 – 20 Dependent Variables Decisional conflict 8.25 4.74 0 – 20 0 – 20 Screening intentiona 10.43 2.51 3 – 15 3 – 15

a Denotes an unchanged factor after factor analysis. b Denotes a new factor identified in factor analysis.

168

7.5 Results: Study 1 Hypothesis Testing

The objective of this preliminary research into CRC screening intention was

to understand the relationships between key cognitive and emotive variables with

screening decisions in order to validate the theoretical assumptions about

directionality, and in particular, the measures refined in Section 7.4.

This section therefore reports on the associations of demographic and health

variables; cognitive variables (knowledge, test- and self-efficacy, risk perception,

worry, and screening bias), emotion variables (fear, disgust, embarrassment), and

social variables (social norms and social support), with the dependent variables of

Screening Intention and Decisional Conflict, as hypothesised in Chapter 5. All of the

subsequent analyses were performed using the refined, factor-analysed versions of

the scales, and were performed in SPSS for Mac, Version 17.0, and Amos Windows,

Version 17.0.

Hypothesis 1: The relationships between demographic and health variables with

CRC screening intention and decisional conflict

Eight demographic and health variables were explored by Pearson product-

moment correlations and t-tests to examine their relationships with the outcome

variables, screening intention and decisional conflict. The means and standard

deviations for the demographic and health variables are presented in Tables 7.9 and

7.10, respectively. Table 7.11 displays the correlations between demographic, health

and dependent variables, indicating the demographic and health correlates of

screening intention and decisional conflict. As depicted by this table, having a

history of engaging in both general and CRC screening practices are strongly

associated with intention to screen for CRC in the future. Being partnered was also

strongly and positively correlated with intention. Of note was the lack of relationship

found for gender, health insurance status, or family history of CRC with screening

intentions. Group differences are presented below.

H1(a): Age. As predicted, screening intention significantly increased with

age, r(202) = .21, p = .003, while decisional conflict decreased with age, r(202) =

-.23, p = .001.

H1(b): Partnership status. An independent groups t-test was employed to

compare screening intentions between partnered and un-partnered participants. As

predicted, partnered people reported significantly higher intentions to screen for

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CRC compared to un-partnered people, t(200) = -3.42, p < .001. However, the two

groups did not differ on decisional conflict, t(200) = 1.00, p = .318, only partly

supporting the hypothesis.

H1(c): Health insurance. Health insurance status was collapsed to two groups

(insured, not insured), and the groups compared on dependent variables using an

independent t-test. There was no significant difference in the screening intentions of

participants with insurance or without, t(200) = -0.55, p = .581. There were also no

differences on decisional conflict between the insured and uninsured, t(200) = 1.54, p

= .125 (see Table 7.9 for means and SDs). The hypothesis about health insurance

status and screening was not supported.

H1(d): Gender. An independent groups t-test to determine whether men and

women differed on screening intention revealed, against the hypothesis, no

significant difference, t(200) = -0.66, p = .508. Nor was there any difference between

men and women on decisional conflict, t(200) = 0.93, p = .355.

H1(e): History of general screening. An independent groups t-test to compare

intentions between people with a history of general disease screening versus those

with a history of never having screened (for any cancer) revealed a significant

difference, t(200)= -5.43, p < .001, where prior screening for other cancer was

significantly related to higher CRC screening intention. There was no significant

difference on decisional conflict between those with a history of cancer screening

and those without a history of screening, t(200) = 0.38, p = .706.

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Table 7.9

Means and Standard Deviations for Demographic Variables

Demographic

Variable

Screening intention Decisional conflict

M SD M SD

Partnered

Un-partnered

11.25

10.01a

2.27

2.53

7.78

8.48

5.28

4.44

Insured

Uninsured

10.50

10.30

2.33

2.77

7.84

8.89

4.61

4.89

Male

Female

10.21

10.49

2.56

2.49

8.81

8.08

4.33

4.85

N = 202. aSignificantly different from partnered.

H1(f): History of bowel screening. CRC screening history was collapsed into

any prior screening and no prior screening. As expected, participants with a history

of screening for CRC reported greater CRC screening intentions than those who had

never screened for CRC, t(200) = -3.86, p < .001. However, participants with a

history of screening for CRC did not differ in decisional conflict from those with no

previous CRC screening, t(200) = 1.66, p = .098.

H1(g): History of gastrointestinal condition. There was no significant

difference in the screening intentions of participants with a history of gastrointestinal

problems (e.g., IBS) compared to those without gastrointestinal problems, t(200) = -

1.93, p = .055. Nor was there a difference in the total decisional conflict reported by

participants with poor bowel health compared to those participants without a history

of gastrointestinal problems, t(200) = 1.02, p = .311.

H1(h): Family history of bowel cancer and gastrointestinal conditions. There

was no difference in reported screening intentions for those participants with a

family history of bowel health problems or CRC compared to those without a family

history, t(200) = -1.84, p = .067. There was no difference in reported decisional

conflict for those with a family history of gastrointestinal illness or CRC compared

to those without a family history, t(200) = 1.33, p = .187.

171

Hypothesis 1(a) to 1(h) was therefore only supported in part, with age,

partnership, general screening history, and CRC screening history all associated with

greater screening intentions.

Table 7.10

Means and Standard Deviations for Health Variables

Health Variable

Screening intention Decisional conflict

M SD M SD

Screened (any)

Not screened (any)

10.95

8.90a

2.31

2.44

8.17

8.46

5.07

3.62

CRC screened

CRC not screened

11.43

9.99b

2.09

2.55

7.41

8.63

5.36

4.41

GI history

GI no history

11.00

10.23c

2.71

2.41

7.67

8.47

5.41

4.48

Family history

No family history

10.95

10.23

2.58

2.46

7.54

8.52

5.15

4.56

Note. GI = Gastrointestinal.

N = 202.

aSignificantly different from participants with screening history.

bSignificantly different from participants with CRC screening history.

cApproached significance between participants with and without a history of GI illness.

172

Table 7.11 Correlations Between Demographic Variables, Health Variables, and Screening Intentions

Variable 1 2 3 4 5 6 7 9 8 9 10 11 12 13 14

1 Screening intention

2 Decisional conflict -.32**

3 Age .21** -.26**

4 Poor GI health .20** -.10 .25**

5 Screening history .41** -.28** .53** .49**

6 Bowel screening history .30** -.27** .36** .57** .79**

7 Female screening history .37** -.15 .50** .27** .82** .38**

8 Male screening history .41** -.26 .52** .11 .67** .04 a

9 Family GI illness .14** -.09 .21** .39** .24** .25** .16* -.06

10 Peer-health perception .12 -.10 .04 .07 .05 .04 .10 -.01 .05

11 Good subjective health .13 -.14* .15* -.10 .10 .08 .13 .17 -.10 .28**

12 Marital status .23** -.13 .37** .10 .32** .20** .31** .37* .13 .13 .19*

13 Employment status -.01 -.01 -.02 -.05 -.04 -.06 -.00 -.21 .05 -.11 -.14 -.17* .08

14 Health insurance status .10 -.18** .14* .10 .21** .15* .15 .33* .06 .03 .15* .15* -.17*

15 Gender .05 -.09 .08 .09 .25** .08 .54** -.46** .10 .01 -.11 .02 -.08 .03

Note. Correlations after factor analysis on Screening Intention and Decisional Conflict (Demographic and Health variables were not factor analysed).

Good GI health = 0 (where GI = gastrointestinal); No prior screening participations (non-bowel cancer) = 0; Good family GI health = 0; Martial Status: partnered = 1;

Gender: Male = 0; Female = 1; Health insurance status: No insurance = 0; Some insurance = 1;

ªnot computed because at least one variable held constant.

N = 202. Correlations with male screening history = male only (n = 47); Correlations with female screening history = female only (n = 155).

Two-tailed significance * p < .05. **p < .01.

173

Hypothesis 2: The relationships between cognitive variables with bowel screening


All Pearson correlation coefficients between cognitive variables, screening intention, and

decisional conflict, are presented in Table 7.12. All cognitive correlates of screening

intention were significant.

While a positive correlation between risk perception and screening intention was

found, supporting the hypothesis, this relationship was weak. Against predictions,

perception of CRC risk was unrelated to decisional conflict. Therefore, H2(a) was only

partially supported.

A weak positive and significant relationship was observed between knowledge of

CRC and screening intention, however there was no significant relationship between

knowledge and decisional conflict, partially supporting H2(b).

Stronger beliefs that CRC screening tests are effective (test-efficacy) were

moderately related to higher intentions to screen. There was also a significant relationship

between stronger test-efficacy beliefs and lower decisional conflict, albeit only weak.

Therefore, H2(c) was supported.

There was support for H2(d), indicating that a strong and significant correlate of

screening intention was a belief about being able to confidently engage in CRC screening

tests (self-efficacy). Decisional conflict was significantly related to lower self-efficacy.

Partial support was observed for H2(e), with worry about CRC being significantly

but weakly related to higher screening intention, and unrelated to decisional conflict.

H2(f) and H2(g): Partial correlation hypotheses

As expected, perception of CRC risk was significantly and strongly related to

worry about CRC and weakly to screening intention, both positively. A partial correlation

revealed that when worry is held constant, risk perception is no longer correlated

significantly with screening intention r(199) = .11, p = .13., supporting H2(f), and

indicating that risk perception only has a positive relationship with intention when there is

also cancer worry.

Screening bias was significantly and strongly related to lower screening intention,

and was also strongly and positively related to decisional conflict (where more biases were

associated with greater levels of conflict). A partial correlation was performed between

screening intention, screening bias, and knowledge. As hypothesised, when knowledge is

174

held constant, distorted beliefs about the value and necessity of screening (i.e., screening

bias) continue to show a strong and unique negative relationship with intention to screen

for CRC, r(199) = -.41, p < .001. These findings support Hypothesis 2(g).

In summary, there were five cognitive variables moderately to strongly associated

with screening intention: self-efficacy; test-efficacy; knowledge; and worry (positively),

and screening bias (negatively). Risk perception was only related to intentions when worry

was present, while screening biases maintained a significant and strong relationship with

intentions when controlling for knowledge about CRC. Risk perception, knowledge, and

cancer worry were unrelated to decisional conflict about intentions.

175

Table 7.12

Bivariate Pearson Correlations Between Cognitions, Screening Intention, and Decisional Conflict

Variable Screen

Intention

Decisional

Conflict

Risk

Perception

Knowledge Test-

efficacy

Self-

efficacy

Worry

Decisional conflict -.21**

Risk perception .18** .04

Knowledge .24** -.13 .12

Test-efficacy .26** -.20** -.04 .16*

Self-efficacy .52** -.29** .04 .22** .29**

Worry .21** -.03 .42** -.06 -.06 -.08

Screening bias -.45** .28** -.09 -.27** -.17* -.23** -.13

N = 202. Two-tailed significance *p < .05. **p < .01.

176

Hypothesis 3: The relationship between emotion variables with CRC

screening intention and decisional conflict

All Pearson correlation coefficients between emotion, with screening intention and

decisional conflict are presented in Table 7.13.

Against predictions in H3(a), no factors of the Disgust Scale-Revised were

related to screening intention however, stronger disgust in relation to bowel-specific

aspects of CRC screening were moderately and significantly related to lower CRC

screening intention. There were only very weak (but significant) relationships with

decisional conflict between animal reminder disgust and bowel-specific disgust.

Therefore H3(a) was only supported in part.

As anticipated, there were significant moderate negative relationships

between intention to screen for CRC and medical embarrassment, but only on

interpersonal and bodily embarrassment (not with judgement concern). Judgement

concern was unrelated to the experience of decisional conflict, however there was a

small and significant positive relationship between decisional conflict and both

interpersonal and bodily embarrassment. Therefore, in relation to screening

intentions H3(b) was almost fully supported, but only somewhat supported in

relation to decisional conflict.

There was a small degree of support for H3(c). Only fear of the screening

procedure was significantly related to lower screening intentions, however all three

factors of screening fear (procedural, and fear of embarrassment and cancer) were

positively and significantly (but only weakly) associated with decisional conflict.

In summary, of the 10 subscales (of medical embarrassment, disgust, and

fear) four were significant negative correlates of screening intention. Screening

intention was significantly, negatively related to interpersonal embarrassment, bodily

embarrassment, fear of procedural aspects, and bowel disgust, while the subscales of

the Disgust Scale-Revised (DS-R) were unrelated to screening intention.

Decisional conflict showed significant but positive relationships with the

same set of variables, however in addition, animal-reminder disgust (reminders of

animal origin), fear of cancer, and fear of embarrassment were weakly associated

with higher decisional conflict. Therefore, hypotheses 3(a) to 3(c) were partially

supported, with factors of Medical Embarrassment in particular (interpersonal

embarrassment; bodily embarrassment) being stronger, significant negative

177

correlates of screening intention. Decisional conflict was strongly associated with

fear of procedural aspects and bowel disgust.

178

Table 7.13

Bivariate Pearson Correlations Between Emotion and Dependent Variables

Variable Screen Intent

DCS 1 2 3 4 5 6 7 8 9 10 11 12

1 DS-R -.13 .09

2 Animal reminder -.13 .15* .89

3 Core -.09 -.02 .83 .61

4 Contamination -.08 .05 .65 .41 .37

5 Bowel-specific -.34** .21** .62 .54 .58 .34

6 MEQ -.25** .17* .57 .50 .51 .33 .69

7 Judgement concern -.12 .08 .38 .24 .40 .29 .49 .81

8 Bodily emb -.24** .19** .57 .56 .46 .30 .59 .89 .54

9 Interpersonal emb -.28** .16* .52 .47 .47 .28 .72 .91 .63 .75

10 Fear -.22** .23** .50 .46 .40 .32 .57 .65 .49 .59 .62

11 Fear procedural -.28** .24** .47 .45 .37 .29 .52 .54 .34 .52 .54 .88

12 Fear cancer -.10 .17* .42 .41 .29 .29 .40 .46 .32 .44 .42 .78 .59

13 Fear emb -.12 .15* .28 .21 .29 .19 .46 .57 .57 .44 .51 .75 .48 .38

Note. DCS = Decisional Conflict Scale; emb = embarrassment. On the dependent variable columns: Two-tailed significance * p < .05. ** p < .01. All other bivariate Pearson

correlations between predictor variables are significant at p < .01, and thus not denoted in the table.

N = 202.

179

Hypothesis 4: The relationship between social variables with CRC screening intention

and decisional conflict

Correlation coefficients between social variables and the dependent variables are

presented in Table 7.14.

There was no support for H4(a). Social norms were unrelated to either dependent

variable, and showed little inter-correlation with any of the predictor variables.

Perceptions of functional social support were significantly related to both

dependent variables. Support was moderately and positively related to screening intention,

and negatively but weakly associated with decisional conflict, supporting H4(b).

Table 7.14

Bivariate Pearson Correlations Between Social Support, Social Norms, and Dependent

Variables

Variable Screen

Intent

DCS DS-R MEQ FEAR SS

Social support .36** -.15* -.04 -.18* -.12

Social norms .08 .06 .10 .09 .14* .22**

Note. DCS = Decisional Conflict Scale, SS = Social support.

N = 202. Two-tailed significance *p < .05. **p < .01.

180

Hypothesis 5: Mediation of social-cognitive variables on negative emotions

and screening intention.

As the Disgust Scale-Revised was unrelated to screening intention, hypothesis

5(a) was not tested. This is because a significant relationship between the initial

(independent) variable and the dependent variable is a necessary precursor for assessing

mediational relationships (Baron & Kenny, 1986). Therefore, hypothesis 5(a) is

unsupported.

Given that social norms showed no correlation with either the primary

dependent variable or any of the total scores on emotion variables (except a weak

correlation with total fear scores), social norms cannot be a mediator (Baron & Kenny,

1986) and this variable was removed from further investigation.

To address Hypotheses 5(b) and 5(c), mediation analyses were conducted in

Amos (Version 18.0) to determine whether two types of emotion (fear of screening,

medical embarrassment) have an effect on screening intention when self-efficacy and

social support beliefs are present. Appendix H presents a full description of these

analyses.

H5(b): Screening Fear. Hypothesis 5(b) was partially supported. There was

partial mediation of the relationship between fear and screening intention by self-

efficacy. Thus, the direct path between fear and intention was partially reduced by self-

efficacy, but remained significant, Chi-Square = 8.54, df = 1, p = .003.

The effect of fear on intention was also partially mediated by social support,

Chi-Square = 4.9, df = 1, p = .027. Figure 7.13 depicts these relationships.

181

Figure 7.13. Partial mediation by self-efficacy (SE) and partial mediation by social

support (SS) on the relationship between screening fear and screening intention.

H5(c): Medical Embarrassment. A mediation analysis revealed that the effect

of medical embarrassment on screening intention was completely mediated by beliefs of

self-efficacy to screen, where the direct path from embarrassment to intention is not

significant when self-efficacy is present, Chi-Square = 1.47, df = 1, p = .225.

Social support was found to partially mediate the relationship between medical

embarrassment and screening intention, with the direct path being significant, but

reduced, when there is perceived social support to screen, Chi-Square = 8.18, df = 1, (p

= .004). These relationships are depicted in Figure 7.14.

Figure 7.14. Partial mediation of social support (SS) and full mediation of self-efficacy

(SE) on the relationship between medical embarrassment and screening intention.

Medical

Embarrassment Screening Intention

Self-Efficacy

Social Support

On SE β = -.56, p<.001 On SS β = -.24, p=.05

SE β = .72, p<.001 SS β = .29, p<.01

β = .11, p = .23 (in SE model) β = -.21, p < .01 (in SS model)

Medical Embarrassment

Fear of

Screening Screening Intention

Self-Efficacy

Social Support

On SE β = -.68, p<.001 On SS β = -.19, p=.05

SE β = .89, p<.001 SS β = .28, p<.001

β = -.36, p=.009 (in SE model) β = -.19, p=.02 (in SS model)

Screening Fear

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7.6 Summary of Findings

There were significant associations between all the cognitive variables with

screening intention, where self-efficacy and screening bias in particular were strong

positive and negative correlates, respectively. Similarly, self-efficacy and screening bias

were strongly negatively and positively related to the secondary dependent variable,

decisional conflict, respectively. Risk perception of CRC was positively but weakly

related to screening intention, and only when cancer worry was present. Screening bias

had a unique significant and strong negative relationship to intentions even when

controlling for the effect of CRC knowledge.

Screening intention was strongly related to the negative emotions of procedural

fear, bowel-specific disgust, and interpersonal and bodily embarrassment. Some of these

emotions were also partly explained by the relationship between self-efficacy beliefs

(fully mediating the relationship between embarrassment and intention, and partially

between fear and intention) and a perception of functional social support from family

and friends (partially mediating the relationship between medical embarrassment and

intention, and between fear and intention). A small but significant negative association

existed between decisional conflict and screening intention.

While these results assist in validating the measures, these relationships are

exploratory at best, given the average sample age recruited for this preliminary study.

There are however, implications of the current findings for further research with a

community sample. Thus, a second study conducted with a sample drawn from the

community is warranted for substantiation of these direct relationships in an age-

targeted sample. Findings of Study 1 are discussed in Chapter 11 in relation to the

literature.

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CHAPTER 8

METHODOLOGY AND PSYCHOMETRIC PROPERTIES OF SCALES IN

STUDY 2


This chapter details the methodology and data preparation of Study 2, conducted

as a self-report survey design in a community sample of Australians. Preceded by a

series of analyses from a preliminary study described in Chapters 6 and 7, a shorter,

refined questionnaire was designed and administered to reflect key components of the

cognitive variables (risk perception, self-efficacy, test-efficacy, knowledge, worry,

bias), emotive variables (fear, medical embarrassment, disgust) and social variable

(social support) of the original design, to better understand screening intention and

decisional conflict, and also screening behaviour in a sample of older Australians. The

method section described below (Section 8.2) only reports on the amendments and

additions to the survey and the refined measures (post-factor analysis) that are used in

Study 2. The assumptions underlying discriminant function analysis are then explored

in Section 8.3, in addition to an assessment of the psychometric properties of the scales.

The aims, rationale, and hypotheses are presented in Chapter 9, and results are

presented in Chapter 10.

8.2 Method

8.2.1 Participants

With the only inclusion criteria being an Australian resident and 35 years of age

or older, the sample in this study comprised 240 Australians, predominantly from

Australian metropolises (84%; with 55% from the Melbourne metropolitan area).

Although deliberate effort was made to recruit a larger number of males in the present

study, two-thirds of participants were women (n = 160; men n = 80). The average age of

participants was 59.5 years (SD = 11.9 years, range = 35 to 87 years). Sample

characteristics are described in further detail in Section 10.2.1, Chapter 10.

8.2.2 Materials

During the preliminary study, several changes were made to the survey based

on: (a) the refinement (using factor analysis) of a number of scales due to concerns

about psychometric soundness and poor factor structure. These were described in

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Section 7.4, Chapter 7, and in appendix G; and (b) the removal of three variables (social

norms, subjective health status, perception of peer health status) due to lack of

magnitude in association with the outcome variables. The reliability coefficients

attained in the present study are reported in Table 8.1, Section 8.4.

8.2.2.1 Demographic and health measures

Demographic measures. Demographic status was assessed by 7 items, which

were identical to those administered in the pilot study (Section 6.2.2.1, Chapter 6).

Health insurance was assessed as: not insured; hospital insurance only; and full hospital

and extras insurance. In addition, postcodes were collected to check for geographical

spread, including rural versus urban respondents, and to operationalise socioeconomic

status based on the 2006 Index of Relative Socio-economic Disadvantage (IRSD)

(Australian Bureau of Statistics, 2008). Postcodes were categorised into two groups:

lowest SES deciles (deciles 1-4) and highest SES deciles (deciles 8-10), with the middle

group comprised of 44 uncategorised postcodes.

Health measures. Seven variables, comprising 20 items were measured. Two

unchanged variables from the preliminary study included Screening Advice and

Competing Medical Condition (both dichotomous variables scored as yes/no).

Gastrointestinal Health (6 items) was similar to the items used in Study 1 but

for 4 additional items relating to bowel health (Crohn’s disease, coeliac disease, lactose

intolerance, and diverticulitis). These were in addition to the 2 items relating to polyps

and IBS in Study 1. Respondents again answered yes or no to these items and scores

were then collapsed to indicate no gastrointestinal concern (0), or one-or-more

gastrointestinal health concerns (1).

History of CRC Screening (4 items) excluded an item from Study 1 on digital

rectal examination, but was otherwise unaltered and treated as dichotomous (screened

for CRC or not screened). History of General Screening (4 items for women, 3 items for

men) was altered by the addition of one item for both women and men (skin cancer

checks). A single scale was used for the full sample where higher scores indicated

greater participation in general screening practices. Two subscales were also created for

men’s history of screening and women’s history of screening separately.

Family health history was measured by 3 variables with one item in each

(reduced from 7 items used in Study 1). Family CRC history was requested, with

response ranges including ‘no’, ‘yes’, and ‘don’t know’. Family Gastrointestinal Health

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was measured by one item, “Has anyone in your family had a gastrointestinal infection

or disorder?”, with the response options, ‘no’, ‘yes’, and ‘don’t know’. All ‘don’t know’

responses were re-coded as ‘no’ and the first two scales were treated as dichotomous.

The third item was a continuous variable, which asked participants to report the number

(if any) of family members who have had CRC.

8.2.2.2 Cognitive measures

Risk Perception. Risk perception comprised four items, using a 5-point scale

from 1 = “very low chance (far below average”) to 5 = “very high chance (far above

average)”, with scores summed so that higher scores reflected perceptions of higher

CRC risk. Scores were possible between the range of 4 to 16.

Three items (from Study 1) were returned to their original format (for the age-

appropriate sample). A new item was included to assess global risk perception

(“Overall, my future risk of getting bowel cancer is”). Item 1 was “The chance that I

will get bowel cancer at some point in my life is”, item 2 was re-worded as: “The

chance that I will get bowel cancer compared to most people my age and sex is”

(Cameron & Diefenbach, 2001), and item 3, “The chance I will get bowel cancer

compared to anyone else is” (Kremers et al., 2000). Reliability Cronbach (α)

coefficients for items 1 and 2 were reported as .80 (Cameron & Diefenbach, 2001), and

item 3 is derived from a scale with a coefficient of .73 (Kremers et al., 2000). Internal

consistency in Study 1 was .78 (Cronbach’s α).

Screening Bias. Six items were assessed, reduced by factor analysis from 10

items in Study 1. Responses were rated on a 5-point Likert scale ranging from 1 =

“strongly disagree” to 5 = “strongly agree” to the same items assessed in Study 1,

following this sentence stem: “Please rate how much the following reasons would make

you LESS LIKELY to screen for bowel cancer”. The sixth item was re-examined in the

current, older sample, (“if I felt too healthy to have bowel cancer”), which was removed

in Study 1 due to potential item overlap (see Appendix G.3.4). Items were summed, and

scores could range between 5 and 30, with higher scores reflecting stronger screening

biases related to the presence of symptoms, gender influences, hereditary influences,

and co-morbidity. Psychometric properties of the five-item scale were excellent in

Study 1, including a Cronbach’s alpha of .87 and a mean inter-item correlation of .56.

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Test-Efficacy. A six-item Test Efficacy inventory was administered to assess

participants’ belief in the effectiveness of CRC testing. Items 1, 2, 3, 5 and 6 were

adapted from Myers and colleagues’ (1995) measure of prostate screening test-efficacy

(re-phrased for CRC-relevance), while item 4 was adapted from Tiro et al. (2005).

Four items were retained from the test-efficacy scale administered in Study 1

(see Section 6.2.2.2, Chapter 6), with two additional items (5 & 6) from Myers et al.

(1995), believed appropriate for the target sample of older adults: “I believe that bowel

screening is an effective way to find bowel cancer early” and “I believe that I can

protect myself from bowel cancer by going through bowel cancer screening”. Scores

were summed so that higher scores reflect greater belief in the efficacy of the test.

Confirmatory factor analysis of the test-efficacy scale used in Study 1 showed

that item 1 was ineffective in explaining the underlying dimension, test-efficacy. Upon

reviewing its surface structure, it was believed this item had poor validity because stool

tests are designed to test for haemoglobin, which may indicate bowel cancer (rather than

detect bowel cancer per se). Instead, item 1 was modified in the present study to assess

stool test-efficacy without implying the ability of the stool test to directly detect cancer.

Thus, the item changed from “The stool test effectively detects bowel cancer” to

“Overall, the results of stool tests are accurate”. The scale used in Study 1 achieved a

Cronbach’s alpha of .76. The current 6-item scale is expected to increase the

measurement validity of test-efficacy with improved face-validity and surface structure.

Bowel Cancer Knowledge. The 12-item Knowledge scale (The PROCASE

knowledge index; Radosevich et al., 2004) administered in Study 1 was re-administered

in the present investigation and is outlined in detail in Section 6.2.2.2, Chapter 6. In this

study, the original “True” / “False” format was modified to include a third response

option (“Don’t know”), where each correct answer accrued a score of one, while “don’t

know” and incorrect answers were accorded zero. Scores were summed, and higher

scores indicated greater CRC knowledge. The full original knowledge scale in Study 1

reached an initial Cronbach’s alpha of .45 and a subsequence coefficient of .53 as a 9-

item scale as a result of deleting items to improve the internal consistency of the scale.

Although low, there are few established knowledge scales for cancer screening, and this

alpha approaches published alpha levels (Hevey et al., 2009; Radosevich et al., 2004).

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Bowel Cancer Worry. The original 6 items of the Worry scale were reduced to 4

items after factor analysis in Study 1, and this 4-item inventory was employed to assess

worry in the present study. Statements were prefaced by the instruction: “Please indicate

how much you think or worry about bowel cancer”, with responses on a 5-point Likert

scale (1 = “not at all” to 5 = “almost all the time”). Scores were summed, and could

range between 4 and 20, with higher scores indicating greater worry about bowel

cancer. Internal consistency was good, indicated by a Cronbach’s alpha of .85. A

detailed description of the worry scale is provided in Section 6.2.2.2, Chapter 6.

Self-Efficacy. The 9-item Endoscopy Confidence Questionnaire (ECQ; Gattuso

et al., 1992) used in Study 1 was re-administered to assess Self-Efficacy in the present

study. While 1 item was made redundant in Study 1, the psychometric properties of the

full 9-item scale were good (including internal consistency assessed by Cronbach’s

alpha; α = .82; and α = .84 as an 8-item scale). Therefore the scale was re-administered

in full in the present study (described in Section 6.2.2.2, Chapter 6). Scores could range

between 9 and 63, with higher scores reflecting greater confidence about being able to

effectively engage in CRC screening preparation and screening.

8.2.2.3 Emotion measures

Emotion scales were administered at the start of the survey. This was done to

reverse the order of their presentation from that in Study 1, to determine whether the

length of the survey (and not the content of the emotion scales) may have been the

source of high attrition rates in the preliminary study. (See Section 8.3.2 for this

evaluation.) The emotion measures were as follows.

Fear of Screening. Fear of screening was measured by 13 items on a 6-point

scale, tapping into three unique but related sources of fear: Fear of Procedural Aspects;

Fear of Embarrassment; and Fear of Cancer. Instructions to the items were, “Some

people have reported the following scenarios as frightening. Please rate how much these

scenarios might cause you to feel afraid”, with response options ranging from 1 = “not

at all frightening” to 6 “extremely frightening”. Higher scores on each subscale

indicated higher levels of that type of screening-related fear.

The modified scale was used in Study 2. While 8 items (derived from Harewood

et al., 2002) were piloted for the original subscale of Fear of Procedural Aspects in

Study 1, exploratory factor analysis reduced the scale to the 5 items administered in the

present study. Scores on this subscale could range from 5 to 30. Fear of Embarrassment

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contained 4 items from Smith and colleague’s (2005) review of barriers to screening for

CRC, which was reduced from 5 items in Study 1. Fear of Cancer (also derived from

Smith & colleagues’ review, 2005) contained 4 items (originally 5 items in Study 1).

Scores on the latter two subscales could each range from 4 to 20. In Study 1 the

reliability coefficient (Cronbach’s alpha) for each of these modified subscales was .88,

.91, and .85, respectively. A full description of the original scale is provided in Section

6.2.2.3, Chapter 6, and the factor analysis in Appendix G.2.3.

Disgust. The 30-item Disgust Emotion Scale (DES; Walls & Kleinknecht, 1996)

was administered instead of the Disgust Scale-Revised (DS-R) which was used in Study

1. Additionally, the four-item bowel-specific disgust scale (Bowel Disgust) piloted in

Study 1 (and described in Section 6.2.2.3, Chapter 6) was re-administered in its

modified form, and its items are depicted as part of the Disgust measurement model in

Figure 7.1, Section 7.4, Chapter 7.

The DS-R (Haidt, McCauley, & Rozin, 1994, modified by Olatunji, Williams et

al., 2007) administered in Study 1 did not demonstrate factor structure in accordance

with previous published findings of the scale. The DS-R was originally employed as its

factors appeared especially pertinent to the context of CRC screening. The DES

however, has a history of sound psychometric properties and strong internal consistency

(Olatunji, Sawchuk, de Jong, & Lohr, 2007), and as it is more succinct, may be more

suitable in a lengthy community-based survey.

Olatunji, Sawchuk, and colleagues (2007) contrasted the DS and DES,

ascertaining superior reliability in the DES, with good internal consistency (Cronbach’s

alpha = .90 and .91; Olatunji, Sawchuk et al., 2007) and convergent validity across two

studies. A CFA supported its five-factor structure, which include disgust associated

with rotting food; piercing of the body (injection, blood draw); odours; small animals

(e.g., slug, rat, snake); and mutilation / death. The items are preceded by the

instructions, “The following situations are known to cause some people to experience

disgust, revulsion, or repugnance. Please rate how much disgust or repugnance you

would experience if you were exposed to each situation at this time”. Examples of the

items include, “The smell of a public restroom”; “Having blood drawn from your arm”,

and “The smell of vomit”. The DES has a 5-point Likert response format from 0 = “no

disgust or repugnance at all” to 4 = “extreme disgust or repugnance”, with higher scores

reflecting greater sensitivity to disgusting stimuli. To date the DES had not been

examined in the context of CRC screening.

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Medical Embarrassment. The Medical Embarrassment Questionnaire (MEQ;

Consedine et al., 2007) was re-administered to assess embarrassment. As a result of

factor analysis in Study 1, the initial 31-item scale was modified from its original 2-

factor structure to a 3-factor solution comprising 17 items. This format was used in the

present study, reflecting bodily embarrassment, judgement concern, and a new factor

called interpersonal embarrassment, which was posited to reflect embarrassment

experienced during direct exchange or conversation about health concerns with a

medical professional.

Instructions and response format were identical to Study 1, including the

sentence stem, “Some people have reported that the following scenarios can be

uncomfortable or humiliating. Please rate how true this would be for you in the

situations described below”. Examples of items include, “Showing my body to a

stranger, even a doctor, is humiliating”, “Describing my bowel movements to a doctor

is awkward for me”, and “Talking with a doctor about how frequently I use the

bathroom and the nature of my faeces or stool is difficult for me”. Higher scores, which

could range from 17 to 85 for the total 17-item refined scale, indicate higher levels of

medical embarrassment. An excellent Cronbach’s alpha of .95 was attained in Study 1

for the final 17-item scale, and the subscales of bodily embarrassment, judgement

concern, and interpersonal embarrassment had reliability coefficients of .93, .86, and

.93, respectively.

8.2.2.4 Social measures

Social Support. The social support measure is described in detail in Section

6.2.2.4, Chapter 6, and was unmodified by factor analysis as it achieved adequate model

fit in its original form as a unidimensional scale. Although Kremers and colleagues

(2000) did not report the internal consistency of the scale, Study 1 revealed this scale

had an adequate Cronbach’s alpha of .60, with a mean inter-item correlation of .31.

Social Norms were not assessed in Study 2 as they showed no association with

either of the dependent variables in Study 1, nor with the predictor variables (except for

social support, and a very weak negative association with animal-reminder disgust and

fear of cancer). As one of the goals of Study 1 was to refine and shorten the survey for a

community sample, this variable was excluded.

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8.2.2.5 Dependent measures

Screening Intention. Screening intention was the primary dependent variable in

the survey, and was measured by five items, including the original 3-item scale used to

assess intentions in Study 1 (in which a Cronbach’s alpha of .70 was obtained). The

original scale measured intent to screen in general, by colonoscopy, and by FOBt.

Instead of asking participants what they want to do about screening (Study 1), two

additional items in the present study measured proposed actions, “Do you intend to be

screened for bowel cancer by stool testing?” and “Do you intend to be screened for

bowel cancer by colonoscopy?” Participants responded on a Likert scale from 1 =

“definitely not” to 5 = “definitely yes”. For the purpose of exploring relationships by

correlation, FOBt intentions (2 items) were summed, as were CS intentions (2 items).

For the purpose of multivariate analysis, two of the items were collapsed to

measure FOBt intention (or non-intention), and a further two items collapsed to assess

colonoscopy intention (or non-intention). “Probably” and “definitely” intend to screen

responses were collapsed to reflect screening intention, while “definitely not”,

“probably not” and “don’t know” responses were treated as reflecting non intention.

Screening Participation. Bowel cancer screening participation was a main

dependent variable, and was measured retrospectively. Items were prefaced by the

question, “Have you ever done, or had a doctor perform, any of the following?”.

Participants were asked to respond “yes” or “no” to 4 items about prior screening by

barium enema (colon x-ray), FOBt, flexible sigmoidoscopy, or colonoscopy. All “yes”

responses were collapsed into “screened for CRC”, while “no” responses across all

types of screening were collapsed to “never screened for CRC”.

Decisional Conflict. Decisional conflict was the secondary dependent variable

in the survey, and as in Study 1, was measured by the Decisional Conflict Scale (DCS).

Higher scores reflect higher decisional conflict. Three subscales of the DCS

(comprising 10 items in total) were used in the present study, reduced by factor analysis

in Study 1 from the full 16-item scale. These 10 items reflect 3 of the original 5

subscales: uncertainty (items 1-3), ineffective decision-making (items 4-7) and feeling

uninformed (items 8-10); while the clarity of values and support subscales were

excluded due to poor factor structure in Study 1 and overlap with the social support

measure. Although 5 items were found to produce the best fit in CFA in Study 1, the 10

items were used to enable the full three subscales to be assessed in the present study.

For correlations, the DCS scale was summed and treated as a continuous variable. For

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discriminant analysis, the scale was dichotomised by a median split into low decisional

conflict/high decisional conflict. The published psychometric properties of the DCS are

described in Section 6.2.2.5, Chapter 6.

8.2.3 Procedure

The survey was advertised as a 20-minute self-report questionnaire on

“Australian attitudes toward screening for bowel cancer”, and could be completed either

online or by hard copy. A consent information statement was attached at the front of the

survey, stating the purpose of the study, types of questions involved, ethical rights of

participants, contact details of the researchers and ethics committee, and contact details

for university health and psychology services (see Appendix J). The online survey was

powered by Opinio Survey SoftwareTM

(ObjectPlanet Inc., 1998-2011), and was open

from 20 April 2009 until 31 May 2010, however active participant recruitment occurred

over an eight-month recruitment drive from June 2009 until February 2010.

Participants were sampled from the Australian community by advertising on

university (Swinburne faculty and staff sites), government health (e.g., VicHealth), and

interest-group websites and newsletters, such as the University of the Third Age (U3A)

newsletter and U3A campus noticeboards, word of mouth or ‘snowballing’ techniques,

and by press release through the Swinburne Media centre (see Appendix K for the full

list of sources). A website was also designed to allow participants to view information

about the investigators and goals of the study (www.bowelscreenresearch.com) (see

Appendix K for snapshots of the website pages), and to assist participants to locate the

online version of the survey, using a simpler web address than that created by Opinio.

Direct invitations to participate included the provision of surveys and reply-paid

mail-back envelopes across university campuses (e.g., at U3A), and the distribution of

small ‘calling cards’ (i.e., akin to business cards) and A5 posters, which provided the

research project website address, a brief overview of the survey, and contact details of

the investigators (Appendix K). To specifically encourage men to participate, male-

predominant workplaces were approached, and employees at both the Australia Post

engineering department in Brisbane, and the annual Qantas Training Pilot meeting in

Melbourne were invited to take part.

Online participants (77% of the final sample) took an average of 21 minutes to

complete the survey. The remaining 54 participants returned hard copy surveys, which

comprised a 45% return rate of the approximately 120 paper surveys distributed. The

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overall response rate from a majority online sample is difficult to ascertain due to

potential multiple survey log-ins, however 186 usable surveys emerged from the 240

times the survey was accessed online (77.5% response rate). Upon survey completion

participants were provided with Website links (online survey) or Website addresses

(paper survey) of a number of Australian and international CRC organisations that offer

further information on CRC; the National bowel cancer screening program; about

acquiring home screening kits; and support networks.

8.2.3.1 Ethical approval. Ethical approval was obtained in April 2009 from the

Swinburne Human Research Ethics Committee (SUHREC). Full anonymity of the

participants was guaranteed, as participants were asked not to provide identifiable

details such as names or addresses. Internet ‘Cookies’ was disabled for online survey

participants so that personal computers could not subsequently be identified. Because of

the full voluntary nature of this survey, consent was implied by participation and

submission of the survey. Participants were advised that in the event of increased

anxiety or concern about their physical health, they make an appointment to speak with

their General Practitioner. Details for national counselling services were also provided.

To satisfy the concerns of SUHREC, an item was added at the conclusion of the

survey to measure discomfort, which was monitored throughout data collection. This

item read “Please indicate for us whether or not you experienced discomfort in relation

to the nature of this survey”, with the response option ranging from 1 = “no discomfort”

to 7 = “extreme discomfort”. The average response was that the survey was not found to

cause discomfort (M = 1.6, SD = 1.2), and this result was included in the annual report

for SUHREC.

8.3 Data Screening and Cleaning

8.3.1 Data screening

Missing values, outliers, and normality of distribution were inspected by

descriptive analysis and graphical inspection of the data using PASW Statistics for Mac,

Release 18.0 (SPSS Inc., 2009). Of the online survey responses, a total of 240 attempts

were logged, 29 were entirely incomplete, and 8 withdrew after the demographic

section. A further 8 cases were ineligible for inclusion because they did not meet the

criteria of being over 35 years of age. Therefore, 45 cases were immediately cleaned

from the data file, leaving 195 online survey responses and 54 completed paper surveys

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for further screening (N = 249). Those cases with more than 30% of the survey

incomplete (n = 9; 3.6%) were retained initially for an assessment of missing values.

8.3.2 Missing data

A Missing Values Analysis (MVA) was employed to assess the distribution of

missing observations, and to ensure there were no systematic differences between cases

with and without missing values. Cross-tabulation tables revealed that participants who

filled in the survey online had more missing data across all of the scales than those who

completed a paper version. Women generally had less missing data than men, however

these differences were negligible. There was one detectable pattern for education, where

those who completed some secondary or all secondary schooling had more missing data

than those participants with further education, although these differences were also

minor. Partnered participants had less missing data than single participants, and there

were no discernable patterns on health insurance status across variables.

Across the individual cognitive items there were lower levels of missing data

ranging from 0% to 6.8% on item 5 of self-efficacy, and 10.8% missing on item 9 of

self-efficacy, however these values are most likely accounted for by a technical problem

which omitted item 9 during data importation from the software server (n = 17; 7.1% of

the sample) and could not be restored. With 86% of the sample missing at least one

value on the self-efficacy scale, this may be attributed to it being the final scale of the

survey. Gastrointestinal health history had the second-largest proportion of cases (18%)

missing at least one item.

There were similarly low levels of missing data across emotion scales. Of the

total scale scores for the emotion variables, factor 5 of the DES (mutilation/injury) had

the highest proportion of missing values (7.6% of the sample missed at least one of

these items). In the assessment of missing values in Study 1 (see Chapter 6, Section

6.3.1), questions were raised about the large degree of missing data across emotion

scales compared to cognition scales. Having reversed their order in the present survey, it

can be inferred that the high degree of missing values across emotion scales in Study 1

were most likely due to attrition as a result of their position at the end of a lengthy

survey, as missing values were few for the earlier placed emotion scales in Study 2.

Little’s Missing Completely At Random (MCAR) test, assessing each variable

of the dataset, was not significant, p = .19, (χ2

(1330) = 1375.18), and therefore missing

values could be assumed to be randomly missing. The 9 cases that were missing 30% or

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more of the survey were thus removed from the data file without biasing the

interpretation of further analyses. As there were acceptably low levels of missing values

across the dataset, the expectation maximisation (EM) algorithm was used to estimate

means for remaining missing data. A final sample of N = 240 (186 online + 54 hard

copy) was then assessed for linearity and normality.

8.3.3 Outliers

Univariate outliers were inspected in histograms, stem-and-leaf graphs, box

plots, and normal probability plots. There were no extreme splits (e.g., 90-10) on

categorical variables. Continuous variables were assessed for univariate and

multivariate outliers by inspection of z-scores, graphical assessment, and Mahalanobis’

distance. Nine cases were multivariate outliers in the full dataset, based on

Mahalanobis’ distances greater than the acceptable cut-off of 52.62 for 25 degrees of

freedom. The 9 outlying cases differed substantially from remaining cases on a

combination of variables, indicating some unusual responses. For example, high CRC

worry was combined with low perception of CRC risk for one participant, while low

belief in the effectiveness of screening was accompanied by high screening intentions

for two participants. Another noteworthy pattern included high screening intentions

with very low levels of social support surrounding screening participation. Although

these responses were unusual, they remain plausible without limiting the

generalisability of the findings. These cases were therefore retained.

8.3.4 Normality, linearity and homoscedasticity

Skewness and kurtosis were inspected to assess normality violations. There was

no excessive skew or kurtosis, although gastrointestinal health was close to the upper

kurtosis limit of 10 adopted for the present study (Kline, 2005). In assessing skew, 4

large z-scores (> 3.29 and < -3.29, respectively; Field, 2005) were detected on the

gastrointestinal health variable. Considering a large number of people reported no

gastrointestinal health problems, these values were not remarkable. One case had high

z-scores on four emotion variables (odour disgust; bowel disgust; judgement concern;

and interpersonal embarrassment) however, appraisal of this individual’s responses

indicated that despite higher than average disgust and embarrassment, they also

exhibited response variability.

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Graphical inspections were also conducted using normal probability plots and

detrended Q-Q plots, which suggested slight deviation from normality in a large number

of variables (gastrointestinal health history, fear of procedural aspects, fear of

embarrassment, MEQ subscales, the DES (except Factor 5: mutilation/blood draw),

screening bias, cancer worry, and decisional conflict). Due to only minor deviations

from normality, transformations were not considered to be gainful in light that they

would increase complexity of data interpretation.

With a large number of variables and no excessive skew, linearity was assessed

by bivariate scatterplots between predictor variables and screening intention. Linear

relationships can be difficult to discern in scatterplots, and shapeless patterns can be just

as validly interpreted to meet the assumption of linearity (Kinnear & Gray, 2009).

Notably, there were no distinctive curvilinear patterns, and many had a distinct linear

relationship with the criterion. On the basis of these scatterplots, it was concluded that

there were no issues relating to heteroscedasticity.

8.3.5 Multicollinearity

Bivariate correlations were inspected for multicollinearity and singularity in the

data set, as discriminant validity between independent variables is an assumption of

discriminant analysis (Tabachnick & Fidell, 2007). Concerns about multicollinearity

were raised due to the high correlation between the decisional conflict subscales (DCS),

including a particularly high relationship of r = .90 between the certainty and

effectiveness subscales. Due to this multicollinearity, the DCS was treated as

unidimensional in subsequent analyses.

In addition, two embarrassment subscales (interpersonal and bodily

embarrassment) had a high correlation of r = .85. As multicollinearity can be

determined by correlations from .80 to .90, or greater than .90 (Tabachnick & Fidell,

2007), and DCS was to act as an independent predictor but also as a dependent variable,

the decision was made to treat the DCS as a single scale, but to treat interpersonal

embarrassment and bodily embarrassment as separate predictors during discriminant

analysis.

8.3.6 Scale reliability

Cronbach’s alpha coefficients were calculated to measure the internal

consistency of scales in the present sample. All of the scales appeared to present with

196

good to excellent internal reliability (α > .70), except for the social support scale, which

showed an adequate coefficient of .67. Means, standard deviations, scale ranges,

Cronbach’s Alpha, and inter-item reliability are presented in Table 8.1. Appendix B

presents the Cronbach’s alphas for comparison of scales across Study 1 and Study 2.

8.4 Chapter Summary

This chapter described the method and data screening procedures for Study 2.

The 9 cases that omitted more than 30% of survey responses were removed from the

dataset. There were no serious violations of the assumptions of normality, linearity, or

homoscedasticity, while nine potential outlying cases were retained due to the genuine

plausibility of their responses. Multicollinearity was found between subscales of the

DCS, which was subsequently treated as a single dependent variable for multivariate

analyses. A final sample of N = 240 was retained for descriptive analysis and hypothesis

testing in Chapter 10. Hypotheses are outlined in Chapter 9.

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Table 8.1

Sample Means, SDs, Scale Ranges, and Cronbach’s Alphas for Study 2 Variables

Variable M SD Observed

range

Possible

range

α Inter-

item r

Number

of items

Cognition

Risk perception 10.94 3.72 4-20 4-20 .97 .91 4

Self-efficacy 43.24 9.85 10-60 9-63 .80 .30 9

Test efficacy 24.28 4.06 7-30 6-30 .82 .44 6

Worry 8.03 3.49 4-20 4-20 .91 .72 4

Knowledge 6.39 2.80 0-12 0-12 .71 .53 12

Screening bias 14.69 6.92 6-30 6-30 .92 .64 6

Emotion

Fear 31.98 10.93 13-78 13-78 .88 .37 13

Fear procedure 10.26 4.92 5-30 5-30 .87 .59 5

Fear cancer 15.26 5.16 4-24 4-24 .87 .63 4

Fear embarrassment 6.46 4.40 4-24 4-24 .93 .78 4

DES 61.45 16.13 30-117 30-150 .92 .28 30

Food items 11.81 4.14 6-29 6-30 .85 .49 6

Piercing; injection

/blood draws

8.54 2.90 6-20 6-30 .74 .39 6

Odours 14.52 4.27 6-30 6-30 .86 .51 6

Small animals 10.71 3.85 6-24 6-30 .71 .27 6

Mutilation/injury 15.87 5.70 6-30 6-30 .85 .49 6

Bowel Disgust 6.91 2.84 4-20 4-20 .80 .49 4

Medical

embarrassment

28.82 12.78 17-80 17-85 .95 .53 17

Bodily 11.66 5.83 6-30 6-30 .94 .75 6

Interpersonal 8.41 4.46 5-25 5-25 .91 .68 5

Judgement concern 8.75 3.86 6-28 6-30 .85 .48 6

Social support 15.34 3.73 4-20 4-20 .67 .35 4

Intention to screen

(total)

18.52 4.35 5-25 5-25 .79 .44 5

Decisional conflict

scale (total)

11.12 8.54 0-40 0-40 .96 .73 10

Note. α= Cronbach’s alpha.

N = 240.

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CHAPTER 9

STUDY 2 AIMS AND HYPOTHESES


Study 2, entitled ‘An Australian community study about colorectal cancer

screening intentions: social-cognitive and emotion discriminants’ encompasses the key

variables and refined scales developed as a result of a preliminary investigation (Study

1). This follow-up was designed to test predicted relationships in a target audience for

CRC screening (that is, Australians aged 35 years or older). Subsequent to an

explanation of the methodology and scale psychometrics in Chapter 8, the aims and

rationale for the current investigation are outlined in Section 9.2, while hypotheses are

described in Section 9.3. The analyses and results follow in Chapter 10, and findings are

discussed in Chapter 11.

9.2 Aims and Rationale of Study 2

Study 2 was concerned with relationships between key variables derived from

the methodological aims and outcomes of Study 1, in which it was found that screening

bias, self-efficacy, fear of screening procedures, interpersonal embarrassment, bowel

disgust, and social support were among the strongest psychosocial correlates of

screening intention. Given the older age of the present sample, the demographic and

health variables were re-tested to provide a full description of the sample and for their

potential relationships with the outcome variables. The primary goals of the present

study were (a) to assess whether the correlates of bowel screening intention and

decisional conflict found in Study 1 were replicated in an older sample, that is, a sample

of recommended CRC screening age, (b) to identify correlates of actual screening

behaviour, and (c) to assess which psychosocial, demographic and health-related

variables best discriminated between screening intenders/non-intenders, those with high

/ low decisional conflict about screening, and past screeners/non-screeners.

The first specific objective of the study was to evaluate the relationships and

magnitude of effect of traditional social-cognitive concepts in the health behaviour

literature in predicting screening intention and participation for CRC. These cognitions

include risk perception, self-efficacy, test-efficacy, knowledge, and worry. While risk

perception and test-efficacy were not strong correlates of intention in Study 1, they

could be expected to have greater importance in an older sample with more experience

in medical testing and a greater appreciation of age-related cancer risk.

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A second, related aim was to contrast the role of screening bias with these more

frequently measured cognitions and to determine which cognitions best distinguished

non-screeners from screeners (and intenders from non-intenders). A third aim was to

assess the relationships and importance of the emotions of disgust, medical

embarrassment, and fear, and their subscales, in the differentiation of non-screeners

from screeners (and intenders from non-intenders). Finally, the fourth aim was to

research the relationship and discriminating ability of social support on intentions and

participation for CRC screening, and to re-assess the relationships, but also the

predictive ability of fixed factors in distinguishing groups of individuals based on their

screening intention and behaviour. All of these aims also relate to the secondary

outcome variable, decisional conflict.

Study 2 was further enhanced by exploring intentions as an outcome variable

separately for the two most commonly utilised screening tests: FOBt and Colonoscopy.

This approach may help to distinguish whether variables that were non-significant

correlates of intention in Study 1 (e.g., the factors of the Disgust Scale) are specific to a

screening test (e.g., stool tests). A set of five hypotheses was developed in line with the

above study aims and is presented in Section 9.3. Hypotheses are based on the literature

review in Chapters 2 to 5 and partially on the outcomes of Study 1.

9.3 Study 2 Hypotheses

9.3.1 Description of planned statistical analyses

Pearson product-moment correlations, partial correlations, t-tests, and

discriminant function analysis were employed to test the hypotheses in Study 2.

Discriminant function analysis, or discriminant analysis (DA), predicts group

membership (e.g., screener or non-screener) from a set of predictors (Tabachnick &

Fidell, 2007). A classification table of correct and incorrect estimates of group

membership is generated, with a high percentage of correct estimates indicating that the

discriminant function reliably predicts membership from a specific set of predictors

(Garson, 2008b). In the present study, discriminating variables (predictors) that group

together to form the best discriminant function in the prediction of screeners and non-

screeners, or intenders and non-intenders, can be identified by DA.

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9.3.2 Hypothesis 1(a) to 1(g): Associations

Hypothesis 1 concerns associations between demographic, health, social, cognitive, and

emotion variables, with screening intention and decisional conflict. These hypotheses

were tested by bivariate Pearson product-moment correlations and partial correlations.

1(a) Associations with Demographic Variables

CRC screening intentions and participation will be positively associated with (and

decisional conflict negatively associated with):

• Age; being partnered; having private health insurance; male gender, and being

employed professionally.

1(b) Associations with Health Variables



• GP advice to screen for CRC; history of general screening participation; history

of gastrointestinal illness (e.g., Irritable Bowel Syndrome); family history of

CRC; family history of gastrointestinal illness; and having no competing,

serious medical condition.

1(c) Associations with Cognition Variables



• perceived risk of vulnerability to CRC

• knowledge of CRC symptoms, risk groups, screening, and treatment

• beliefs about the efficacy of CRC screening (test-efficacy)

• beliefs about ability to successfully participate in CRC screening (self-efficacy)

• worry about CRC

Bowel screening intentions and participation will be negatively associated with (and

decisional conflict positively associated with):

• distorted beliefs about the necessity and value of bowel screening (screening

bias).

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A final prediction in 1(c) is that knowledge will not explain the relationship between

screening bias and screening intentions. That is, screening bias will be uniquely and

significantly associated with screening intention when controlling for knowledge of

CRC. Partial correlations will be performed to test this hypothesis.

1(d) Associations with Emotion Variables

CRC screening intentions and participation will be negatively associated with (and

decisional conflict positively associated with):

• disgust (with five factors: food items, piercing of body, odours, small animals,

mutilation/injury)

• embarrassment (subscales of bodily embarrassment, judgement concern,

interpersonal embarrassment)

• fear (subscales of fear cancer, fear of embarrassment, fear of procedural

aspects).

1(e) Emotion and Type of Screening Intention

It is anticipated that fear of embarrassment, interpersonal embarrassment, the Disgust

Emotion Scale, and bowel-specific disgust will each be more strongly associated with

FOBt intention than with colonoscopy intention.

It is anticipated that fear of procedural aspects and bodily embarrassment will

each be more strongly associated with colonoscopy intention, than with FOBt intention.

The differences between correlations for each set of two dependent variables will be

assessed by dependent sample t-tests.

1(f) Associations with Social Support


decisional conflict negatively associated with) perceptions of functional social support.

1(g) Associations Between Dependent Variables

Intentions to screen for CRC will be negatively associated with decisional conflict.

Participation in CRC screening is predicted to negatively correlate with decisional

conflict.

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9.3.3 Hypotheses 2 to 5: Discriminant function analysis

The purpose of discriminant analysis in this investigation is to (1) establish the

most parsimonious means for differentiating between people who intend to screen and

people who do not intend to screen (and likewise for screening participation and

decisional conflict as outcome variables) from a set of commonly-measured variables

(e.g., risk perception, self-efficacy), as well as more recent variables of interest in the

literature (e.g., bias, emotion); (2) compare differences between the prediction of these

outcome groups, and compare variables on the strength of their ability to classify group

membership; and (3) establish the amount of variance accounted for by the

discriminating variables.

9.3.3.1 Hypotheses 2 to 5: Discriminants of screening intention, participation

and decisional conflict. The predictors of intention to screen for CRC (by both FOBt

and colonoscopy), of prior CRC screening participation and of low decisional conflict

about intention to screen for CRC are identical across demographic, health, social,

cognitive and emotion variables. Therefore, these predictions are described collectively,

for FOBt intentions (Hypothesis 2), colonoscopy intentions (Hypothesis 3), screening

participation (Hypothesis 4), and low decisional conflict (Hypothesis 5).

(a) The demographic and health variables expected to predict membership in the

intender, screener, and low decisional conflict groups are:

• older age

• being male

• being partnered

• history of cancer screening (including CRC)

• having been advised to screen by a GP or health professional

• family history of CRC

• history of gastrointestinal illness

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(b) Cognitive variables expected to predict membership in the intender, screener, and

low decisional conflict groups are:

• high risk perception of CRC

• high levels of CRC knowledge

• high test-efficacy beliefs

• high self-efficacy beliefs

• high CRC worry

• low screening bias

(c) Emotion and social variables expected to predict membership in the intender,

screener, and low decisional conflict groups are:

• low fear of screening (fear procedure, fear cancer, and fear embarrassment)

• low disgust (blood draw disgust, odour disgust, and bowel disgust)

• low medical embarrassment (bodily embarrassment, judgement concern,

interpersonal embarrassment)

• high perception of social support

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CHAPTER 10

STUDY 2 ANALYSES AND RESULTS

An Australian community study on CRC screening intention and behaviour:

social-cognitive and emotion discriminants


This chapter reports the findings in relation to the hypotheses of Study 2. Section

10.2 describes sample characteristics, and 10.3 the health and general screening status

of the sample. Section 10.4 presents inferential results corresponding to the hypotheses

presented in Chapter 9: H1, associations between predictor and dependent variables;

H2, discriminants of FOBt intention and non-intention; H3, discriminants of

colonoscopy intention and non-intention; H4, discriminants of screening participation

and non-participation); and H5, discriminants of high and low decisional conflict about

screening.

All statistical analyses were performed using the software, PASW Statistics for

Mac, Release 18.0 (SPSS Inc., 2009). Statistical tests were defined as statistically

significant if the p value was < .05.

10.2 Sample Characteristics

Demographic status of the sample (N = 240) is described below and summarised

in Table 10.1. The sample comprised an uneven gender split, with a greater proportion

of women (n = 160; 67%). The average age was 59 years, with little age difference

between women (M = 59.5, SD = 11.3; age range 36 to 86) and men (M = 60.0, SD =

13.1; age range = 35 to 87).

A large proportion of the sample were partnered (73%) and highly educated,

with the most common level of education being TAFE or College (32%). Graduate and

postgraduate degrees were held by half of the sample, while only 10% of the sample

had not completed secondary school. Reflective of their high education levels, 71% of

the sample reported having full hospital and extras health insurance, with only 16%

having no health insurance. There therefore appears to have been a selection bias in the

sample, with those volunteering to take part in the survey reflecting a more privileged

Australian population. Australian census findings show that around 51% of Australians

have private health insurance (contrasting with the 71% of the present sample), and that

205

disadvantaged Australians are significantly less likely to hold private health insurance

(ABS, 2006).

The Australian and New Zealand Standard Classification of Occupations

(ANZSCO, First Edition, Revision 1; ABS, 2009) provided the basis for occupation

categories, with the largest category being retired, followed by professionals/managers

and then clerical/administrative positions. The remaining 16% of participants were

mostly in technician roles, trades, home duties, or study.

Socio-economic status (SES) was measured according to the Index of Relative

Socio-economic Disadvantage (IRSD) (ABS, 2008), which is formulated from the 2006

Australian Census and indicates low income, low educational attainment,

unemployment, and dwellings without motor vehicles. Because the study was open to

nation-wide residents, the ranking within Australia was used (rather than the ranking of

postcodes within State or Territory). A large majority of participants fell into the highest

three deciles (n = 166; 69%), reflecting a high SES sample, while just 12% (n = 30) of

the sample were from the lowest four deciles (and 18% were in the middle deciles).

This contrasts Australian ABS figures, where 44% reside in the highest 3 deciles, and

24% in the lowest 4 deciles (Ananda et al., 2009).

Most of the sample resided in the major metropolitan and extra-metropolitan

areas of Australia, showing a similar ratio to the national population city / rural split

(87% of Australians live in major cities and inner regional areas; ABS, 2004). Rural

areas comprised villages, townships, and similarly small communities and towns

outside the major metropolitan areas of each state. The sample therefore reflects a fairly

accurate residential stratification of the Australian population.

Although a heterogeneous sample was targeted via a range of recruitment

opportunities, the present sample reflects a highly educated, majority female, and

relatively high SES subset of the Australian population, and it is therefore possible that

health-awareness and screening behaviours are higher than average within this sample.

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Table 10.1

Sample Characteristics on Demographic Variables in Study 2 (N =240)

Demographic Variable n %

Age (M=59.47, SD=11.89)

35-49 51 21.5

50-59 55 23.2

60-69 85 35.9

70 + 46 19.4

Gender

Male 80 33.3

Female 160 66.7

Rural / Metropolitan

Metropolitan 202 84.2

Rural/Outer Regional 38 15.8

Marital Status

Single 20 8.3

Partnered (married / de facto) 174 72.5

Separated/Divorced 33 13.8

Widowed 13 5.4

Educational Attainment

Some secondary school 22 9.2

Completed secondary school 24 10.1

TAFE or College 76 31.9

Degree 56 23.5

Postgraduate 60 25.2

Health Insurance Status

None 39 16.3

Hospital only 29 12.1

Hospital and Extras 171 71.5

Occupational Type

Professional/Manager 81 34.5

Technician/Trade 11 4.7

Clerical/Administration 26 11.1

Sales 6 2.6

Labourers/Machinery operator 2 0.9

Home duties 9 3.8

Student 7 3.0

Retired 89 37.9

Unemployed 4 1.7

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10.3. Health and screening practices of the sample

Health status and behaviour is summarised below and in Table 10.2.

Overall health. Participants were generally healthy and not suffering from any

serious medical condition, with 82% reporting no serious health concerns (n = 196), and

40 participants indicating a serious health concern (4 participants declined to answer).

Gastrointestinal health history. Overall the sample reported a very low history

of gastrointestinal health problems (M = 0.54, SD = 1.03, range 0–6), with Irritable

Bowel Syndrome (IBS) the most commonly reported health problem followed by colon

polyps. While some participants reported more than one health problem (14%) the

majority of the sample indicate no history of gastrointestinal illness.

More women (36%) than men (23%) reported experiencing at least one

gastrointestinal condition; however this difference was not significant, t(238) = -1.60, p

= .11. The prevalence of conditions was different between men and women; with IBS

the most commonly reported health problem among women (n = 35; 22%) and colon

polyps most commonly reported among men (n = 11; 14%).

CRC screening behaviour and screening intentions. The average age of the

sample (59 years) was within the targeted screening age of the NBCSP and the age

group recommended for CRC screening as indicated by the Australian Department of

Health and Ageing (DHA, 2009).

While 62% of the sample had previously participated in CRC screening, only

40% of the total sample had been advised to screen by a medical professional. Prior

screening participation in the present sample is higher than the national average in

Australia, most likely reflecting a sample bias. Across the four bowel screening tests

(X-ray, FOBt, flexible sigmoidoscopy, colonoscopy), there was a mean prior

participation rate of 1.1 (SD = 1.2), with 65% of screeners having participated in at least

one of the two most common tests (stool test and/or colonoscopy).

Screening intentions for the sample were high, with most reporting an overall

intention to screen for CRC. Looking at specific test intentions, around two-thirds

intend to screen by FOBt and about half intend to screen by colonoscopy.

Gender differences in screening practices and intentions. Nearly half of the men

had been advised to screen for CRC by a health professional, while around a third of

women had been advised to screen (not a significant gender difference in screening

advice, t(238), 1.09, p = .279). Of future bowel screening intentions, most women

(64%) reported they would probably or definitely screen by stool test, while 49% intend

208

to screen by colonoscopy. Men responded with similar but slightly higher intentions

(69% intend to screen by FOBt, while 56% intend to screen by colonoscopy). While

these gender differences were not significant for FOBt t(238) = 1.09, p = .279, men

reported significantly higher intentions to screen by colonoscopy, t(238) = 2.28, p =

.024.

In terms of screening for other cancers, the majority of men reported they had

engaged in prostate screening, testicular self-examination, and skin cancer checks by a

medical professional. Women reported even higher rates of female-specific cancer

screening behaviours, including mammography, breast self-examination, cervical

screening, and skin cancer checks by a medical professional.

Family history of bowel cancer. One-third of participants reported that at least

one member of their family (by blood relation) had been diagnosed with CRC, while

about a quarter reported a family history of gastrointestinal conditions. For participants

with a family history of CRC, those family members represented 110 people with CRC,

with one participant reporting 6 cases of bowel cancer in their family. Most commonly,

participants reported no known family history of CRC.

209

Table 10.2

Sample Characteristics on Health Variables in Study 2 (N = 240)

Health Variable n %

CRC Screening History (n = 149; 62.1%)

Barium Enema (X-ray) 41 17.1

Faecal Occult Blood Test 96 40.2

Flexible sigmoidoscopy 35 14.6

Colonoscopy 95 39.7

Advised to screen by health professional 95 39.7

Female Screening History (n = 160)

Mammography 132 82.5

Cervical Screening 149 95.5

Breast Self-Examination (BSE) 145 91.2

Skin cancer screening 112 71.3

Male Screening History (n = 80)

Prostate Screening 49 62.8

Testicular Self-Examination (TSE) 50 64.9

Skin cancer screening 55 70.5

Personal Bowel Health History

None 165 68.7

Crohn’s Disease 6 2.5

Coeliac Disease 9 3.8

Lactose Intolerance 17 7.1

Irritable Bowel Syndrome (IBS) 40 16.7

Colon Polyps 31 12.9

Diverticulitis 26 10.8

Gastrointestinal condition (e.g., IBS) 55 23.0

Family History of CRC

Participants with family history of CRC 78 32.9

Family members with cancer (total) 110

Screening Intentions (definitely/probably yes)

Intend to screen overall 196 81.6

Intend to screen by colonoscopy 124 51.7

Intend to screen by stool test 157 65.4

Undecided about bowel screening 25 10.4

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10.4 Results in Relation to the Hypotheses of Study 2

10.4.1 Hypothesis 1(a) to 1(g)

Associations between demographic, health, social, cognitive, and emotion variables

with screening intention and decisional conflict

1(a) Associations with Demographic Variables

Correlation matrices for demographic and dependent variables are presented in Table

10.3.

Age. As hypothesised, older age was moderately and significantly associated

with lower decisional conflict, and weakly with higher FOBt intention, but unrelated to

CS intention. There was a moderate association between being older and having

screened for CRC.

Partnership. Partnership status was unrelated to any of the outcome variables of

decisional conflict, FOBt intention, CS intention, or CRC screening participation.

Health Insurance. There was no relationship between health insurance and any

of the dependent variables of decisional conflict FOBt intention CS intention or

screening participation.

Gender. Intention to screen by colonoscopy was significantly but weakly related

to being male. However there was no relationship between gender and any other

dependent variable (FOBt intention, screening participation, decisional conflict).

Occupation. Decisional conflict was weakly and positively correlated with

professional employment, while screening participation was moderately associated with

non-professional or un-employment (including retirement), however this is likely a

reflection of an older sample, many of whom are retired and no longer in the workforce.

There was no relationship between occupation and screening intention by either FOBt

or CS.

Socioeconomic Status (SES). There was no relationship between SES and any of

the dependent variables (decisional conflict, FOBt intention, CS intention, screening

participation).

Hypothesis 1(a) Summary. Hypothesis 1(a) was only partially supported for the

variables age, gender, and occupation. Only age was significantly but moderately

related to the outcome variables (except CS intention). There was a weak association

between being male and reporting higher CS intentions.

211

Table 10.3

Correlation Matrix of Demographic and Dependent Variables in the Community Sample

1 2 3 4 5 6 7 8 9 10 11 12

1 Decisional Conflict 1.000

2 FOBt intention -.24** 1.000

3 Colonoscopy intention -.46** .17** 1.000

4 Screening participation -.32** .22** .32** 1.000

5 Age -.26** .16* .08 .31** 1.000

6 Gender .08 -.07 -.15* .01 -.04 1.000

7 Education .06 -.03 -.12 .03 -.07 .01 1.000

8 Partner status .01 .05 .02 .00 -.09 -.16* -.04 1.000

9 Occupation .15* -.11 -.01 -.20** -.47** -.09 .23** .05 1.000

10 SES .07 -.12 .02 .07 -.02 -.01 .15* -.08 .11 1.000

11 City/Rural .01 .04 -.06 -.06 .04 -.06 -.04 .04 -.06 -.52** 1.000

12 Health insurance .00 .06 .06 -.03 .01 -.03 .01 .33** .05 .22** -.21** 1.000

Note. N= 240; Two-tailed significance * p < .05; **p < .01.

Dependent variables: Higher scores on 1, 2, and 3 reflect higher conflict, higher FOBt intentions, and higher CS intentions. Screening non-participation = 0, Screening

participation = 1; Independent variables: Occupation: non-workers = 0, non-professional workers = 1, professional workers = 2. Gender (0 = male and 1 = female);

Rural/Metro = rural / metropolitan and extra-metropolitan residential status (metropolitan = 0 and rural = 1); Partner status (0 = un-partnered and 1 = partnered); Education (0

= high school, 1 = TAFE/college, 2 = Degree and 3 = Postgraduate; Occupation (0 = non-worker, 1 = non-professional, and 2 = professional); SES = socioeconomic status (0

= low deciles postcode and 1 = high deciles postcode); health insurance (0 = none, 1 = hospital only and 2 = hospital and extras).

212

1(b) Associations with Health Variables

Correlations between health variables and dependent variables are presented in Table

10.4.

Screening advice. There was a moderate relationship between receiving GP

advice to screen and experiencing lower decisional conflict. GP advice was also

significantly moderately (but positively) related to CS intention and strongly to

screening participation, however it was unrelated to FOBt intention.

History of general screening participation. Having a history of screening for

cancer in general was significantly and moderately associated with less decisional

conflict about future screening intentions, and with greater FOBt and CS screening

intentions. A history of past screening behaviour for non-CRC cancers was significantly

and strongly associated with having screened for CRC.

Gastrointestinal illness. Gastrointestinal illness was weakly but significantly and

positively related to CS intention and moderately to past CRC screening participation.

There was a weak relationship between having a gastrointestinal health condition and

experiencing decisional conflict. No relationship was observed between gastrointestinal

health problems and intention to screen by FOBt.

Family history of gastrointestinal illness. A history of family gastrointestinal

illness was not related to any dependent variable (decisional conflict, FOBt intention,

CS intention, CRC screening participation).

Family history of CRC. Lower decisional conflict was significantly but weakly

associated with having a family history of CRC. Intending to screen by colonoscopy

was significantly but weakly related to having a family history of CRC, but FOBt

intentions and screening participation were not.

Competing medical condition. No relationship was found between having a

competing serious medical condition and decisional conflict, FOBt intention, CS

intention, and screening participation.

Hypothesis 1(b) Summary. Intending to screen by CS was moderately and positively

related to two independent variables (screening advice, family history of CRC) and

weakly to having had a gastrointestinal condition. Intending to screen by FOBt was only

moderately related to having undertaken gender-specific screening practices (such as

prostate screening or mammography).

213

Only participation in screening was strongly related to any of the independent

variables (screening advice). It was also moderately related to having had

gastrointestinal illness. Decisional conflict about future screening intentions were only

weakly related to having no family history of CRC or personal history of

gastrointestinal illness. Overall, most significant relationships between health variables

and screening intentions were weak to moderate.

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Table 10.4

Correlation Matrix of Health and Dependent Variables in the Community Sample

1 2 3 4 5 6 7 8 9 10 11

1 Decisional Conflict 1.000

2 FOBt intention -.24** 1.000

3 Colonoscopy intention -.46** .17** 1.000

4 Screening participation -.32** .22** .32** 1.000

5 Screening advice -.34** .08 .35** .48** 1.000

6 Male screening history -.06 .36** .19 .19 .24* 1.000

7 Female screening history -.14 .28** -.03 .13 .09 ª 1.000

8 Gastrointestinal condition -.14* -.04 .18** .32** .33** -.01 .10 1.000

9 Medical condition -.03 -.02 .05 .06 .08 -.06 .12 .28** 1.000

10 Family gastrointestinal .02 .12 .05 .13 -.01 -.03 .04 .26** .15* 1.000

11 Family CRC history -.20** .06 .26** .12 .27** -.07 .01 .13* .07 .20** 1.000

Note. Outcome variables as in Table 10.3. Screen advice (0 = no and 1 = yes); Male screen history (0 = none and 1 = screened); Fem screen = female-specific screening

participation (0 = none and 1 = screened); CRC screen = bowel screening history (0 = none and 1 = screened); Gastro health (0 = none, 1 = one or more conditions); Med

condition = current competing serious medical condition (0 = no and 1 = yes); Family gastrointestinal condition history (0 = no/don’t know and 1 = yes); Family CRC history

(0 = no/don’t know; 1 = yes).

ªNot computed because at least one variable held constant.

N = 240. Correlations with male screen history n = 80; Correlations with female screen history n = 160. Two-tailed significance * p < .05; **p < .01.

215

1(c) Associations with Cognitive Variables

Correlations between cognitive variables and the dependent variables are displayed in

Table 10.5.

Risk Perception. Perception of CRC risk was weakly associated with lower

decisional conflict about screening, moderately associated with higher CS intention, and

strongly related to screening participation, all significantly as predicted. However, there

was no significant association between risk perception and FOBt intentions.

Knowledge. Knowledge of CRC was only very weakly significantly associated

with lower decisional conflict and greater FOBt intentions. Against predictions, there

was no significant relationship between CRC knowledge and CS intention or

participation.

Test-Efficacy. As expected, stronger beliefs about the efficacy of tests were

moderately and significantly associated with lower decisional conflict and higher FOBt

and CS intentions, while only weakly associated with higher participation in CRC

screening.

Self-Efficacy. Self-efficacy beliefs to engage in CRC screening were strongly

and significantly related to less decisional conflict, and higher CS intention. Self-

efficacy was moderately and significantly related to higher FOBt intentions and prior

CRC screening, as predicted.

Worry. Worry about CRC showed a weak but significant association with lower

decisional conflict and moderate relationship with past screening. There was a strong

significant relationship between worrying about CRC and intending to screen by CS,

but not by FOBt.

Screening Bias. As predicted, screening bias was strongly and significantly

related to higher decisional conflict and lower CS intention. Biases were also

significantly but only moderately related to lower intentions to screen by FOBt and to

less CRC screening participation.

As higher levels of knowledge were significantly related to less screening bias, a

partial correlation was performed to control for the effects of knowledge on screening

bias and its relationship with the outcome variables. When knowledge of CRC is

partialled out of the correlation, screening bias remains strongly and positively

associated with decisional conflict, r(237) = .39, p < .001, and negatively associated

with FOBt intention, r(237) = -.23, p < .001, colonoscopy intention, r(237) = -.38, p <

.001, and CRC screening participation, r(237) = -.27, p < .001, as predicted.

216

Hypothesis 1(c) Summary. Decisional conflict was significantly associated with all

cognitions, but only strongly with self-efficacy and screening bias (negatively and

positively, respectively). Similarly, FOBt intentions indicated strong relationships with

self-efficacy and screening bias, but also beliefs about test accuracy and effectiveness

(test-efficacy), while intention to screen by CS was strongly associated with self-

efficacy, risk perception, and screening bias. Therefore, self-efficacy and screening bias

demonstrated moderate to strong relationships with all outcome variables except

participation (with which they showed weak to moderate relationships).

Only three correlations between cognitive variables and the dependent variables

were non-significant. Cancer worry and risk perception were unrelated to FOBt

intention, and knowledge was unrelated to CS intention. Amongst the cognitive

variables, noteworthy correlations were observed between cancer worry and perception

of risk, while high levels of self-efficacy related to lower levels of screening bias,

supporting the findings of Study 1.

217

Table 10.5

Correlation Matrix of Cognitive Variables, Social Support, and Dependent Variables in the Community Sample

1 2 3 4 5 6 7 8 9 10 11

1 Decisional conflict

2 FOBt intention -.24**

3 CS intention -.46** .17**

4 Screening participation -.32** .22** .32**

5 Risk perception -.16* .06 .38** .26**

6 Knowledge -.20** .14* .10 .06 .12

7 Test-efficacy -.35** .27** .32** .16* .16* .18**

8 Self-efficacy -.46** .34** .47** .18** .06 .20** .29**

9 Cancer worry -.16* .11 .30** .24** .41** .07 .12 .04

10 Screening bias .41** -.24** -.38** -.27** -.19** -.13* -.12 -.43** -.17**

11 Social support -.29** .13* .31** .18** .15* .18** .21** .27** .02 -.36**

Note. Screening non-participation = 0, screening participation = 1. Intentions and decisional conflict assessed as scales, with higher scores indicating higher intentions and

conflict, respectively.

N = 240.

Two-tailed significance *p < .05. **p < .01.

218

1(d) Associations with Emotion Variables

Correlations between emotion variables and the dependent variables are displayed in

Table 10.6.

Disgust. Of the five subscales of the Disgust Emotion Scale (DES), only blood

draw, odour, and injury disgust were significantly related to higher decisional conflict,

and these relationships were only weak. There were no other significant correlations

between the DES factors and screening intention or participation. However, bowel-

related disgust was significantly moderately associated with decisional conflict and

lower screening intentions (both FOBt and CS), but unrelated to participation in

screening.

Medical Embarrassment. Embarrassment in relation to being judged by others

(judgement concern) was significantly related to two dependent variables. It was

moderately and positively associated with decisional conflict, and weakly and

negatively with CS intention. It was unrelated to FOBt intention or screening

participation.

Bodily embarrassment was significantly and moderately related to greater

decisional conflict and lower screening intentions (both FOBt and CS), but unrelated to

screening participation.

In general, interpersonal embarrassment was significantly and moderately

related to all the dependent variables. That is, embarrassment associated with direct

communication with the health provider was significantly related to higher decisional

conflict, and to lower screening intentions for both tests, but particularly FOBt. It was

also weakly related to lower screening participation.

Fear. Against predictions, only one subscale of fear significantly correlated with

three of the dependent variables. Fear of the procedural aspects of screening for bowel

cancer was moderately related to greater decisional conflict and lower CS intention, and

was also weakly but still significantly related to lower FOBt intention. None of the fear

subscales were related to screening participation.

Hypothesis 1(d) Summary. Hypothesis 1(d) was moderately supported. Emotion

variables were associated with the decision process (decisional conflict and intentions)

involved in CRC screening, but not with participation in screening, except for

interpersonal embarrassment, which showed a small negative association with

screening.

219

The strongest relationships with decisional conflict were fear of procedural

aspects of screening and interpersonal embarrassment. Thus, the only emotive (but

weak) correlate of CRC screening behaviour was low interpersonal embarrassment.

1(e) Emotion and Type of Screening Intention

Part one of H1(e) predicted (i) fear of embarrassment, (ii) interpersonal

embarrassment, and (iii) the DES and bowel-related disgust would be more strongly

associated with FOBT intention (than with CS intention), however these hypotheses

were unsupported. Against this prediction, fear of embarrassment (i) was unrelated to

either type of screening intention and therefore this hypothesis was unable to be tested.

Interpersonal embarrassment was not more strongly associated with FOBt

intention than with CS intention, t(237) = -0.76, p = .224. In terms of reported disgust

levels, the Disgust Emotion Scale was unrelated to intention to engage in FOBt (r(240)

= -.11, p = .100) or CS (r(240) = -.06, p = .331), and therefore unable to be statistically

compared. Bowel disgust was not more strongly related to FOBt than CS intention,

t(237) = -0.89, p = .188).

Part two of H1(e) was partially supported, which predicted bodily

embarrassment and fear of procedural aspects of screening would be more strongly

related to CS intention (than to FOBT intention). Bodily embarrassment was not more

strongly related to CS intention than FOBt intention, t(237) = -0.50, p = .307. However,

fear of procedural aspects was significantly more strongly related to CS intention, t(237)

= -2.14, p = .016, than to FOBt intention.

1(f) Associations with Social Support

As expected, perception of social support was significantly and moderately

associated with lower decisional conflict and greater CS intention, and moderately with

participation in CRC screening. FOBt intentions were only weakly but significantly

associated with perceptions of social support. These correlations are presented in Table

10.6.

1(g) Associations Between Dependent Variables

Decisional conflict was significantly negatively associated (but only weakly)

with FOBt intentions, strongly with colonoscopy intentions, and moderately with

screening participation. Prior CRC screening participation was associated with

220

intentions to engage in both FOBt and colonoscopy, while intention to engage in FOB

testing was positively related to CS intention, supporting hypothesis 1(g). Tables 10.3 to

10.6 display the correlation coefficients between dependent variables. A combined

correlation matrix for all cognitive and emotive variables together with the dependent

variables is in Appendix L.

221

Table 10.6

Correlation Matrix of Emotion Variables, Social Support, and Dependent Variables in the Community Sample

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 Decisional conflict

2 FOBt intention -.24**

3 CS intention -.46** .17**

4 Screening -.32** .22** .32**

5 Fear Procedure .36** -.16* -.33** -.10

6 Fear Cancer .08 -.04 -.12 .10 .45**

7 Fear Emb .08 -.07 -.12 -.04 .32** .28**

8 Food disgust .06 -.06 .05 .03 .25** .25** .17**

9 Blood draw disgust .19* -.06 -.07 -.06 .33** .34** .13* .29**

10 Odour disgust .16* -.10 -.12 -.04 .33** .34** .27** .63** .42**

11 Small animals .06 -.11 -.03 .03 .31** .37** .33** .53** .45** .63**

12 Injury disgust .13* -.07 -.03 .02 .33** .34** .16* .31** .49** .58** .53**

13 Bowel disgust .30** -.31** -.24** -.08 .47** .29** .39** .43** .39** .65** .51** .45**

14 Bodily emb .31** -.25** -.29** -.12 .55** .35** .43** .23** .28** .39** .36** .43** .56**

15 Judgement concer .22** -.15 -.14* .01 .48** .36** .48** .29** .34** .34** .40** .32** .40** .62**

16 Interpersonal emb .34** -.31** -.25** -.14* .54** .34** .49** .33** .31** .44** .41** .40** .70** .85** .66**

17 Social support -.29** .13* .31** .18** -.22** -.05 -.13* -.10 -.12 -.16* -.07 -.05 -.22** -.17** -.18** -.18**

Note. Screening non-participation = 0, screening participation = 1. Intentions and decisional conflict assessed as scales, with higher scores indicating higher intentions and

conflict, respectively. Emb = embarrassment.

N = 240.

Two-tailed significance *p < .05. **p < .01.

222

10.4.2 Hypotheses 2 to 5: Discriminant function analyses

Hypotheses 2 to 5 were tested inferentially using discriminant function analysis

(DA). DA was employed to reliably distinguish, from a set of demographic, health,

cognition, social and emotion predictors, between (a) those with and without screening

intentions for FOBt and, (b) for colonoscopy; (c) those who have participated in

screening from those who have not, and (d) those with high or low decision conflict

about intentions to screen for CRC. The emphasis of these analyses is on the

combination of variables (the discriminant function) that best differentiates between

each of the categorical groups.

Assumptions of discriminant analysis include multivariate normality, linearity,

homogeneity of variance, and the absence of outliers and multicollinearity, all of which

were sufficiently met in the present dataset (see Section 8.3, Chapter 8). An acceptably

small number of outliers arose in decisional conflict, FOBt intention, and CS intention

analyses, and each discriminant analysis was re-run, excluding the most extreme outlier,

revealing that outliers did not significantly alter the outcome. Where small violations of

normality were found, the use of DA remains appropriate due to its robustness to the

failure of normality from excessive skew (Tabachnick & Fidel, 2007).

Wilks’ lambda was used to determine the statistical significance of the

discriminant function, while the effect size for a single discriminant function is

indicated by the squared canonical correlation (Tabachnick & Fidell, 2007). Gender,

screening advice, family history of CRC, and gastrointestinal health condition were

dummy coded so that male = 0, female = 1; no screening advice = 0, screening advice =

1; no family history = 0, family history =1; no gastrointestinal condition = 0; one or

more gastrointestinal condition(s) = 1. Occupation was excluded from DA as it was

likely confounded by a largely retired sample (unemployed but only due to age), while

partnership status, SES, and health insurance status were unrelated to any of the DVs

and therefore precluded from the analyses. All variables in the discriminant function

analyses, and their hypothesised relationship with the outcome variables are presented

in Table 10.7. Means, standard deviations, and the F-statistic for each predictor variable

is presented in Table 10.11, while means for each predictor on each level of the

categorical outcome variables are presented in Appendix M.

223

Table 10.7

Independent Variables Included in Discriminant Function Analyses and the Predicted

Direction of the Relationship with Outcome Variables

Independent variable FOBt

intentiona

Colonoscopy

intentiona

Screening

participation

Decisional

conflict

Demographic/Health

Age + + + -

Gender (male=0) + + + +

Screening advice (none=0) + + + -

Family history CRC

(none=0)

+ + + -

GI condition (none=0) + + + -

Cognitions

Risk perception + + + -

CRC knowledge + + + -

Test-efficacy + + + -

Self-efficacy + + + -

Cancer worry + + + -

Screening bias - - - +

Emotions

Fear of procedure - - - +

Fear cancer - - - +

Fear embarrassment - - - +

Blood draw disgust - - - +

Odour disgust - - - +

Bowel disgust - - - +

Bodily embarrassment - - - +

Judgement concern - - - +

Interpersonal embarrassment - - - +

Social Support + + + -

Decisional conflict NA NA - NA

Note. GI = Gastrointestinal. aDecisional conflict is posited to be an outcome of a screening intention or decision, and is therefore not

treated as a predictor in these discriminant function analyses.

N = 240.

224

Hypothesis 2: Discriminants of intenders and non-intenders of Faecal Occult Blood

testing (FOBt). DA was employed in order to ascertain the variables that best

discriminate between people with intentions (n=157) and without intentions (n=83) to

screen using the FOB test. According to the univariate ANOVAs, there were significant

differences between people with and without intentions on 8 of the predictor variables

(see Table 10.11). The chi-square test indicated that the single discriminant function

calculated to differentiate intenders from non-intenders on these predictors was

significant, χ2

(21) = 38.57, p = .011, with a Wilks’ Lambda of .84 and a squared

canonical correlation of .16, indicating only 16% of variance was accounted for by this

discriminant function. The function at group centroids (the ‘group mean’ of the

discriminant score) for FOBt non-intenders was -.59, and for FOBt intenders was .31.

The boxplot (see Figure 10.1) suggests overlap, indicating that while the function is

significant, it may not strongly discriminate between FOBt intenders and non-intenders.

The pooled within-groups correlations revealed that three emotions

(interpersonal embarrassment, bowel disgust, bodily embarrassment), four cognitions

(self-efficacy, test-efficacy, screening bias, cancer worry), and age, were strong

predictors, with pooled group correlations > .30 (all other pooled correlations < .30). Of

these, bowel disgust and interpersonal embarrassment had the highest standardised

canonical discriminant function coefficients and therefore contributed most to

discriminating between FOBt intenders and non-intenders.

It was evident by inspection of the structure matrix that intenders of FOBt

screening had lower interpersonal embarrassment, bowel disgust, bodily

embarrassment, and screening bias, and had higher self-efficacy, test-efficacy, and

cancer worry, and were also older. The discriminant function accurately predicted

65.8% of cases (56.6% of non-intenders and 70.7% of intenders were correctly

classified). Table 10.8 depicts the statistics of the analysis, and Table 10.9 presents

pooled within-groups correlations.

Figure 10.1. Box plots illustrating the distribution of discriminant s

intenders of FOBt and intenders of FOBt.

Table 10.8

Discriminant Function Analysis S

Statistics

Chi-square (df = 21)

Wilks’ Lambda

Canonical correlation

% Correctly classified

% Variance explained

*p < .05. **p < .01. ***p < .001.

adf = 22 because decisional conflict

. Box plots illustrating the distribution of discriminant scores for non

intenders of FOBt and intenders of FOBt.

Discriminant Function Analysis Statistics

FOB test

Intention

Colonoscopy

Intention

Screening

Participationa

38.57* 81.38*** 108.39***

.84 .70 .62

.40 .55 .62

65.8 75.8 76.7

16 30 38

< .001.

= 22 because decisional conflict is an additional predictor

225

cores for non-

Decisional

Conflict

92.87***

.67

.58

76.7

34

Hypothesis 3: Discriminants of intenders and non

Colonoscopy (CS) screening intention was assessed by DA to determine whether there

were differentiating variables between intenders and non

significant differences between these two groups on 13 of the 21

10.11 for a list of significant variables), while a chi

discriminant function calculated to distinguish the groups was significant,

p < .001, Wilks’ Lambda = .70. The discriminant function explained 30% of the

variance, while the functions at group centroids were non

.67, and intender of colonoscopy = .63 (see Figure 10.

Figure 10.2. Box plots illustrating the distribution of discrimina

intenders of colonoscopy and intenders of

Of pooled within-groups correlations >.30, the most significant differentiating variables

were largely cognitive (self-efficacy, risk perception, screening bias, cancer worry, test

efficacy), and also screening advice, social support,

history of CRC. Of these variables, the canonical coefficients indicated that the two

most important variables of colonoscopy intention classification were self

: Discriminants of intenders and non-intenders of colonoscopy.


were differentiating variables between intenders and non-intenders. There were

significant differences between these two groups on 13 of the 21 variables (see Table

r a list of significant variables), while a chi-square test indicated that the

discriminant function calculated to distinguish the groups was significant, χ2

(21) = 81.38,

The discriminant function explained 30% of the

ctions at group centroids were non-intender of colonoscopy =

ntender of colonoscopy = .63 (see Figure 10.2).

. Box plots illustrating the distribution of discriminant scores for non-

ders of colonoscopy.

groups correlations >.30, the most significant differentiating variables

efficacy, risk perception, screening bias, cancer worry, test

efficacy), and also screening advice, social support, fear of the procedure, and family


most important variables of colonoscopy intention classification were self-efficacy and

226


(see Table

= 81.38,

intender of colonoscopy = -

groups correlations >.30, the most significant differentiating variables

efficacy, risk perception, screening bias, cancer worry, test-

fear of the procedure, and family


efficacy and

227

cancer worry. Intenders had higher self-efficacy, risk perception, cancer worry, social

support, and test-efficacy, and reported more screening advice and family history of

CRC. CS intenders also reported less screening bias and less fear of the procedure.

Correct classification occurred for 75.8% of all cases, while 73.3% of non-intender and

78.2% of intender group membership was accurately classified. The statistics and

pooled within-groups correlations are presented in Tables 10.8 and 10.9, respectively.

Hypothesis 4: Discriminants of those who have participated in CRC screening and

those who have not. Discriminant function analysis was employed to identify group

differences between CRC screeners and non-screeners. According to univariate

ANOVAs, there were significant differences between screeners and non-screeners on 11

of the 22 variables (see Table 10.11). The discriminant function differed significantly

for screeners and non-screeners, χ2

(22) = 108.39, p < .001, Wilks’ Lambda = .62. A

squared canonical correlation of .38 indicated 38% of the variance was explained by

this discriminant function. The functions at group centroids were never screened = -

1.00, and screened = .61 (see Figure 10.3). Pooled within-groups correlations ranged

from .01 to .70, with seven correlations > .30. Emotions did not discriminate screening

action, however three cognitions were important. Screeners were more likely to have

been advised to screen, to have had a gastrointestinal condition, be older, report less

screening bias, and report higher risk perception and cancer worry. Decisional conflict

was the strongest non-fixed factor predicting prior screening behaviour, where higher

conflict predicted less likelihood of prior screening.

The canonical coefficients indicated that screening advice, gastrointestinal

health, decisional conflict, and age were making the largest contribution to the capacity

of the discriminant function to distinguish between screeners and non-screeners. Except

for decisional conflict and bias, the predictors positively correlated with screening

participation, suggesting that older participants who have had gastrointestinal

condition(s), and have been advised to screen by a health professional, are more likely

to have screened. The next most successful predictor was interpersonal embarrassment,

which was predictive of non-participation, however it failed to achieve an effectively

large correlation within the discriminant function (r < .30). Overall, 77% of cases were

correctly classified, where 74% of never-screened participants and 79% of screeners

were accurately classified into their respective group membership. Statistics of the

analysis are presented in Table 10.8

in Table 10.9.

Figure 10.3. Box plots illustrating the distribution of discriminant scores for screeners

and non-screeners.

Hypothesis 5: Discriminants of those who experience high decisional conflict about

CRC screening intention and those who do not.

categorise decisional conflict into lower (

240). While there are drawbacks to performing median splits, such as loss of power

(MacCallum, Zhang, Preacher, & Rucker, 2002), there were extreme score ranges

between higher and lower decisional conflict, with a very small number of high conflict

participants (n = 30) and a much larger group of low conflict participants (

to concern over the loss of variation amongst levels of high conflict, a

deemed useful (MacCallum et al., 2002).

8 and pooled within-group correlations are depicted

. Box plots illustrating the distribution of discriminant scores for screeners


CRC screening intention and those who do not. A median split was used to

categorise decisional conflict into lower (n = 122) or higher (n = 118) conflict (N =

ere are drawbacks to performing median splits, such as loss of power

(MacCallum, Zhang, Preacher, & Rucker, 2002), there were extreme score ranges


and a much larger group of low conflict participants (n = 210). Due

to concern over the loss of variation amongst levels of high conflict, a median split was

deemed useful (MacCallum et al., 2002).

228

lations are depicted

. Box plots illustrating the distribution of discriminant scores for screeners


=

ere are drawbacks to performing median splits, such as loss of power


210). Due

median split was

229

Linear discriminant analysis was used to assess whether the independent

variables (listed in Table 10.7) would differentiate people with lower or higher

decisional conflict. Univariate ANOVAs indicated significant differences on decisional

conflict across 17 of the predictor variables (see Table 10.11). One discriminant

function was generated, which was significantly different for low and high decision

conflict groups (χ2

(21) = 92.87, p < .001), accounting for 34% of the variance in

decisional conflict. The canonical correlation was significant at .58 (Wilks’ Lambda =

.67, p < .001), with a squared canonical correlation of .34. The functions at group

centroids were low conflict = .69, high conflict = -.72 (see Figure 10.4). The pooled

within-groups correlations, in order of the size of the correlation with the function,

revealed that three cognitions (screening bias, self-efficacy, test-efficacy), four

emotions (fear of procedure, interpersonal embarrassment, bowel disgust, bodily

embarrassment), social support, advice to screen, and age were all strong discriminants,

while all other pooled group correlations were < .30.

The standardized canonical discriminant functions revealed that the strongest of

these predictors were test-efficacy, screening bias, and bodily embarrassment. The

structure matrix showed that people with low decision conflict were less likely to have

screening biases, a fear of screening procedures, interpersonal embarrassment, bowel

disgust, and bodily embarrassment, and more likely to have high self-efficacy, strong

beliefs in test-efficacy, high social support, to have received screening advice from a

health professional, and be older. The discriminant function correctly classified 76.7%

of cases, with correct predictions for 78.7% of people with low conflict and 74.6% with

high conflict. The statistics for the DA are presented in Table 10.8, and pooled within-

groups correlations in Table 10.9.

Figure 10.4. Box plots illustrating the distribution of discriminant s

decisional conflict and high decisional conflict groups.

Box plots illustrating the distribution of discriminant scores for low

decisional conflict and high decisional conflict groups.

230

231

Table 10.9

Discriminant Function (Dis) Pooled Within Groups Correlations

Pooled within groups correlations

Dis1 Dis2 Dis3 Dis4

Variable FOBt

intentions

(yes/no)

Colonoscopy

intentions

(yes/no)

Screening

participation

(yes/no)

Decisional

Conflict

(low/high)

Demographic/health

history

Age .299* .039 .412** .346**

Gendera -.115 -.099 .016 -.033

Screening adviceb .050 .443** .702** .459**

Family history of CRCc .042 .354** .156 .264**

GI conditiond -.003 .229* .438** .175

Cognition

Risk perception .067 .494** .340** .215*

CRC knowledge .155 .068 .076 .216*

Test-efficacy .447** .332** .202* .461**

Self-efficacy .571** .573** .233** .592**

Cancer worry .311* .440** .312** .197*

Screening bias -.393* -.481** -.366** -.611**

Social Support -.220 .374** .240** .319**

Fear

Fear of procedure -.194 -.366** -.128 -.443**

Fear cancer -.157 -.144 .130 -.178

Fear embarrassment -.121 -.113 -.048 -.080

Disgust

Blood draw disgust -.066 -.140 -.080 -.182*

Odour disgust -.099 -.088 -.048 -.202*

Bowel disgust -.541** -.298** -.097 -.368**

Embarrassment

Bodily embarrassment -.443** -.274** -.154 -.297**

Judgement concern -.285 -.101 .013 -.241**

Interpersonal

embarrassment -.587**

-.269** -.174* -.377**

Decisional conflict NA NA -.426*** NA

Note. aMale=0, female = 1; bNo screening advice = 0, Screening advice=1; cNo family history of CRC =

0, family history = 1; dGastrointestinal condition=1, no gastrointestinal condition = 0.

N = 240. *p < .05. **p < .01. ***p < .001.

232

Table 10.10

Summary of the Discriminating Variables (in Order of Importance) for each

Discriminant Function (Dis)

Predictor Variable: No FOBt

intention (Dis1)

Predictor Variable: No CS intention

(Dis2)

Interpersonal

embarrassment

+ Self-efficacy -

Self-efficacy - Risk perception -

Bowel disgust + Screening bias +

Test-efficacy - Screening advice -

Bodily embarrassment + Cancer worry -

Screening bias + Social support -

Cancer worry - Fear of procedure +

Family history of cancer -

Predictor Variable: No prior

screening (Dis3)

Predictor Variable: High decision

conflict (Dis4)

Screening advice - Screening bias +

Gastrointestinal

condition

- Self-efficacy -

Decisional conflict + Test-efficacy -

Age - Screening advice -

Screening bias + Fear of procedure +

Risk perception - Interpersonal

embarrassment

+

Cancer worry - Bowel disgust +

Age -

Social support -

Note. All variables represent pooled within-groups correlations > .3.

233

Table 10.11

Means, SDs, and F-Statistics for each Discriminant Function Analysis and their Discriminant Variables

Variable M SD FOB test intention

F (1,238)

Colonoscopy intention

F (1,238)

Screening participation

F (1,238)

Decisional conflict

F (1,238)

Age 59.47 11.81 3.92* .16 24.64** 14.16**

Gendera .67 .47 .59 1.00 .04 .13

Screening adviceb .40 .49 .11 20.11** 71.37** 25.24**

Family CRC historyc .33 .47 .08 12.82** 3.51 8.36**

GI conditiond .31 .46 .00 5.36* 27.74** 3.69

Risk perception 10.94 3.72 .20 24.99** 16.70** 5.53*

Cancer knowledge 6.39 2.80 1.05 .47 .84 5.58*

Test-efficacy 24.28 4.06 8.78** 11.31** 5.92* 25.45**

Self-efficacy 43.24 9.85 14.35** 33.55** 7.85** 42.01**

Cancer worry 8.03 3.49 4.25* 19.78** 14.09** 4.65*

Screening bias 14.69 6.92 6.80* 23.64** 19.41** 44.73**

Social support 15.34 3.73 2.13 14.29** 8.35** 12.02**

Fear of procedure 10.26 4.92 1.65 13.68** 2.38 23.54**

Fear of cancer 15.26 5.15 1.08 2.12 2.45 3.81e

Fear of embarrassment 6.46 4.40 .64 1.30 .34 .77

Blood draw disgust 8.54 2.90 .19 2.01 .94 3.99*

Odour disgust 14.42 4.27 .43 .80 .33 4.88*

Bowel disgust 6.91 2.84 12.85** 9.07** 1.37 15.98**

Bodily embarrassment 11.66 5.83 8.64** 7.70** 3.45 10.61**

Judgement concern 8.75 3.86 3.56 1.04 .03 6.98**

Interpersonal

embarrassment 8.41 4.46 15.13 7.40** 4.41* 17.01**

Decisional conflict 11.12 8.54 NA NA 26.47** NA aMale=0, female = 1;

bNo screening advice = 0, Screening advice=1;

cNo family history of CRC = 0, family history = 1;

dGastrointestinal (GI) condition=1, no

gastrointestinal condition = 0. ep = .05 on Decisional Conflict and Fear of Cancer. N = 240. *p < .05. **p < .01.

234

10.5 Chapter Summary

A descriptive analysis of the sample’s demographic and health characteristics

revealed that the sample had higher than average levels of health insurance and

education, while occupation type and SES ranking according to the IRSD also indicated

negative skew for SES.

Perhaps as a result of this skew, a remarkably high percentage of the sample

indicated intention to screen for CRC (81.6%), however when dependent on the type of

screening test, intentions were closer to current Australian screening participation rates

(FOBt intentions 65% of sample; colonoscopy intentions, 52% of sample). Past

screening participation was higher than the national average in Australia, with 62% of

the sample having participated in some form of accepted CRC testing.

Pearson product-moment correlations were performed to analyse the

relationships between predictors and outcome variables. A large number of variables

were associated with decisional conflict and intentions to screen, while psychosocial

and emotion variables were less associated with actual participation (see Tables 10.12

for a summary of significant associations and relationship direction).

DA was implemented to assess the combinations of variables that most

successfully predict whether or not somebody experiences decisional conflict, intends to

screen, or has been a participant of CRC screening in the past.

FOBt intention was best predicted by low interpersonal embarrassment and high

self-efficacy, followed by low bowel disgust, high test-efficacy, and low bodily

embarrassment. Cognitions appeared to be more important in distinguishing intenders

of colonoscopy, with self-efficacy and risk perception predictive of intention, and

screening bias predictive of no intention. The most important emotion was a fear of the

screening procedure. In relation to these screening intentions, high decisional conflict

was best predicted by high screening bias, low self-efficacy, and low test-efficacy.

Important emotion discriminants of decisional conflict included a fear of screening

procedures, interpersonal embarrassment, and bowel-disgust.

Lastly, participation in CRC screening was most successfully predicted by the

health and demographic factors of screening advice, having a gastrointestinal condition,

and older age, however screening bias also played a part in explaining non-

participation. These discriminants and their relation to the outcome category were

summarised in Table 10.10. The results are discussed in Chapter 11.

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Table 10.12

Summary of Significant Associations with the Outcome Variables

FOBt intention Colonoscopy

intention

Screening

participation

Decisional

conflict

Demographic/Health

Age (+) GP advice (+) Age (+) Age (-)

Past screening

(women only) (+)

Gastro condition (+) Professional

employment (-)

Professional

employment (+)

Family history (+) GP advice (+) Screening advice (-)

Gastro condition (+) Past screening

(women) (-)

Family history of

CRC (-)

Cognition

Self-efficacy (+) Self-efficacy (+) Screening bias (-) Self-efficacy (-)

Test-efficacy (+) Test-efficacy (+) Cancer worry (+) Test-efficacy (-)

Cancer worry (+) Risk perception (+) Risk perception (+) Risk perception (-)

Screening bias (-) Cancer worry (+) Test-efficacy (+) Knowledge (-)

Screening bias (-) Self-efficacy (+) Cancer worry (-)

Screening bias (+)

Emotion/Social

Interpersonal emb (-) Interpersonal emb (-) Interpersonal emb (-) Fear of procedure

(+)

Bodily emb (-) Bodily emb (-) Social support (+) Interpersonal emb

(+)

Bowel disgust (-) Bowel disgust (-) Bodily emb (+)

Fear of procedure (-) Judgement concern

(+)

Social support (+) Bowel disgust (+)

Blood draw disgust

(+)

Odour disgust (+)

Social support (-)

Secondary Outcome

Decisional conflict (-)

Note. ‘emb’ = embarrassment.

N = 240.

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CHAPTER 11

GENERAL DISCUSSION


This chapter reviews the findings of two studies which share a related, central

aim, including an understanding of the relationships between key cognitive (including

cognitive bias), discrete emotion and social variables with CRC screening intentions

and behaviour, with a focus on the combinations of these variables that most

successfully distinguish between those who do or do not participate and those who do or

do not intend to participate in CRC screening. A secondary aim in both studies was to

examine the level of decisional conflict involved in screening decisions, a state that can

hinder the implementation of screening intentions.

The literature on health behaviour intentions is immense, however its emphasis

has been on the predictive power of established health behaviour models, whereas

experiential cognitive processes and discrete emotions have undergone less empirical

investigation. On the whole, this investigation has aimed to enhance the understanding

of CRC screening reluctance. The antecedents to forming intentions to screen for cancer

are many and multifaceted; however there is emerging empirical interest in the role of

emotion in cancer screening participation and on the influence of biased thinking

toward health behaviours and testing. The results of both studies converged on several

important findings.

Across Study 1 and 2, emotions and screening bias performed well in

comparison to commonly measured health behaviour constructs (such as risk perception

and test-efficacy) in explaining both screening intention and participation. The general

findings indicate that there is an important role for these emergent research areas in the

explanation of health behaviour, particularly in the interpretation of screening

reluctance or non-participation. This chapter will first focus on both the separate and

integrated findings of these studies (Section 11.3), and particularly on the capacity of

cognitive biases (screening bias) and emotions to account for reluctance to participate in

CRC screening, relative to more commonly explored social-cognitive variables. In the

latter part of the chapter (Section 11.8), limitations of the research are considered,

followed by a discussion of the project’s implications, recommendations, and

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suggestions for future research (Sections 11.9 and 11.10). To conclude, salient aspects

of this investigation are summarised (Section 11.11).

11.2 Brief Review of Project Aims

11.2.1 Aims of Study 1. The methodological aim of Study 1 was to assess the

psychometric evidence for scales that may be useful in the measurement of emerging

constructs involved in screening (e.g., medical embarrassment). A further aim of the

study centred on validating the relationships between screening intentions with

cognitive, emotion and social factors, primarily seeking to investigate the magnitude of

relationships between under-explored constructs such as negative emotion (fear, disgust,

medical embarrassment) and screening bias, with screening intentions. A number of

hypotheses were also formulated regarding the relationships between demographic- and

health-status with screening intention, and these are discussed in Section 11.3.

11.2.2 Aims of Study 2. According to the findings of Study 1, screening bias,

self-efficacy, negative emotion, social support, and several demographic and health-

related variables were moderately to strongly associated with screening intention. The

aims of Study 2 focused on ascertaining whether these variables were replicated as

correlates of screening intention in an older sample, and were effective discriminants of

intenders/non-intenders and participants/non-participants of CRC screening, as well as

people who experienced high and low levels of decisional conflict. Specifically, aims

were to evaluate the magnitude of traditional health behaviour constructs such as self-

efficacy and risk perception in predicting the outcome variables, and compare these

effects with the less-explored variables of screening bias and emotion.

11.3 Discussion of the Findings in Relation to Demographic and Health Status with

Screening Intention and Decisional Conflict

The hypothesis in relation to demographic variables, that older age, being partnered,

holding private health insurance, and being of male gender, would be associated with

greater screening intention and lower decisional conflict, was partially supported (H1a)

across both studies.

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The hypothesis in relation to health variables (H1(b) in Study 1, and H1(a) in

Study 2), that history of general screening, history of CRC screening, gastrointestinal

condition(s), and family history of CRC and gastrointestinal illness would be related to

greater screening intention and lower decisional conflict was partially supported.

Multivariate predictions about both health and demographic variables in Study 2 (H2 to

H5) were partially supported.

11.3.1 CRC screening in relation to age

Older age was associated with greater screening intention and less conflict; a

finding maintained by previous research indicating that older age is a strong predictor of

CRC screening participation (Javanparast et al., 2010; Partin et al., 2010). The evidence

for such a relationship is vast, although some studies suggest that screening wanes again

for those >70 years, (e.g., Ko et al., 2005; Seef et al., 2004), suggesting it may be best

described by a U-shaped relationship. This decline in screening may be due to other,

competing causes of mortality (Lewis et al., 2010), or because of a lack of knowledge

and awareness (Berkowitz et al., 2008). However, it can be difficult to determine how

screening intention differs across age groups when many studies explore this decision

only in those >50 years, with no upper age limit. Few studies explore screening

intentions with younger (or even discretely older) populations (see Quarini & Gosney,

2009, for a study of older participants >70 years).

The uniquely younger sample employed in Study 1 (with an average age of 27

years) offers insight into the potential determinants of screening intention in younger

people, when CRC screening decisions are not proximal. Precautionary analyses (see

Appendix E) showed that younger participants (aged 18 to 30) reported lower screening

intention than older participants (aged 31 to 75), possibly reflecting that few of the

younger participants had considered bowel screening in earnest, in lieu of any

significant objective CRC risk. Younger participants also indicated lower self-efficacy

and higher levels of medical embarrassment. Embarrassment in particular may echo

limited experience within the health system, and this is conceivably a variable that

diminishes over repeated exposure in health and medical settings, enabling patients to

feel more comfortable discussing health concerns of an intimate or private nature, and

over time contributing to greater self-efficacy to participate in health checks. This

possible relationship will be discussed further in Section 11.7.5. Disgust and fear were

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almost significantly higher in the younger age group (p = .05 and .07, respectively),

again suggesting these negative emotions, in relation to health tests, may weaken with

age.

It was also hypothesised that older age would relate to greater certainty in

decision-making about screening. This was found to be the case, with older participants

reporting significantly lower decisional conflict. Few studies have explored decisional

certainty in CRC screening, however a small study of CRC screening decision-aids in

the elderly (aged >75; Lewis et al., 2010) show that participants were better prepared to

make screening decisions and felt clearer about their values.

Study 2 therefore augments the findings of Study 1, with older participants

reporting significantly less decisional conflict about screening, and higher levels of

prior screening. Older age was weakly related to (and predicted) FOBt intention, but

was unrelated to colonoscopy intention, and not a significant discriminant of intending

or not intending to screen by colonoscopy, suggesting that in an older group, age may

be less important during the formation of intentions than emotion and social-cognitive

variables, but more important in decision certainty and implementation. This difference

is noteworthy, as a number of studies have failed to find the multivariate importance of

age as a predictor of screening (e.g., Jandorf et al., 2010).

11.3.2 CRC screening in relation to partnership status

The relationship between partnership status and intention was supported in

Study 1, with partnered participants reporting significantly higher intentions to screen

than un-partnered participants, but not for decisional conflict, where there was no

observed difference between partnership statuses. Being partnered was also related to

older age, past screening participation, and self-efficacy, variables related to screening

intention and participation. It is difficult to determine the cause and effect of this

relationship, that is, is there a selection bias for healthier individuals marrying, or does

being married improve health behaviour (van Jaarsveld et al., 2006), or whether it is an

artefact of partnering being related to other variables key to screening intention. In the

present investigation, Study 1 suggests that the positive effect of being partnered can be

observed at the level of behavioural intention, however partnership was unrelated to

screening intention or prior screening participation in Study 2.

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As the relationship between marriage and healthy lifestyle and outcomes is well

documented (DiMatteo, 2004; Power et al., 2009), the lack of effect for partnership

status in Study 2 may be related to the small sample of single participants (just 8% of

the sample), while 73% were partnered, and a further 14% separated or divorced. It is

also possible that there was a significant effect of partnership status in Study 1 as it

included a broader age range within a younger, predominantly single, sample (34%

partnered). Being partnered is likely to be a major source of functional social support,

which is discussed in Section 11.6.

11.3.3 Health insurance, socioeconomic status, and CRC screening

The results of Study 1 and Study 2 did not support the prediction that having private

health insurance would be associated with increased screening intention and lower

decisional conflict. Further, Study 2 indicated that socioeconomic status (SES) was not

associated with decisional conflict, screening intention, or participation. These findings

are curious in that they deviate from extensive literature indicating a relationship

between insurance and screening intention and/or participation, and between SES and

screening. Jandorf et al. (2010) conducted a recent study in the US showing a strong

relationship between increased participation in colonoscopy screening and increased

levels of health insurance cover; consistent with recent findings in the UK (Weller et al.,

2007).

In many Western countries, including Australia, possessing health insurance is a

reflection of higher SES status (Palangkaraya et al., 2009), as found in the present

study. Both SES and insurance have been related to CRC screening participation and

subsequent earlier cancer detection (Emmons et al., 2008; Phillips et al., 1999; Ward et

al., 2008), as is the case in Australian research on the NBCSP (see Javanparash et al.,

2010). Conversely, there has been some indication that health insurance and SES may

not have as large an impact on screening as other demographic variables. Ko et al.

(2005) found that there was little increase in screening 2-3 years after the introduction

of insurance coverage in a US sample. Two Australian studies (Ananda et al., 2009;

Cole, Young, Esterman, Cadd, & Morcom, 2003) have similarly shown no significant

difference in participation between high and low SES participants who were classified

according to the Index of Relative Socioeconomic Disadvantage (IRSD) national

deciles, the same system used to classify SES in the present investigation. In terms of

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screening outcome, Hall et al. (2005) found no effect of private health insurance or SES

residency on CRC surgery uptake or survival. More recently, however, national data

from the Australian NBCSP is supportive of a link between SES inequalities and poorer

screening rates (AIHW, 2009), suggesting this area would benefit from further

investigation.

The present investigation (Study 2) indicated disproportionately high health

insurance, with 71% holding health insurance, but having no relationship with

screening. While it is possible that the limitations of Study 1 and 2 contributed to the

lack of power in predicting screening from health insurance, insurance coverage and

SES may also be less powerful determinants of screening intention and participation

than other demographic factors. Alternatively, the assessment of SES by geographical

indices of deprivation may be an insufficient measurement, and a more direct measure

including an individual’s household income may better reflect proximate health

intentions and behaviour. With no substantive literature on the relationship between

decisional conflict and health insurance status, conclusions cannot be drawn about a

lack of relationship in the present investigation (across both studies), however the

prediction that health insurance enables greater confidence and certainty regarding

participating in screening has scope for further empirical consideration.

11.3.4 Gender in CRC screening

Gender effects on CRC screening were hypothesised to predict greater intention

and prior participation, and lower decisional conflict among men in the present

investigation, however neither study revealed any gender difference (Section 11.3.5

discusses gender differences in relation to past screening behaviour). Interpreting these

findings is two-fold. First, difficulty arises from an incongruent literature on gender

differences, with a range of studies indicating that men are more likely to screen for

CRC than women (Brennenstuhl et al., 2010; Guessous et al., 2010; Janz et al., 2003;

Sewitch et al., 2007) and a number of studies indicating the reverse (Ananda et al.,

2009; Javanparast et al., 2010). Recent findings from the Australian NBSCP revealed

participation in the NBCSP was 19% more likely for women (AIHW, 2009). In the

Adelaide (South Australia) cohort of the program, women were slightly more likely to

participate (51.3%) than men (48.7%), but this difference was not significant

(Javanparast et al., 2010).

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It was posited, but not supported, that women are less likely to screen than men.

There is a sizeable literature indicating that gender differences do exist in screening

participation, albeit with debate about the direction of those differences. Some authors

suggest that women receive less GP advice to screen for CRC, which is an

underappreciated risk for women (Donovan & Syngal, 1998), and possibly reflecting a

provider bias.

Other authors have argued that certain emotions may be experienced more

strongly by women. It has been reported that females experience enhanced mental

imagery and feel emotion more intensely than males, and that this may be linked to

differences in emotional responsiveness (Loewenstein et al., 2001), however whether

these gender differences translate into decision-making is not well explored, particularly

as there may be a number of factors that degrade the negative impact of emotion (e.g.,

social networks and support), some of which may be more available to women.

This variation between men and women’s CRC screening may have origins in the

type of test under investigation. A closer inspection of studies differentiating between

type of CRC test generally indicates that women are more likely to participate in FOB

testing (von Euler-Chelpin, Brasso, & Lynge, 2010), but less likely to undertake

endoscopic screening (Janz et al., 2003). This may explain Australian NBCSP findings,

which are based on a mail-out of the FOBt kit, a test that is shown to have higher

proportions of women participants. Reasons for lower male participation on the FOB

test may stem from a dearth of opportunity to participate in organised national cancer

screening programs in Australia (for example, there is no prostate-screening program).

Australian women on the other hand have been recruited, and engaged, in organised

screening programs specific to female cancers (cervix, breast) for a number of years. In

support of this observation, Powers and colleagues (2008) showed that females were

more likely to be non-intenders and also non-attenders of colonoscopy screening, a

finding also documented by a US study (Ko et al., 2005) where women were less likely

than men to have participated in invasive (endoscopic) CRC screening tests.

The second aspect involved in interpreting the present findings is the smaller

male sample size across both studies, limiting the power in accounting for variance in

men’s CRC screening intention and behaviour, and potentially obscuring any weaker

effects related to men’s participation in CRC screening.

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11.3.5 Past cancer screening behaviour

Participation in CRC screening has been associated with engaging in a range of

screening for various illnesses. In line with this literature, the present investigation

found that past screening behaviours (including CRC screening) were significantly

related to higher intention to screen for CRC in general (Study 1 and 2). Study 2 also

found support for the hypothesis that prior screening would be associated with less

decisional conflict. Against predictions in Study 1, neither past CRC screening or

general screening participation was associated with decisional conflict, however in this

sample few participants had actually screened for CRC.

In the community sample (Study 2), when the sample was stratified by gender,

greater decisional conflict was related to less past screening for women only (not for

men). It is important to consider gender differences as they may reflect different

histories of screening opportunity, as already mentioned. Therefore women’s familiarity

with the processes involved in screening may reduce uncertainty during the decision

process for CRC screening.

11.3.6 Gastrointestinal health

There was only borderline support in Study 1 for the expectation that having a

gastrointestinal condition would be related to screening intention (p = .055), and no

difference in decisional conflict between those with and without gastrointestinal illness.

Study 2 showed stronger evidence for a relationship between having a gastrointestinal

condition and both colonoscopy intention and participation in CRC screening. Indeed,

having a gastrointestinal illness was the second strongest predictor of CRC screening

participation in the discriminant function analysis, after GP advice. As a fixed variable

(i.e., one that is not changeable by intervention), these results imply that the experience

of gastrointestinal illness such as IBS or polyps is a likely stimulus for screening. Such

conditions may increase awareness of related illness and potential vulnerabilities to

CRC, as well as increase medical contact and advice. The results of the current project

are therefore consistent with the literature in which gastrointestinal illness has been

found to increase CRC screening participation. It is interesting that Power et al. (2009)

argue that one of most prominent self-reported barriers to CRC screening is being in

good health and lacking symptoms. This concept will be discussed in Section 11.5.6 as

a potential bias associated with the perceived value and necessity of screening.

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11.3.7 Family history and GP advice

Contrary to predictions, there was no relationship between family history of

CRC or bowel illness with screening intention or decisional conflict in Study 1.

However in Study 2, a substantial minority of the sample (33%) reported a family

history of CRC, and this subset of the sample reported significantly higher intentions to

screen by colonoscopy and less decisional conflict, partially supporting the hypotheses.

In the multivariate analysis in Study 2, family history of CRC discriminated between

colonoscopy intenders and non-intenders and high/low decisional conflict groups, but

was not a significant discriminant of FOBt intention or prior screening participation.

This may suggest an intention to participate in a screening test that is considered to have

a more successful record for detecting lesions in the colon, and one that has likely been

advised for persons with a family history. It is therefore noteworthy that while family

history discriminated between intenders of colonoscopy and non-colonoscopy, it did not

differentiate between screeners and non-screeners, possibly reflecting a well

documented divide between the precursors of intention and action.

Study 2 enabled a separate analysis of screening intentions by the type of

screening test, indicating a strong relationship between GP advice to screen and higher

colonoscopy (but not FOBt) intentions, more screening participation, and less decisional

conflict. That participants in Study 2 who had a family history of CRC had higher

intentions to screen by the most accurate test available (colonoscopy) and had lower

decisional conflict about this, may also reflect an awareness and increased knowledge of

the importance of endoscopic screening tests in detecting CRC, which may be

encountered through their family history, and endorsed by their GP.

Interestingly, lack of association between family history and screening intention

in Study 1 contrasted with an association between family history and colonoscopy

intention in Study 2, perhaps illustrating an age-related difference in CRC risk. The

younger sample in Study 1 reported distal perceptions for a disease that poses low

imminent risk. A sense of detachment from disease risk in the younger sample may

conceivably explain the disparity between Study 1 and Study 2. Sample age is also a

limitation that will be considered in Section 11.8.

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11.4 Decisional Conflict and CRC Screening

Precautionary analyses tested whether primary (screening intention and

participation) and secondary (decisional conflict) dependent variables were related to

each other, and this relationship was supported in both studies, where higher intention

to screen was associated with less decisional conflict. In Study 2, decisional conflict

was negatively associated with intention (both FOBt and colonoscopy), and screening

participation. In Study 1, the strongest components of decisional conflict associated

with increased screening intention were perceived decision certainty and effectiveness.

These findings are consistent with research indicating that lower decisional conflict is

associated with better acceptance of healthcare interventions and also decision

implementation, as well as being able to discriminate between intenders and non-

intenders on a variety of health behaviours (O’Connor, 1995). Often, the effect of lower

decisional conflict on health decisions is attributed to more informed choices and clarity

about personal values, which leads to perceptions of effective decision-making (e.g., see

Davison, Kirk, Degner, & Hassard, 1999), and therefore the provision of information is

thought to be central to enabling such outcomes.

While no causal relationship between decisional conflict and intention can be

imputed from either Study 1 or Study 2, decisional conflict may be better measured for

its causal relationship with participation, not screening intention. Indeed, Study 2

showed that decisional conflict discriminated between screeners and non-screeners,

supporting the hypothesis that low decisional conflict is linked with higher

participation. Therefore decisional conflict may be an important link between intention

and action, and therefore an important indicator of subsequent decision implementation.

11.5 The Role of Cognitive Variables in CRC Screening: Discussion of Findings in

Relation to Cognitive Hypotheses

By far the most common approach for examining health behaviour in

psychology is via social-cognitive theories, including the health belief model (Becker,

1974), the TRA, and the TPB (Ajzen, 1991). Together, such theories provide a

conceptual framework that emphasises the importance of cognitive constructs such as

self-efficacy, perceived risk (or perceived susceptibility), as well as social norms and

246

support, while emotions are sometimes encompassed as “background variables” (e.g.,

Fishbein, 2008, p. 835).

An issue that has arisen in the literature in recent years about conventional

theories of health behaviour is the absence of an evaluation of cognitive shortcuts and

biased beliefs. This absence may be an upshot of the difficulty in measuring automatic

cognitive responses such as heuristics. However many of these heuristics can be

captured by their associated biases, which can be persistent, enduring and robust beliefs.

Therefore, it was predicted in this investigation that major psychosocial variables (self-

efficacy, test-efficacy, risk perception, knowledge, and worry) would increase intentions

to screen and predict prior screening participation, while an associated bias of the

representativeness heuristic (screening bias) would predict lower intention and non-

screening behaviour. These hypotheses were mostly supported across both studies.


In Study 1, risk perception was significantly related to higher screening

intention, but not to decisional conflict, providing partial support for the hypothesis that

greater risk perception is associated with intention to screen for CRC and lower

decisional conflict. In the second study based in the community with persons of CRC

screening age, risk perception was a significant correlate of lower decisional conflict,

higher colonoscopy screening intention, and greater participation in screening (but not

of FOBt intention).

Of interest in both studies, was the stronger relationship between risk perception

and cancer worry than between risk perception and screening intention. Certainly, the

present research parallels earlier opinion that comparative risk perception of cancer

(perceived risk relative to others) is related to greater worry (see Lipkus et al.’s

longitudinal study, 2005); with the findings in Study 1 indicating that comparative risk

perception was significantly related to higher screening intention, but only when there

was cancer worry in relation to CRC. Perception of comparative risk may be more

prone to the optimistic bias often found across comparative risk ratings (Klein &

Weinstein, 1997), and may therefore be more likely to moderate risk-related cancer

worry. The findings correspond to a large literature on the relationship between worry

and risk perception (e.g., Vernon et al., 2001; Zajac, Klein, & McCaul, 2006), and

between worry and CRC screening (Collins et al., 2000; McCaul & Mullins, 2003;

247

Moser et al., 2007). The relationship between risk perception and worry with screening

intention is also consistent with observations in breast cancer research (Bjorvatn et al.,

2007; Lipkus et al., 2005; Stark, Bertone-Johnson, Costanza, & Stoddard, 2006), where

both risk perception and worry require awareness of susceptibility to the disease.

It may therefore be possible that worry arises from a perception of risk of a

disease, and it is the worry per se that directly and positively influences intentions to

engage in preventive health behaviour. Therefore risk perception is arguably a

necessary precursor to developing disease-related concerns and worries. As suggested

by the results in Study 1, Chapman and Coups (2006) argue that worry is directly

related to both personal assessments of risk and to preventive health behaviours,

sometimes predicting interest in screening over and above risk perception (Cameron &

Diefenbach, 2001).

Study 2 showed that risk perception was predictive of past CRC screening

participation (H4), that is, participants who had taken part in CRC screening reported

higher levels of risk perception than participants who had never undergone screening.

This finding is not unique, and many studies have reported higher risk perceptions in

intenders of screening as well as amongst CRC screeners. The mechanisms by which

risk perception affects screening remain unclear, and some researchers have found no

direct relationship between perceived risk and screening (Helzlsouer et al., 1994).

Likewise, risk perception did not discriminate between FOBt intenders and non-

intenders in Study 2, but did separate colonoscopy intenders from non-intenders. This

difference across screening tests may be attributed to the nature of the FOBt as a

noninvasive and simpler test in comparison to the greater commitment required for

engaging in structural CRC exams such as colonoscopy. The worth and value of

engaging in a time-consuming and invasive structural bowel screening test may be

related to the level of vulnerability perceived in relation to that disease. Alternatively, a

more discriminating test, such as colonoscopy, may be intentionally sought if a person

believes himself or herself to be at significant risk (or is told they are at risk by their

GP).

In summary, the present findings suggest that comparative risk is associated

with screening intention, but more strongly associated with cancer worry. Risk

perception also appears to discriminate between people who intend to screen by

248

colonoscopy and who actively participate in screening (according to self-reports of past

behaviour). Overall, level of risk perception of CRC in the sample was low, with just

19% of participants in Study 2 believing themselves to have a higher than average

chance of developing CRC, however, perceptions of comparative risk appear to be

important in cancer screening participation, while a sense of invulnerability to cancer

may hinder screening.

11.5.2 Self-efficacy

Self-efficacy beliefs were anticipated to correlate with screening intention and

decisional conflict in Study 1, and discriminate between intenders and non-intenders,

high and low decisional conflict, and screeners and non-screeners in Study 2. In Study

1, amongst the cognitive variables, self-efficacy beliefs were the strongest correlate of

intention to screen, and one of the strongest negative correlates of decisional conflict.

Extant data support this finding, suggesting self-efficacy is a consistently powerful

correlate of CRC screening intention (DeVellis et al., 1990; McQueen, Tiro et al.,

2008). Study 2 provides evidence for the direct determinacy of self-efficacy on

screening intention, which is of considerable importance both theoretically (for

example, Bandura’s [1982] theory of self-agency) and for practical efforts in enhancing

screening participation. As lower decisional conflict is associated with the

implementation of intentions, as is higher self-efficacy, the present study also provides

one of the first findings on the predictive relationship between these variables. It was

found that self-efficacy was one of the strongest variables predicting decisional conflict

about intentions to screen for CRC.

Perhaps unsurprisingly, self-efficacy was also strongly related to being older

(Study 1), suggesting that confidence to participate in screening increases over time,

and may indirectly be augmented by successful medical and health-related experiences

and contact with health professionals. This implies a transactional or reciprocal

relationship (Bandura, 1982), where self-efficacy beliefs may be reinforced by

successful participation in medical experiences, in turn increasing self-efficacy beliefs

to engage in future testing.

With self-efficacy a primary correlate and predictor of CRC screening intention,

it is important to recap that the samples in both Study 1 and 2 had high levels of

education and therefore literacy, which is a variable that is independently associated

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with self-efficacy to take part in screening (von Wagner et al., 2009). The levels of self-

efficacy reported in this research may therefore be an inflated estimate, above that of the

general population.

Study 2 broadened these findings by establishing a discriminant role for self-

efficacy between low and high decisional conflict participants and CRC screening

intenders and non-intenders (for both FOBt or colonoscopy). Against predictions, self-

efficacy did not differentiate between past CRC screeners and non-screeners. Instead

there may be a role for self-efficacy in augmenting intentions and reducing decisional

conflict, which subsequently facilitates the implementation of those intentions, but is

less important in the action phase of decision implementation. A logical deduction from

these findings is that higher confidence to participate in CRC screening may facilitate

the perception that barriers and difficulties (emotional, practical, or social) can be

overcome. Indeed, self-efficacy was found to partially mediate fears about screening,

and fully mediate the effect of medical embarrassment on intentions to screen (Study 1)

(discussed in Section 11.7.5). Overall these results imply that this is a cognitive variable

with direct positive, powerful, and far-ranging implications in CRC screening intention.

11.5.3 Test-efficacy

The prediction that perceptions about the test’s effectiveness (test-efficacy)

would be positively associated with screening intention and negatively with decisional

conflict was supported in both studies. Additionally, in Study 2 it was predicted that

test-efficacy would be positively related to participation in screening tests and would

differentiate between intenders/non-intenders, screeners/non-screeners, and low/high

decisional conflict. This prediction was partially confirmed, with test-efficacy

predicting intenders from non-intenders but not screeners from non-screeners. This

finding supplements a number of studies indicating a positive relationship between test-

efficacy beliefs and screening intention (Clavarino et al., 2004; Hawley et al., 2008;

Janz et al., 2007; Shokar et al., 2010), but departs from the literature which suggests

test-efficacy is a cue to CRC screening compliance (Mack et al., 2009).

While test-efficacy was found to be a positive correlate of intentions in both

studies, it discriminated only between intentions, not actions, in Study 2. There are a

number of possibilities for non-significant findings. Firstly, test-efficacy beliefs may be

better assessed by separate measures depending on the type of screening test (FOBt or

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colonoscopy). One US study has shown that preference for a specific screening test is

significantly influenced by exposure to information about test-specific attributes.

Shokar et al. (2010) found an initial preference for screening by FOBt (59%), however

this majority shifted to colonoscopy after exposure to each test’s attributes (54%). Of

most importance to participants when it came to decision-making was test-accuracy,

scientific evidence for test efficacy, amount of colon examined, and then sedation.

Shokar and colleagues emphasise the importance of presenting test-efficacy information

to all participants, given that fixed factors such as demographic status cannot reliably

predict patient preference. This view fits with the present results, particularly in Study 1

where participants were younger and less likely to have been exposed to test-specific

information aside from the brief description provided in the survey. Information

highlighting test options and specific test attributes may contribute to test-efficacy

beliefs that are influential on intentions to screen, but only if they relate to the test under

consideration and not CRC screening tests in general.

In Study 2, test-efficacy beliefs differentiated intenders from non-intenders of

both FOBt and CS screening. While Study 1 and 2 used identical descriptions of the

screening tests, many participants in Study 2 had taken part in screening previously

(62%) and were therefore likely to be more familiar with the process of screening.

However, beliefs about test-efficacy failed to differentiate between persons who had

actually participated in screening and those who had not screened.

Patients may find the cognitive appraisal of test attributes and efficacy a

demanding process, which is a finding previously reported by Schroy, Glick, Robinson

and Heeren (2007). As such, and in a dual-process framework, decision-makers may

rely more heavily on cognitive heuristics, increasing the possibility that biases will arise

and influence the decision outcome, instead of decisions weighted on the objective

benefits and risks of each test.

Another possible reason for the lack of discrimination between screeners and

non-screeners, may be that test efficacy is valued differently across fixed factors, such

as educational attainment. While there was no relationship between educational

attainment and test-efficacy in the present research, this may be attributed to overall

high levels of beliefs about test-efficacy, limiting the variance on this variable. Earlier

research suggests test-efficacy may interact with educational status. Salkeld, Solomon,

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Short, and Ward (2003) reported that the test’s perceived effectiveness was significantly

more concerning to respondents with high levels of education than those with lower

levels of education. A number of studies have identified education and SES-related

factors in the differential valuation of test-attributes and efficacy beliefs (Holmes-

Rovner et al., 2002), generally suggesting low income is related to poorer screening

rates (Phillips et al., 1999). Test-efficacy may therefore be a variable that is particularly

sensitive to populations separated by SES indicators such as educational background

and income. On the whole, the literature indicates that beliefs about the efficacy of

screening tests are very important in predicting intentions (Salkeld et al., 2003), and the

present findings support this in relation to FOBt intentions and decisional conflict. The

finding that low test-efficacy beliefs were amongst the top four variables predicting

FOBt non-intention (Study 2) may be partly explained by the weaker sensitivity and

specificity of the FOBt compared to colonoscopy.

11.5.4 Cancer worry

The hypotheses in relation to cancer worry were generally supported across

studies, maintaining the supposition that cancer worry is both related to, and a

predictor of, screening intention. In Study 1, cancer worry was significantly and

positively associated with screening intention but unrelated to decisional conflict. In

Study 2, cancer worry was significantly negatively related to decisional conflict, and

positively related to both types of intentions, and also participation.

Since high cancer worry has also been attributed to lower screening intent and

action (Mack et al., 2009), the present results will be discussed in relation to an

incongruous literature. The research in favour of the influence of worry on screening

intention and behaviour suggests that worry has a direct effect on precautionary health

behaviour (Peters, Slovic et al., 2006), indicating worry is a common response to

perceiving risk, and may be better than risk perception at predicting precautionary

behaviour. Certainly, the findings of Study 2 suggest that perceptions of vulnerability to

CRC may affect screening via an increase in CRC worry, rather than directly shaping

screening intentions themselves. Screening intentions may be motivated by a desire to

reduce the perceived threat (and risk) that is contributing to worry.

Contradictory conclusions about the role of worry in cancer screening can often

be attributed to operational definition and conceptual issues. For example, there is

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discrepancy in the conceptualization of worry as either a cognitive construct (e.g.,

Borkovec et al., 1983; McCaul & Mullins, 2003; Peters, Slovic et al., 2006) or an

emotion (e.g., Bowen et al., 2003; Hay et al., 2006). As such, cancer worry is

sometimes treated synonymously with the emotion constructs of cancer anxiety and fear

(Bowen et al., 2003). For example, Hay et al. (2006) assessed worry with two items,

one relating to worry directly, and another questioning the extent that respondents were

afraid of a CRC diagnosis. This distinction is important because these unique

conceptualizations may have generated contradictory findings. When studied as an

anxiety or fear response to the threat of cancer, it is often reported that worry impedes

cancer screening (Janz et al., 2007), yet when conceptualized as a cognition it is often

thought to motivate screening (McCaul & Mullins, 2003; Wardle et al., 2000).

While there is some overlap in the physiological features of anxiety and worry,

they are two separable constructs, and clearer delineation in the literature will promote a

stronger understanding of the relationship between worry and cancer screening. A valid

and standardized measure of cancer worry, like that developed for breast cancer (the

Breast Cancer Worry Scale; Lerman et al., 1991), will be central to the empirical

assessment of worry as a cognitive construct. As it stands, use of an adaptation of the

Breast Cancer Worry Scale in the present study led to support for the role of worry in

fostering CRC screening intention and behaviour.

As discussed in Section 11.5.1, worry and risk perception are strongly related

but unique cognitions. Worries leading to the enactment of preventive health behaviour

indicate that the behaviour is a coping strategy that aims to reduce worry by directly

addressing the cause of that worry (an active coping style). On the contrary, intention or

participation in screening itself may or may not alleviate perception of risk (hence

screening is a repeatable preventive health behaviour, requiring adherence to a

screening schedule to effectively reduce the risk of associated morbidity or mortality).

Therefore, worry may fluctuate with the enactment or abstinence from previous health

behaviours.

This also suggests that risk perception may operate through its effects on more

proximal antecedents to screening intention, such as cancer worry. This moderation

explanation of the mechanism of risk perception is argued by a number of researchers.

For example, the precaution adoption process model, where risk perception is thought to

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indirectly influence screening by contributing to variables that more directly elicit a

health behaviour, while being insufficient in itself to bring about behaviour change or

participation (Weinstein, 2003). This finding is also partly supported by McQueen and

colleagues (2010), who reported mediating and moderating pathways of perceived

susceptibility to CRC across four models, and support the theoretical models which

posit perceived susceptibility is a necessary but not sufficient determinant of intention

and behaviour. Therefore, intention to screen for CRC may be partly dependent on a

certain degree of cancer worry in combination with perception of risk of CRC.

11.5.5 Knowledge

It was anticipated knowledge would have a positive relationship with screening

intention and negative relationship with decisional conflict in Study 1 and 2, however

only the former prediction was supported. In Study 2 knowledge was expected to

positively relate to, and predict, both types of intentions and screening participation, as

well as negatively relate to and predict lower decisional conflict. Only a relationship

between knowledge and decisional conflict, and knowledge and FOBt intention was

supported. However, knowledge did not predict intentions.

Both samples revealed a high level of knowledge about CRC; inconsistent with

a large literature reporting low levels of public knowledge about CRC (Camilleri-

Brennan & Steele, 1999; Paul et al., 2003). Interestingly, scores on knowledge appear to

be higher in the Study 1 population (M = 9.27, SD = 1.82) than in Study 2 (M = 6.39,

SD = 2.80). In Study 1, high levels of knowledge were found about a disease of which

the majority was not yet at risk. This may be a consequence of high educational levels

and SES across the sample, variables that have been related to health awareness and

preventive behaviour (Emmons et al., 2008; James et al., 2008).

The results of Study 2 were consistent with the relationship found between

knowledge and screening bias in Study 1, but contrasted with Study 1 in relation to the

dependent variables. Instead, knowledge had a moderate negative relationship with

decisional conflict, and a weak positive relationship with FOBt intention. Knowledge

was unrelated to colonoscopy intention or screening participation, and discriminant

function analysis showed that knowledge did not discriminate between the dependent

variables of FOBt intention/non-intention, colonoscopy intention/non-intention, or

screening participation/non-participation, but did predict individuals with lower

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decisional conflict. This is inconsistent with several studies where knowledge has been

both a significant correlate and predictor of CRC screening intention (Geiger et al.,

2008; Gili et al., 2006; Kelly et al., 2007; Powe et al., 2004; Zheng et al., 2006),

however the relationship between higher knowledge and lower decisional conflict is

consistent with the limited available literature (Dolan & Frisina, 2002).

These findings suggest that the dissemination of information about CRC in an

effort to increase public knowledge may facilitate CRC screening intentions,

particularly for FOBt. Of note in both Study 1 and 2, was the significant positive

relationship between knowledge and self-efficacy. Although a cross-sectional design

was employed, and the relationship was not hypothesised, it is logical that greater CRC

knowledge encourages self-confidence to screen for CRC. The literature shows strong

relationships between knowledge and self-efficacy in the facilitation of behavioural

outcomes, and it is widely argued that self-efficacy is the essential link between

knowledge and the execution of a particular planned course of action. Bandura (1989)

argues that beliefs about self-efficacy direct how an individual will act upon their extant

knowledge and skills. That is, an individual may have exceptional knowledge about

CRC, its risks, and the importance and value of screening, but without the self-

confidence to successfully engage in preventive screening their likelihood of

participating would be reduced. The influence of knowledge on CRC screening may

therefore be greatly dependent on reaching adequate levels of self-efficacy.

Along this line of argument, it is possible that knowledge is not a direct or

immediate precursor of screening intention, and is instead operationalised via other

cognitive processes, such as self-efficacy. For example, several studies have suggested

that knowledge is unrelated to screening use and instead impacts on other factors that

are more directly related to screening, such as optimism about cancer prevention

(Brown et al., 1990). Therefore, the mechanism by which knowledge influences

screening intention and participation may be via more imminent antecedents, and as

such may still be an important and amenable target for public education campaigns,

which are purported to be effective in improving CRC knowledge in the general

population (Paul et al., 2003; Zheng et al., 2006).

Knowledge may improve in the general population with the advent of a national

screening program, which carries with it an increase in the opportunity for exposure to

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information about CRC epidemiology, risk factors, and screening tests. Zheng et al.

(2006) suggest that a 10-year national CRC screening program was the reason behind

very high levels of knowledge reported in a Japanese sample, while an Australian study

looking at improvements across knowledge concurs that cancer knowledge appears to

increase in line with public education and screening campaigns (Paul et al., 2003). This

may also explain why the public has a better understanding of cancers where there is a

national screening program in place (e.g., breast cancer) particularly in comparison to

low levels of CRC knowledge in the general population (Camilleri-Brennan & Steele,

1999; Paul et al., 2003).

11.5.5.1 Knowledge and screening bias.

In both Study 1 and 2, greater knowledge about CRC was related to higher

screening intention and fewer biases about the value of CRC screening, however both

relationships were only weak to moderate. This suggests the dissemination of

information about CRC screening may be associated with higher screening, but also

subject to differences in interpretation and preconceptions about screening, manifesting

in a number of biases about the importance of screening to various sub-groups within

the broader population targeted by public campaigns. A clearer understanding of the

way that various aspects of CRC information is understood is important as it may

differentially encourage or inhibit screening in different groups of individuals.

Knowledge was partly explored in the present investigation to exclude the

possibility that screening bias is only, or moderately, a function of low CRC knowledge.

It was hypothesised that screening bias is not a sole function of limited knowledge

about CRC, and is instead an aspect of peripheral or experiential cognitive processing in

decision-making (resulting in rapid estimations and judgements known to generate

biases). Therefore, it was argued biases could arise regardless of (limited) knowledge

alone, but as a consequence of natural limitations on cognitive processing. This

hypothesis was supported by the present research, where controlling for levels of

knowledge (which were high in the present samples) did not reduce the negative

relationship between biases and screening intention.

A lack of bowel symptoms was reported as a significant discriminant of non-

intention to screen by Powers et al. (2008), while Janz et al. (2003) found a consistent

barrier to screening was the belief that the test was not needed. These may be

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interpreted as biases associated with the need to experience symptoms for screening to

be of value (a bias that contradicts the level of knowledge reported by participants, and

which included knowledge that CRC can be asymptomatic). While a number of

researchers have argued that low levels of knowledge about CRC in the population and

frequent misperceptions may lead to low levels of screening participation (McCaffery et

al., 2003; Shokar et al., 2005; Wee et al., 2005; Weitzman et al., 2001), the present

research is the first evidence to date to suggest that biases in relation to CRC screening

are unique from knowledge deficits, and can be persistent and influential in spite of high

levels of cancer knowledge, that is, even when high knowledge is partialled out of the

relationship. While increasing CRC knowledge may facilitate screening by decreasing

reliance on heuristics during decision-making, the present findings imply that efforts to

increase public knowledge per se may be insufficient to improve screening

participation. Instead, interventions may benefit from additionally concentrating on

specific biases about screening.

11.5.6 Screening bias

In Study 1 and 2 screening bias was a strong correlate of lower intention to

screen, and was strongly associated with higher decisional conflict. These findings

were confirmed by the discriminant predictions in Study 2, with biases distinguishing

between intenders/non-intenders, screeners/non-screeners, and low/high decisional

conflict.

Evidence from discriminant analysis suggests that stronger biases about

screening significantly predict those with low FOBt intentions, low colonoscopy

intentions, low CRC screening participation, and high decisional conflict (H2 to H5).

Indeed, while screening bias was a significant discriminant for all outcome variables, it

was the most important predictor of decisional conflict, and one of the strongest

predictors of colonoscopy intentions. Interestingly, screening bias is one of the few

potentially malleable psychosocial variables predicting a history of no prior screening

(along with low risk perception and low cancer worry). These results suggest screening

bias may be one of the most important psychosocial variables inhibiting screening

participation.

The significant positive relationship between screening bias and decisional

conflict across both studies implies that screening bias is inconsistent with feeling

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informed, and with decision clarity and effectiveness. With no published literature on

this relationship, further investigation is necessary to replicate the relationship between

biases and decisional conflict.

There is a paucity of literature on the way that heuristics such as

representativeness operate in health decisions, however there is ample evidence that

heuristics and their biases are inherently present in complex health decision-making.

The judgement and decision-making literature describes biases as an inevitable trade-off

for the efficiency in information processing that heuristics often afford, and arise not

only in time-pressured decision settings but also during decisions that are complex, as is

the decision to screen for CRC. The complexity of screening decisions presents a

number of opportunities for heuristics and their associated biases to arise. For example,

patients have reported it to be cognitively challenging to process detailed test

information about CRC screening before making an informed decision (Schroy et al.,

2007).

Beliefs that certain categories of people represent ‘typical’, or representative

cancer sufferers may be understood to be an effect of the representativeness heuristic

and its potential for related biases. Individuals who do not fit into a distinctive risk

category (e.g., smoker, family history, symptomatic, obese) may view themselves as

having an exaggeratedly smaller risk of developing cancer, and many qualitative studies

have identified such cognitive barriers (e.g., Berkowitz, 2008). For example, people

without a family history may judge their own risk as disproportionately low, and weigh

the risk of others with a family history as disproportionately high. This may be partly

explained by the representativeness bias, where people without a known risk factor for

cancer may have less impetus to screen because they do not belong to what are

perceived to be the major risk factor groups of CRC (e.g., family history, symptomatic,

male, feel physically unwell).

A number of these biases argued to be a by-product of representativeness, and

relating to the perceived relevance and necessity of screening, have arisen in qualitative

research, however these are often classified as part of a general set of ‘barriers’ to

screening. For example, a number of authors have reported symptom absence and

‘feeling well’ as a frequent impediment in people’s decisions to participate in CRC

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screening (Denberg et al., 2005; Dent et al., 1983; Hynam et al., 1995; McCaffery et al.,

2001; Sutton et al., 2000; Wackerbath et al., 2005; Wardle et al., 1999).

Other researchers have been more specific in interpreting these barriers, noting

they are misconceptions about screening that do play a role in the decision outcome. For

example, Busch and colleagues (2003) conducted focus groups and found that common

and repeated reasons for not screening for CRC included, “I have no symptoms so I

don’t need screening” (p. 102). Similar misconceptions have arisen about gender, for

example, reports of women believing it to be a ‘man’s’ disease and therefore not a

relevant screening test (Donovan & Syngal, 1998; Friedemann-Sánchez et al., 2007), or

women believing they should focus on female-specific cancers (“I am not likely to have

colon cancer, but maybe breast cancer” (Busch, 2003, p.101). Some participants also

state they just “don’t need the test” (e.g., in Janz et al., 2007) possibly concealing a

number of biases about the necessity of screening (and as the present study shows, this

is possible while simultaneously having high knowledge of CRC). A review of both

qualitative and quantitative literature reporting on screening barriers, suggests many

misconceptions are consistent across findings, implying there may be a systematic

nature to this style of cognitive processing in screening decisions.

Exploring a range of biases associated with specific heuristics offers a novel

approach to psychosocial explanations of health behaviour, and a means of identifying

factors that inhibit screening. Furthermore, there is evidence to suggest that heuristics

are employed more often in older people, which has particular relevance in screening

for cancers in older age groups, as is the case for CRC (Peters et al., 2007). Identifying

and targeting misconceptions may help to ensure that a wider range of groups consider

the relevance and importance of screening. This will entail clear messages about the

importance of screening for all individuals, including those without symptoms, those

who eat well, do not smoke, have no family history or genetic risk factors, maintain a

healthy weight and lifestyle, and are women, and indeed, that the only possible

preventative measure is screening participation for early detection of pre-cancerous

polyps. Therefore, while the present findings support the importance of biases in

understanding screening reluctance, translating these and existing qualitative findings

into observable features of the screening decision, and exploring their influence on

screening systematically, should be an important goal in health research. This is

particularly so in understanding factors specific to both screening motivation and

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screening reluctance. The present findings indicate that misconceptions or screening

biases arising from the representativeness heuristic may hamper both screening

intention and participation.

11.6 The Role of Social Factors in CRC Screening

11.6.1 Social support

Predictions that social support would be related to higher screening intention

and participation and lower decisional conflict across Study 1 and Study 2 were fully

supported. Of note were significant negative relationships between social support and

bowel disgust, bodily embarrassment, interpersonal embarrassment, and fear of

procedural aspects of screening, suggesting greater social support is connected to lower

experiences of negative emotions about screening. The expected effect that social

support would allay part of the impact of negative emotion on screening intention was

also confirmed in Study 1, where social support was found to partially mediate the

effects of fear and medical embarrassment on screening intention (discussed in Section

11.7.5).

While social support did not predict FOBt intentions in Study 2, there was

evidence for the positive role of social support in predicting greater intention to screen

by colonoscopy, CRC screening participation, and low decisional conflict. A factor

within the decisional conflict scale assesses social support as an aspect of decisional

conflict; suggesting it contributes to the experience of decisional conflict and is viewed

as an external resource that is necessary in order to make and implement a decision

(O’Connor & Jacobsen, 2007). People with inadequate social support are thought to

experience greater conflict about making and implementing a decision (O’Connor,

1997). The current findings suggest perceptions of low social support are linked with

higher levels of decisional conflict. While social support was predictive of less

decisional conflict in Study 2, further investigation into the relationship between these

variables and the predictive role of social support on decisional conflict is necessary.

The relationship between social support and CRC screening intention and

participation is consistent with the literature. These findings imply that when

considering structural exams such as colonoscopy, which often involve sedation,

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patients are greatly benefited by having the functional support of friends or family. A

large amount of evidence is amassing for the health protective and beneficial role of

functional social support, although the mechanisms of its effects remain unclear, and

there are a number of studies demonstrating incongruent conclusions (Allen et al., 2008;

DiMatteo, 2004; Uchino et al., 1996). For example, Kinney et al. (2005) examined

structural (size of social network) and functional (emotional or instrumental) social

support in CRC screening participation and found only structural (social integration and

network size) support was associated with recent CRC screening. Emotional and

instrumental/tangible support were not related to screening behaviour. While Kinney

and colleagues (2005) suggested functional support may be associated with better

psychological adjustment to illness or physical functioning, rather than to preventive

health behaviours such as cancer screening, other studies have found a positive

relationship between functional support and cancer screening. In line with the present

findings, a number of studies report that perceptions of higher social support motivate

screening acceptance and adherence (Chapple et al., 2008; Gili et al., 2006). A report by

the Australian Institute of Health and Welfare (1998) also emphasised that social

isolation and a lack of social support was a significant risk factor for cancer incidence

and mortality, and part of this effect may be related to missed screening opportunities

because of limited encouragement or support due to a small or absent social network.

At the centre of these inconsistencies, it is apparent there is a need for further

elucidation of the mechanism by which social support operates, in addition to

systematic measurement. Single-item measures are often administered, which is a

recognized drawback in estimating the reliability of a construct (Nunnally & Bernstein,

1994), and precludes the ability to capture the different types and range of social

support that can characterise its effect on health behaviour. However, diverse study

designs enhance the understanding of social support in cancer screening, including

prospective cross-sectional surveys, retrospective survey designs, and tailored

interventions. Interventions have shown that social support may be effectively

manipulated to promote health behaviour changes (Caswell, Anderson, & Steele, 2009).

It is essential that well-conducted longitudinal research be performed to assess some of

the mechanisms by which social support has an effect on health (Bowling, 1994), and to

confirm the relationship between discrete emotions (both positive and negative) and

support. In summary, the present studies confirm a positive effect of social support on

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screening intention and participation, and a negative relationship with decisional

conflict.

11.6.2 Social norms

Against expectations, subjective social norms were not associated with

screening intention or decisional conflict (Study 1). Nor were they related to any other

variable, except for weak positive relationships with animal-reminder disgust and fear

of cancer, and a strong positive relationship with social support. As the aim of Study 1

was to exclude from further analysis the variables that were not demonstrating any

expected (or theoretically warranted but unexpected) relationships, social norms were

removed from further exploration in the shorter, refined survey of Study 2.

The findings that subjective social norms are unrelated to screening intention or

participation are inconsistent with a number of studies that strongly support this

relationship (Cooke & French, 2008; Honda & Kagawa-Singer, 2006; Palmer et al.,

2007). Norms have also been linked with participation in other types of screening such

as mammography (Allen et al., 2008). As subjective norms are thought to be a result of

salient normative beliefs (Armitage & Conner, 2001b), a possible reason for the null

findings in the present study could be that few participants held any prominent

normative beliefs about CRC screening, particularly given that the sample was

predominantly youthful and were unlikely to have had much experience with this type

of cancer screening. Furthermore, this group were especially unlikely to have

experienced social influence from their GP, probably one of the most relevant social

referents for CRC screening participation (Janz et al., 2003; Salkeld et al., 2003;

Vernon, 1997). Costanza et al. (2005) found that participants were twice as likely to

have decided to screen for CRC than other participants if they had also received a

recommendation from their health service provider, suggesting that the success of a

national program may benefit immensely from the involvement of the patient’s regular

doctor in the decision to be screened.

Alternatively, subjective social norms may have been less pertinent in

demonstrating a relationship with CRC screening intention because it is a private

screening test. It is therefore conceivably less likely to be discussed openly and

candidly, even within a social network, and therefore less likely to have a set of

behavioural standards or expectations. Several authors argue that norms are often weak

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or inadequate predictors of intention (Armitage & Conner, 2001b; Hevey et al., 2009),

although the operationalization of social norms has also been a major weakness in

comprehending the true performance of subjective norms in predicting intention and

behaviour (Armitage & Conner, 2001b).

Some of the standard measures of social norms might not have the scope to

reflect the substantial influence of the social environment and more diverse social

referents. Pasick et al. (2010) investigated the cultural environment and experiences

(historical, political and legal structures and practices; as well as personal and

community beliefs; and institutions and organisations within the community) of Filipina

and Latina participants, two groups chosen for their traditionally low rates of breast

screening, and as well-represented immigrant groups in the US population from which

the sample was drawn. Subjective norms in this context were less a source of normative

pressure and more about a collective decision process aimed at maintaining harmonious

relationships with empathetic and supportive individuals. The authors suggest that

extending the typical concepts of social influence to include relational culture may add

an important dimension to the analysis of social context and health behaviour. Although

there are few studies examining this concept in cancer screening research, their findings

underscore the importance of expanding on standard social concepts and integrating

approaches from different, but related, disciplines.

11.7 The Role of Discrete Emotions in CRC Screening

Emotion plays an integral and complex role in decision-making however, as

with cognitive processing, the type and strength of the emotion can have a negative or

positive impact on the decision process (Dillard & Nabi, 2006). There may be many

contexts in which emotion can distort or impede a person’s ability to select the optimal

decision. Emotion was therefore explored in the present investigation for its role in

reluctance to screen for CRC.

Health behaviours appear to be strongly associated with emotional responses,

and participation in them is often explained in affective terminology such as, “exercise

makes me feel good”, “smoking relaxes me”, or “I’m scared of going to the dentist”.

Messages about cancer and its potential outcome entail powerful affective meaning

(Dillard & Nabi, 2006), and may have consequences for behavioural intentions over and

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above variations in true probability or risk of developing that cancer (Slovic et al.,

2004). Many emotion researchers argue that when emotional and cognitive reactions

diverge, emotions often become dominant influences, and can lead to behaviour not

considered to be adaptive or optimal (Loewenstein et al., 2001).

A person’s awareness of a specific emotion when considering their health

behaviour options may be a culmination of a number of features of the behaviour, past

and present, which have been labelled to varying degrees with that emotion. When

confronted with the decision to participate in health behaviour such as cancer screening,

certain emotions may lead the individual toward a particular decision. Different

emotions can share the same general valence or ‘feeling’, for example anger and

sadness can both reflect negative affect, although they may differentially influence

health behaviour (Lerner & Keltner, 2000), therefore a discrete emotions approach is

ideal for exploring specific health decisions. The discrete emotions examined in the

present investigation for their influence on CRC screening were medical

embarrassment, fear, and disgust, and the findings in relation to these emotions are

discussed below.

11.7.1 Emotion and CRC screening

Overall, subtypes of medical embarrassment were the strongest negative

correlates of screening intention across both studies, while fear of procedural aspects

was also strongly related to both intention and decisional conflict, and in Study 2 shown

to be more strongly associated with colonoscopy intentions than FOBt intentions.

Notably, factors on the Disgust Scale-Revised were not associated with either outcome

variable. In Study 2, blood draw and odour disgust (from the DES) were related only to

higher decisional conflict, and bowel disgust related to lower FOBt intention. In

summary, the most important associations between emotions and screening intentions,

decisional conflict and screening participation across both studies were bodily

embarrassment, interpersonal embarrassment, bowel-specific disgust, and fear of

procedural aspects.

Study 2 also demonstrated that certain types of emotion were predictive of

screening non-intention but did not predict non-participation. Just one type of emotion,

interpersonal embarrassment (involving dialogue with the doctor about private or

sensitive health concerns), was associated with less participation in screening. FOBt

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non-intentions were predicted by high interpersonal embarrassment, high bowel

disgust, and high bodily embarrassment, while colonoscopy non-intentions were

predicted by high fear of procedural aspects.

These results are consistent with a growing body of evidence supporting the

unfavourable effects of negative emotion on CRC screening intention. Notably,

emotions were more relevant to screening intention and almost entirely immaterial

when it came to predicting participation. This is logical, in that at the point of enacting

the intended behaviour, emotional reactions to the anticipation of the event have ended,

and cognitive mechanisms and environmental influences such as GP advice, bowel

health, and age operate as more direct determinants of the behaviour. These findings

will be discussed below in terms of their unique effects on screening intentions and

participation (Sections 11.7.2-4).

11.7.2 Fear and CRC screening

The present findings support a negative relationship between fear of screening

and screening intention and participation. The findings partly correspond to the

literature on fear and health behaviour. The discrepancy appears to lie in the conceptual

definition of fear, which is sometimes referred to as anxiety or worry, both of which are

distinct from fear (Jones & Jakob, 1981; Whitley, 1994) and have a complex role in the

enactment of screening behaviour, acting as either motivational or avoidant cues to

behaviour (Consedine, et al., 2008; McCaul, Reid, et al., 1996).

Most studies utilising the emotional definition of fear find an inverse

relationship with screening (Jandoorf et al., 2010). Indeed, Consedine and colleagues

have proposed that the effects of fear on screening may be contingent on the unique

sources of fear in relation to testing for cancer (2004). That is, there are likely to be

fear-specific effects on screening intention. While all the types of fear examined in the

present thesis (fear of cancer, fear of embarrassment, and fear of procedural aspects of

screening) had a negative relationship with screening intention, they had varying

magnitudes of effect, supporting the premise that different types of fear may be

differentially important in cancer screening.

11.7.2.1 Fear of procedural aspects. According to the present findings, the most

important source of fear in relation to screening intention is a fear of the procedural

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aspects of screening, which is specific to a fear of side effects, pain, and complications

that can occur during or as a result of screening. The results of Study 2, which explored

antecedents to stool and colonoscopy intentions separately, suggest that fear of

procedural aspects is especially salient for colonoscopy intentions and less so for FOBt

intentions, and also predictive of lower colonoscopy screening intentions. This supports

the view that specific types of fear may be more or less relevant in different CRC

screening decisions.

Fear has rarely been, but is increasingly so, explored in health behaviour

research according to its source or target. As such, there is a dearth of empirical

evidence to corroborate the present results however, a number of studies have reported

fear of the CRC screening procedure in its various elements (e.g., fear of pain, fear of

complications) as an obstacle to participation (Busch, 2003; Consedine et al., 2008;

Denberg et al., 2005; Hart et al., 1997; Hynam et al., 1995; Jandorf et al., 2010; Janz et

al., 2007). In one study, 22% of participants reported a fear of the test as a reason to

decline screening (Feeley et al., 2009). The present findings are consistent with this

literature of ad hoc exploration of screening decliners, and supports the investigation of

distinct sources or origins of fear as having varying strength in predicting screening

intention.

11.7.2.2 Fear of cancer. Rawl et al. (2000) reported a range of qualitative

themes as barriers to screening, one of which was a fear of complications in the

colonoscopy screening procedure, however the authors also reported a strong fear of

finding cancer among non-screeners, with statements such as, “I think we’re more

afraid of what the results are going to be and that’s the reason that we hesitate to have

FOBT done”. This is consistent with other qualitative findings (e.g., Chapple et al.,

2008; Feeley et al., 2009), which suggest a pervasive and general fear of having cancer

can lead to screening delay or avoidance.

Interestingly, in the present research, fear of cancer was not a correlate of

intentions or participation (Study 1 and Study 2). With scant empirical evidence for the

role of differential types of fear, these findings are therefore only moderately in line

with the qualitative literature, where fear of cancer and disease has arisen as a

qualitative theme connected with cancer screening avoidance (Berkowitz et al., 2008;

Clavarino et al., 2004; Denberg et al., 2005). In these studies, fear is one of the most

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commonly reported barriers in cancer screening avoidance, with some participants

reporting it as difficult to even talk about cancer for fear of the disease (e.g., Severino et

al., 2009), while others report an immediate dismissal of the idea of screening for CRC

due to a fear of having cancer and experiencing future worry in relation to test results

(Clavarino et al., 2004). Possible reasons for the lack of a relationship with CRC

screening intentions in the present research may be the limited public profile of CRC

(Reeder, 2011). This would suggest that fear of the disease may be limited if there is

only minimal public awareness of its seriousness and prevalence. Alternatively,

qualitative findings supporting this relationship may not have the magnitude to detect

any relationship between cancer fear and CRC screening intentions when assessed

quantitatively.

11.7.2.3 Fear of embarrassment. A fear of experiencing embarrassment during

CRC screening has also been reported in a number of qualitative papers (Denberg et al.,

2005), however the present findings are at odds with this literature. A small number of

studies have reported patient apprehension and fear associated with modesty during the

screening exam, particularly among non-completers of CRC testing (Denberg et al.,

2005), and a fear of experiencing embarrassment when mailing the stool test to the

laboratory (Chapple et al., 2008). Various possibilities exist for the lack of effect of a

fear of embarrassment and a fear of cancer on screening intention. It is possible that

when these types of fear are separated, participants gain clarity in assessing the

comparative and competing types of emotion that hinder their interest and intention to

screen, as opposed to the open format of focus group interviews conducted in the

qualitative literature. Alternatively, fear of cancer or of embarrassment may not be

powerful determinants of CRC screening reluctance. It is also possible that better

measures are required for assessing these variables, and with increasing theoretical

emphasis on the motivational function of emotion in health behaviour, improving the

instruments for assessing these emotions may receive greater research attention.

In summary, the type of fear most strongly related to weaker screening

intentions in the present research was a fear of the procedural aspects of screening. A

general undifferentiated fear of cancer and a fear of experiencing embarrassment during

screening were only related to higher decisional conflict in Study 1, and were unrelated

to any outcome variable in Study 2. While these emotions shared a negative relationship

with screening intention, they may be less important types of emotion in predicting

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CRC screening compliance, and continued delineation of sources of fear may serve to

benefit an understanding of how variants of fear are involved in CRC screening.

11.7.3 Disgust and CRC screening

While aspects of disgust were associated with decisional conflict in Study 1 and

Study 2, there was no association between disgust sub-types of the DS-R and DES with

screening intention or participation across both studies. Only the measure developed

specifically for this study, bowel-specific disgust, was related to screening intentions in

both Study 1 and 2, and significantly predicted intentions and decisional conflict in

Study 2. These results are divergent from qualitative literature on CRC screening, which

suggests disgust is a powerful disincentive to screen (Chapple et al., 2008; Friedemann-

Sánchez et al., 2007).

Although empirical study into disgust and CRC screening behaviour is absent,

there is extensive literature on the positive relationship between disgust and withdrawal

or avoidant behaviour (Deacon & Olatunji, 2007; Fallon, Rozin, & Pliner, 1984). There

is qualitative evidence for the association between disgust and CRC screening (Chapple

et al., 2008; Friedemann-Sánchez et al., 2007), as well as descriptive evidence that

disgust inhibits CRC screening participation (Worthley et al., 2006). The qualitative

findings suggest that disgust is felt particularly in relation to the handling of stool swabs

during home stool testing, but also at the idea of the sample being tested at the

laboratory and sending the samples through the postal system. This may correspond to

the significant findings for odour disgust in relation to decisional conflict in Study 2. As

an extension of these typologies of disgust, revulsion associated with bowel habits and

behaviours involved in stool testing (bowel-disgust) was especially prominent in

relation to weaker screening intentions in both Study 1 and 2, but not to participation

(Study 2), suggesting that explicit activities associated with screening may elicit disgust

during decision making, but trait personality thresholds may fail to influence the

participation phase of screening. A different possibility is that the bowel-specific items

were inadequately sensitive, or did not sufficiently pertain to the domain of disgust they

were posited to measure.

Disgust may alternatively be salient at an automatic level, and might be best

observed by its behavioural effects rather than by self-report. Rozin, Nemeroff, Wane,

and Sherrod (1989) examined this concept by investigating contagion of disgust and the

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law of similarity. One of the tasks required 50 participants to rate their preference for

imitation dog faeces (chocolate fudge) versus disc-shaped chocolate fudge, and rubber

vomit versus a flat rubber sink stopper of about the same size. Participant preferences

for sampling the chocolate fudge or for holding the piece of rubber were significantly

reduced when they resembled a disgusting entity than when they were in an innocuous

form. The authors note similarities between this type of non-rational thinking and

cognitive heuristics and biases, but comment that disgust-related reactions appear to

occur at a “gut level” and may therefore be impervious to interventions and

modification.

The present findings suggest that various types of disgust are not powerful

enough to be directly associated with CRC intentions or participation. Instead they may

be connected with decisional conflict about screening, which itself is independently

associated with weaker screening intention. Only disgust specific to bowel habits was

strongly associated with lower intention and greater conflict in both studies, and

performed well as a predictor of FOBt non-intention (but not participation). The

particular typologies of disgust that arose as significant correlates and predictors in the

present study make sense as elicitors of disgust related to CRC screening expectations,

with contamination disgust being the only predictor from the DS-R factors (compared

with animal-reminder and core disgust), and in Study 2, blood-draw and odours being

the only types from the DES (compared with food, small animals, and injury disgust)

associated with decisional conflict. Despite the weak relationships reported here, there

is some indication that disgust is involved in CRC screening reluctance based on the

qualitative literature, and more effective ways of measuring the more transient state

disgust specific to screening tests (instead of trait disgust mostly assessed in the present

investigation) may be more appropriate.

11.7.4 Medical embarrassment and CRC screening

Medical embarrassment was found to be the most important emotion related to

weaker screening intention in the present research. It was also the only emotion

predictive of screening participation (Study 2). The most significant sub-types of

embarrassment were bodily embarrassment and interpersonal embarrassment, whereas

judgement concern exhibited only weak relationships with the outcome variables.

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These findings are in accord with the growing literature on the role of

embarrassment in cancer screening, and in particular on CRC screening (Consedine et

al., 2007; Madlensky et al., 2003). A number of studies have found that embarrassment

is frequently reported by participants to be an impediment to CRC screening

participation. In Codori et al. (2001) the only significant between-group difference

between screeners and non-screeners was that non-screeners were significantly more

likely to report embarrassment as a reason for avoiding endoscopy. Often, these

findings are not differentiated to indicate what it is about screening or medical

assessments that participants find embarrassing.

The present findings are able to articulate the situations that are considered

embarrassing by patients, and have the most impact on CRC screening intention and

participation. Of the three types of embarrassment explored (judgement concern, bodily

embarrassment, and interpersonal embarrassment), interpersonal embarrassment and

bodily embarrassment were strongly related to screening intention and decisional

conflict in Study 1. Study 2 enabled the two main screening tests to be explored

separately; suggesting interpersonal embarrassment is strongly related to FOBt

intentions, and bodily embarrassment strongly related to colonoscopy intention. Only

interpersonal embarrassment was both related to, and predicted less participation in,

CRC screening. These findings suggest that the interactive component of the medical

appointment, and the necessary dialogue between patient and doctor, is a strong

deterrent to engaging in CRC testing, affecting both screening intention and

participation. This highlights a tendency to want to avoid discussing sensitive and

private health concerns. The results also demonstrate the importance of bodily

embarrassment (embarrassment about modesty and being naked in front of a

doctor/nurse) on a person’s intention to screen by colonoscopy, but not to participate in

screening.

Of note was the comparatively weak relationship between judgement concern

and screening intention across both studies, with no significant relationship detected in

Study 1, and only a weak relationship with colonoscopy intention and decisional

conflict in Study 2. These findings suggest there may be a lesser role for embarrassment

that is related to being perceived as weak, at fault for the illness or symptoms, or a

hypochondriac, in explaining CRC screening intentions.

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While these findings generally support the literature on embarrassment and CRC

screening, it is predominantly a qualitative literature, defining embarrassment broadly

and without the systematic organisation of different types of embarrassment that were

explored in the present thesis. Only recently has a measure been developed which is

designed to capture medical embarrassment and its differential effects on health

behaviour (Consedine et al., 2007). Based on this measure, the present findings confirm

that discriminating between types of embarrassment is useful and may differentially

relate to intentions to screen in either FOBt or colonoscopy. They also indicate that

embarrassment about disclosing sensitive medical concerns or bodily functions in

dialogue with the doctor, and embarrassment related to physical or bodily appearance,

may be especially pertinent to CRC screening reluctance.

11.7.5 The relationships between emotion, self-efficacy, and social support with

screening intention and participation

11.7.5.1 Negative emotion and self-efficacy. In Study 1, self-efficacy was found

to partially mediate the effect of fear on screening intention, and fully mediate the effect

of medical embarrassment on screening intentions. That is, self-efficacy acted to explain

the impact of these negative emotions on intention. Therefore, a perception of efficacy

and ability to undertake and complete screening may remove the effects of fear and

embarrassment on intentions to screen.

One interpretation of the present findings is that those with greater self-efficacy

experience fewer negative emotions about the target behaviour. Events outside of our

control are more feared than those within our control (Klein & Stefanek, 2007), and

individuals with perceptions of increased self-efficacy may therefore experience less

negative emotionality about CRC screening because they feel more empowered to be

able to effectively participate. Klein and Stefanek (2007) frame an example of fear of

dying in a plane crash versus a much higher but less feared risk of dying in a car

accident, and suggested that because people feel they are better drivers (and more in

control of their vehicle) than others, they deem the difference in risk to be irrelevant to

them (but applicable to others). There are also several studies that support the

mediational effect of self-efficacy against depression (Robinson-Smith, Johnston, &

Allen, 2000), general stressors (Bandura, 1986), and as a mediator of arousal in a stress-

provoking medical context (Gattuso et al., 1992). Given that self-efficacy is generally

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thought to help an individual surmount barriers or impediments affecting the enactment

of a particular behaviour, the present results may suggest that feeling efficacious about

screening may be especially useful in overcoming the unconstructive influence of

emotions, such as medical embarrassment, disgust, and screening-related fear. The

direction of causality between higher self-efficacy and lower emotion requires

additional research, however the present findings support the importance of self-

efficacy in explaining the impact of negative emotion toward screening.

11.7.5.2 Negative emotion and social support. Social support was found in

Study 1 to partially mediate negative emotions and screening intention, with the positive

effect of social support reducing the impact of embarrassment and fear on CRC

screening intention. The perception of emotional as well as instrumental support (e.g.,

having someone to accompany to a colonoscopy appointment) may be especially

germane to CRC screening, in which the tests, one involving individual effort (the stool

test) and the other being an invasive procedure (colonoscopy), may be particularly

likely to elicit negative emotions. There is little by way of empirical evidence for the

relationship between emotion and social support on cancer screening, however social

support is thought to be beneficial by suppressing or inhibiting stress (Cohen, 2004),

part of which may consist of negative emotion. By its provision of emotional and

physical support (e.g., being accompanied to the colonoscopy appointment), social

support may account for effects of negative emotion by offering an outlet for emotional

expression within the social network (Cohen, 2004).

There is evidence to suggest that emotional self-disclosure benefits health

(Magai et al., 2009), and one mechanism for this effect may be through a perceived

social network of support. One study has linked positive emotionality with higher social

support amongst a group of older participants (aged 72 to 99), and suggested that

perceptions of support may prompt a range of positive emotions (Chipperfield, Perry, &

Weiner, 2003). The present findings, exploring discreet negative emotions, suggest that

there may be a protective effect of social support by quelling the effect of fear and

embarrassment on screening intention.

11.7.6 Emotion and decisional conflict

Associations with decisional conflict were similar across both studies in the

present investigation, being strongly associated with medical embarrassment factors,

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bowel disgust, and fear of procedural aspects, and weaker positive relationships with

animal-reminder, blood-draw, and odour disgust; and with fear of cancer and of

embarrassment. Study 2 suggested three types of emotion are also predictive of higher

decisional conflict, including fear of procedural aspects, interpersonal embarrassment,

and bowel disgust.

Negative emotions predicted decisional conflict in one study investigating

shared decision-making between breast cancer patients and their doctor, but only when

the doctor evaded or hindered discussion of emotional issues raised by the patient (by

interrupting or changing the subject) (Smith et al., 2011). Therefore, there may be

contexts in cancer-related behavioural decisions where emotions do predict decisional

conflict, for example, when those emotions are blocked or emotional expression and

discussion is hindered. Another study found that negative emotion (anxiety, depressive

symptoms) predicted decisional conflict in women who had received an uninformative

genetic breast cancer test result, and were in a position of deciding on risk management

options, suggesting that in conditions of high uncertainty and risk, emotion may be an

especially prominent predictor of decisional conflict.

There is some evidence to support a cyclical relationship between decisional

conflict and negative emotion, where ambivalence and decision conflict are considered

to be psychologically unpleasant and thus lead to further negative feelings (van

Harreveld et al., 2009). Stalmeier et al. (2005) found that decisional conflict was

associated with negative emotional reactions after reading written material about

medical treatment choices. Both the current studies and the literature suggest an unclear

but probable relationship between negative emotions and decisional conflict. The effect

of negative emotion on decisional conflict may be mediated or moderated by a range of

factors, including reduced self-efficacy, access to empathy and emotional expression, or

higher-risk or uncertain health contexts. Alternatively, a few specific negative emotions

may influence decisional conflict, such as fear of the procedural risks and side effects,

embarrassment arising from discussions with the doctor about bowel health and

screening, and disgust specific to bowel habits and tests, as implied by Study 2.

11.7.7 Summary: The effect of negative emotion on CRC screening

While fear and anxiety are more often comprehensively examined for their

impact on cancer screening (Consedine et al., 2004), the present research suggests that

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medical embarrassment may be a more significant emotion affecting screening

intentions and participation. These findings also help to establish both what it is that

people are afraid of in relation to screening for CRC, and which aspects of the medical

experience is most embarrassing and subsequently detrimental to screening uptake. As

such, fear of procedural aspects, embarrassment related to interpersonal communication,

and bodily embarrassment, appear to be barriers to screening intention, and predictive

of intentions not to screen. Medical embarrassment in particular seems to differentiate

intenders from non-intenders of FOBt, while fear of procedural aspects appears to

predict non-intenders of colonoscopy. Interpersonal embarrassment also emerged as a

predictor of less screening participation. Also notable was the lack of effect of disgust

on CRC screening, which is one of the most frequently reported emotional barriers to

CRC screening in qualitative research. While some of these emotions were unrelated to

screening, none of them facilitated greater screening. Despite a shortage of empirical

research, it is evident that there is a directive for further investigation of discrete and

measurable emotions, such as medical embarrassment and fear, and their differential

influence on cancer screening decisions.

11.8 Limitations

A number of methodological considerations related to one or both studies should be

considered in the interpretation and generalizability of the findings. These shortcomings

relate largely to sampling and measurement, and are discussed in the following sections.

11.8.1 Sampling limitations

Four concerns relate to sampling limitations. While representative samples were

sought, both studies comprised samples that were highly educated, with higher SES

than general population norms, as indicated by the Index of Relative Socio-economic

Disadvantage and health insurance status. These variables are known to indirectly affect

psychosocial variables, such as self-efficacy, to take part in CRC screening (von

Wagner et al., 2009) and therefore limit the generalizability of the present findings. As

self-efficacy may be one of the most important variables linking health literacy to health

outcome (von Wagner et al., 2009), and was likely to be higher amongst these high SES

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samples, it may partly account for the lack of significance of SES in predicting outcome

variables.

Second, it is not unusual for a survey of this nature to attract participants with a

personal or family history of CRC or a personal history of polyps. Other surveys where

non-respondents and respondents have been identifiable for comparisons have shown

this bias (Vernon et al., 2001). Similarly, individuals who have already screened for

CRC are also more likely to choose to take part in related questionnaires (Kelly &

Shank, 1992; Lindholm, Berglund, Haglind, & Kewenter, 1995). Given screening

intentions were considerably higher than population norms in both studies and

participation in CRC screening in Study 2 was higher than national norms for CRC

screening (with 62% of this sample having already screened), it is plausible that

participants were attracted to survey participation due to a pre-existing interest in the

topic.

Thirdly, concerns arose relating to the heterogeneity of the samples across both

studies. The sample in Study 1 was notably youthful with a mean age of 27; far below

age 50 at which CRC screening is recommended in Australia. One of the main

complications with this sample is that risk perception is argued to be unrelated to

screening in people who never or rarely think about developing CRC (Codori et al.,

1999). As the primary goal of this study was to explore the utility of a range of new

measures, and to refine the survey for use in a community sample, a sampling bias was

anticipated; however there should be caution in generalising these findings to older

populations, particularly as validity cannot be fully established to the relevant

population. Nevertheless, scale reliabilities in both studies did generalise to published

reliability reports. Conversely, one benefit is that few studies explore CRC screening in

younger populations, and the results offer insight into a group of people who are yet to

seriously consider screening, suggesting that similar variables identified in an older

population in Study 2 (self-efficacy, social support, medical embarrassment, and

screening biases) may be important in predicting distal future health intentions in a pre-

screening group.

Lastly, far fewer men than women participated in the studies (approximately one

third in both studies). The discrepancy between the sample sizes of men and women

prevented the possibility for reliable statistical comparison on the basis of gender.

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11.8.2 Study design and measurement limitations

Four drawbacks related to study design and measurement were identified. First,

it is likely there are distinct psychosocial antecedents to intentions to screen using

specific CRC screening tests, particularly as people perceive unique benefits and

barriers to each test (James et al., 2002). In Study 1, screening intention was not

examined separately in relation to non-invasive (FOBt) or invasive (colonoscopy)

screening, and as demonstrated in Study 2 these tests are somewhat likely to be

predicted by different variables. Thus, while the present findings substantiate the

importance of screening bias and emotion in the formation of screening intentions, and

also cognitive biases throughout the intention and action phase of CRC screening in

Study 2, the first study failed to distinguish these variables across specific tests. Given

that these tests require either passive or active participation by the patient (endoscopy

versus stool kit, respectively), further examination of these predictors across test-type is

warranted.

Second, there were few measurement tools available for a number of the

variables under investigation. With the dearth of theoretical health behaviour models

concerning emotions and their relationship with screening behaviour, a necessary

antecedent to the development of these models is a requirement for standard and

recognised conceptualisation and measurement (Consedine et al., 2004). While the best

measures were obtained where possible, no studies could be located which

quantitatively explored the emotion measures pertaining to CRC screening specifically

(however, since the inception of this research, the Medical Embarrassment

Questionnaire has been investigated in relation to CRC; see Consedine et al., 2011). For

example, there is no measure of disgust that is specific to health or medical

environments. The DS-R is a measure of propensity to experience disgust (trait levels)

and not the degree to which people find specific contexts or settings disgusting or

unpleasant (state levels). A measure of both propensity and sensitivity may be useful to

investigate general tendencies to feel disgust in various health decisions, however as

exists for medical embarrassment, an instrument to measure disgust in the medical

context would be beneficial.

Third, the cross-sectional design introduces a disadvantage in the interpretation

of causal relationships. This is particularly the case in the exploration of emotion and

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cognition where theoretical understanding of their relationship remains ambiguous.

While experimental longitudinal methods are a more accurate means of assessing

causality, structural equation modelling is a technique that allows for a range of

complex analyses and causal relationships to be conceptually modelled and tested,

which can then be interpreted to reflect real-world relationships (Cunningham, 2008).

However, for the purpose of predicting non-intention and non-participation,

discriminant analysis was identified as the most appropriate method in the present

investigation. Relating to this design limitation was the measurement of past screening

behaviour and interpreting current cognition and emotion as predictors of that

behaviour. It would be ideal in future research to assess these variables after a screening

invitation, in order to predict subsequent screening action.

Fourth, the length of the survey in Study 1 appeared to be the cause of a high

amount of attrition, probably as a result of fatigue, however this was partly unavoidable

given the addition of a wide range of measures in the survey. Reversing the order of

presentation of the groups of variables in Study 2, and patterns of missing data,

indicated this attrition was not due to the emotion measures. Survey length, particularly

in Study 1, was therefore a design limitation, but a somewhat necessary one.

11.8.2.1 Theoretical limitations. Intention is thought to be a useful predictor of

future actions, however it is important to recognise its flaws. Several studies indicate

that social cognitive variables are strong predictors of intention but not of action (Power

et al., 2008), therefore the antecedents to intention and action may be very distinct.

Study 1 was not designed to address this behaviour-intention gap. While Study 2 helped

to delineate the predictors of intention and past screening, longitudinal within-groups

assessment of the predictors of intention and subsequent behavioural manifestation of

those intentions would be an ideal research venture. Intentions that have specific plans

and strategies for carrying out the intended action (implementation intentions) may also

be a more reliable indicator of subsequent behaviour (Sheeran & Orbell, 2000).

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11.9 Implications of the Findings

11.9.1 Implications for research: measurement and theory

The present investigation is important for two chief measurement and theoretical

reasons. Firstly for its examination of the relationships and predictive ability of

emergent, non-rational variables such as screening bias and negative emotion, and

secondly, it is the first to explore whether these variables (screening biases and discrete

emotions) can predict reluctance to screen for CRC; specifically their ability to predict

screeners and intenders, from non-screeners and non-intenders. The present findings

provide cursory support for the roles that these variables play in inhibiting screening

intention and participation, and encourage further exploration of their impact on

intention and subsequent screening participation or non-participation. As such, the

present evidence provides a platform for incorporating experiential system processes

such as emotion and cognitive biases into the examination of important preventive

health activities such as cancer screening. These findings indicate that these processes,

both emotive and heuristic, are embedded in health behaviour decisions and can

influence intention to screen, as well as screening participation.

11.9.2 Implications for intervention

The present findings may be employed to benefit the selection of intervention

targets, and how they might best be operationalised and manipulated to encourage CRC

screening. The main areas of investigation (emotive, cognitive, and social) indicated

that particular variables may be superior for their impact on screening intention,

compared with variables that appear to impede screening intentions and behaviour.

Decisional conflict was associated with lower screening intention in both

studies, suggesting that interventions aiming to reduce this conflict may concurrently

have a positive effect on intentions. While there is extant research that decision aids can

reduce decisional conflict associated with medical and health related decisions

(Hollinghurst et al., 2010), this adds to the literature in support of a relationship

between CRC screening intention and decisional conflict, and suggests decision aids

may reduce conflict and indirectly increase screening participation.

Results from the present investigation have implications for the way that

interventions for promoting CRC screening may be designed and targeted toward a

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specific audience. Interventions may seek to target misconceptions associated with

screening that are derived from judgement and probability fallacies. Misconceptions

include biases about family history, diet and lifestyle factors, being asymptomatic,

‘feeling healthy’, and CRC being predominantly a male disease. For example, females

with this belief tend to be lower users of screening procedures than females who do not

relate CRC to gender (Donovan & Syngal, 1998; McCaffery et al., 2001; Weitzman et

al., 2001).

Taboo associated with colorectal health needs to be addressed, particularly with

regard to mixed-gender physician-patient relationships, where women report being

deterred from having a bowel procedure performed by a male health professional. A

survey in the US found that half of all women interviewed preferred to see a same-sex

colonoscopist, even at personal expense (Menees et al., 2005). Women were also

prepared to delay their procedure for the opportunity to have a female endoscopist,

despite the availability of light anaesthetic or sedation. As indicated by the current

findings, there is therefore likely to be an emotive facet to screening reluctance, which

suggests that bodily embarrassment is a significant predictor of lowered intentions to

undergo colonoscopy for both men and women. Having a colonoscopist of the opposite

sex may further exacerbate this embarrassment, however the present findings cannot

substantiate this hypothesis. With fewer than 10% of Australian gastroenterologists

being female (Rosenfeld & Duggan, 2008), this may contribute substantial levels of

embarrassment to the screening experience for women. Therefore, medical

embarrassment may be an important yet malleable factor affecting screening uptake and

should receive further empirical investigation in order to be a successful intervention

target. For example, the finding that some variables provide pathways for reducing the

effect of negative emotions (i.e., self-efficacy) may offer a clear target for intervention

design.

It would be fruitful to explore whether this is also the case for screening biases.

As cognitive biases are notoriously enduring and robust (Kahneman, 2003), targeting a

cognition that helps to moderate their impact, rather than attempting to address the

biases themselves, may be a more practical means of handling this cognitive barrier to

screening. However, different groups of people (e.g., asymptomatic, men or women,

people without a family history) may also need different, tailored intervention

approaches.

279

Along these lines, Weitzman et al. (2001, p. 511) state several key points that

should be addressed in publicly disseminated programs, and many of these address

biases and emotional deterrents to screening: “(1) CRC is one of the ‘top three’ most

common cancers in both men and women; (2) CRC is a leading cause of cancer related

death; (3) CRC screening is important even if no symptoms are perceived or present; (4)

Family history is important, but most CRC affects people without a family history; (5)

Screening should be repeated at intervals recommended by a health professional; (6)

Flexible sigmoidoscopy and CS is conducted with the patient covered and lying down;

(7) FOBT can be carried out privately and with minimal mess”.

A goal for public health promotion may be to promote awareness of CRC

screening to achieve the same level of community consciousness attained for breast and

cervical cancer through health campaigns. Similar methods of screening promotion

could be adopted to improve the perceived worth of CRC screening, which could also

be strategic in jettisoning some of the anxiety, embarrassment, and fears that hamper

screening uptake.

11.9.3 Public health and policy implications

Screening reluctance may impair the success of any future national screening

program. Without public acceptance, a national program may fail to impact national

morbidity and mortality at a level that is cost-effective. The findings of the current

investigation imply a number of considerations for public health education and policy

initiatives, particularly if the Australian national bowel cancer screening program

continues as a result of the current pilot program.

Consistent with the literature, the present findings confirm that physician

recommendation is a significant factor in the action phase of CRC screening

participation. Indeed, it was the strongest predictor of participation in Study 2, over and

above psychosocial and emotional variables. Should a national screening program be

endorsed as a result of the piloting program currently in place, systematic methods for

GPs to recommend screening to patients reaching screening age could incorporate a

component that focuses on addressing screening biases. As health providers are also

prone to biases in their recommendations (Sladden & Ware, 1999), provider biases

could be explored in future research for their impact on the way messages are

communicated to patients about the necessity and value of CRC screening.

280

As evidenced by the present study, part of the route in reaching the behavioural

enactment of a screening intention may require overcoming a number of cognitive and

emotive barriers to participation. Although heuristics offer efficient ‘rules-of-thumb’

and are generally adaptive in decision-making, the rapidity of heuristic-based choices

can lead to errors and biases. Given the enduring nature of heuristic biases (Tversky &

Kahneman, 1983), their influence on health decisions may be extensive. The potential to

reduce this bias exists in the development of health communication messages for the

public.

The results attained in this thesis indicate that intention may be attenuated if

there are high levels of medical embarrassment, fear of the screening procedure, and

biases about the value of screening, with the latter being a significant predictor of

screening inaction, along with embarrassment about the interpersonal component of

help-seeking and discussion. While these factors may be amenable to public health

interventions, it may be advisable to incorporate training within the medical community

to address, or have the provisions to address, patient barriers of a psychological and

emotional nature. The ability to deliver this at the primary care phase of screening

advice may be especially advantageous in facilitating screening compliance on

schedule, and reducing the delay that some practitioners report in their patients, which is

often attributable to psychosocial obstacles (de Nooijer et al., 2001; Menees et al., 2005;

Smith et al., 2005).

11.10 Further Research

A number of further research opportunities can be derived from the findings,

implications, and limitations of the present research. Firstly, the availability of

standardised measures for predicting health-related intentions and behaviour is limited.

The paucity of measures is explicable given that definitions for constructs such as fear

in relation to screening remain incomplete in the literature, however there is great

capacity for further scale development and improvement for a number of constructs

including cancer worry, test-efficacy, and risk perception. There has been recent

progress with the development of emotion scales relevant to health behaviours, such as

the Medical Embarrassment Questionnaire (Consedine et al., 2007) and the use of trait

emotion scales in health research, while the measurement of fear in relation to medical

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tests and other health-related behaviour remains vastly under-explored. This

investigation suggests that the development of new and existing scales may enhance

research efforts in the prediction of cancer screening.

Further research may explore these predictors in structural equation modelling,

to help to inform practitioners and researchers about the mechanisms by which these

variables influence screening intention, further assess mediational effects, and inform

the evaluation of the intervention’s success. For example, improving self-efficacy and

perceptions of social support may reduce the impact of a range of negative emotions

and screening bias, thereby facilitating CRC screening. Perhaps increased levels of self-

efficacy can explain the effects of experiencing emotions that would ordinarily inhibit

screening participation. Alternatively, negative emotion may be resistant to changes in

self-efficacy and these relationships needs further investigation in subsequent research.

While women and men reported similar emotions (although women reported

slightly higher levels on some emotions), it would be worthwhile to determine whether

women report more affective fears in relation to screening and men report more

physical fears. For instance, do men have a greater fear of procedural aspects of

screening, and do women experience greater medical embarrassment in CRC screening?

Friedmann-Sanchez et al. (2007) suggest that men and women do have different

emotions in relation to CRC screening, with women more fearful of colonic preparation

(although it is unclear whether the source of this fear is related to expectations of

embarrassment or a fear of procedural aspects). Women also reported embarrassment

about being vulnerable and ‘exposed’ during the colonoscopic procedure, whereas men

were more concerned about the invasiveness and potential pain of the test. Emotional

disparities between men and women may be a useful avenue for investigating the

impact of discrete, source-specific emotions on screening intention and participation in

greater depth.

Fourth, it seems inevitable that part of the reluctance to screen for CRC would

be driven by heuristic biases. For instance, anecdotes, recent experiences, and news

broadcasts and stories may become reference points for judging the efficacy of

screening tests, or for estimates about personal risk to CRC (Peters, Slovic et al., 2006).

In uncertain or difficult decision contexts, these decision cues can introduce biases and

govern choice selection. There is scope to further explore screening biases in the

282

context of cancer-related judgements and decision-making. An important further step is

to assess the discriminant nature of screening biases from low CRC knowledge. The

present studies suggest that these two variables, whilst related to each other, are

independent and uniquely relate to screening intention, but this finding needs

replication.

There is also opportunity in examining cognitive and emotive variables together,

as heuristics and negative emotions may interact in their influence on screening

decisions. McCaffery et al. (2001) conducted a qualitative study investigating the

decision making process in those who declined an offer of flexible sigmoidoscopy

screening. Using an open-ended interview style, many participants stated that decisions

were typically made instantly upon receiving the letter, suggesting little cognitive

elaboration occurred. This may indicate that information about CRC and the screening

process was not processed systematically. Others participants report they “put the offer

aside”, and avoided making a decision. While these appear to be primarily cognitive-

based decisions, they may also be emotive responses to the screening invitation. For

example, screening avoidance is linked with fear about possible physical and

psychological harm (Smith et al., 2005). Furthermore, while there is evidence that

negative emotion such as fear and embarrassment influence CRC screening, future

research could also explore the motivating effects of positive emotions (such as interest)

on CRC screening.

The origins of risk perception for a specific disease may be numerous. In

addition to understanding factors that might directly affect screening intentions,

understanding what influences cancer risk perception is also a useful research target.

Such influences could include cognitive biases about participating in cancer screening,

and while these may shape intentions directly, they may also operate through decreasing

or increasing perceived risk. In other words, a range of cognitive processes may

influence risk perception, including those that are formed non-consciously and which

inherently affect judgement and probability estimates. Indeed personal experiences with

cancer have been reported to be the most salient factors in risk estimates, over and

above objective risk (Robb et al., 2007), suggestive of a cognitive influence based on

experiential processing. Of related research interest would be the development of an

objective risk index (based on known risk factors such as hereditary risk, diet and

exercise behaviour, age, and so on) for comparison with perceived risk and screening

283

bias. While the present research has focused on the detrimental influence of screening

bias, Peters, McCaul et al. (2006) argue that heuristics may also be harnessed to make

more informed risk assessments and health decisions. Examples include involving

celebrities in public CRC screening campaigns to increase the salience of the disease.

An alternative theoretical framework may involve the influence of mortality

salience on consequential medical decisions. To date there are no empirical studies

investigating screening intentions and mortality salience, although the present

investigation touched on this topic with the measurement of cancer fear. According to

the tenets of mortality salience, and studies exploring the accessibility of death

thoughts, death-related issues (death reminders) can generate defensiveness. Studies

have demonstrated that when individuals consciously focus on death issues, defence

strategies are very likely to form, including distraction, denial of vulnerability, or

projecting the problem into the future (Greenberg, Pyszczynski, Solomon, Simon, &

Breus, 1994). The emergence of these defences can occur directly following

presentation of threat stimuli and messages (Greenberg et al., 1994) however the

inclusion of a distraction task could potentially reduce the effect of salient threat cues

(Greenberg et al., 1994). Mortality salience may therefore be another useful framework

to investigate the primary factors that inhibit screening participation.

11.11 Conclusion

This thesis explored the relative importance of systematic cognition, heuristic

bias, social factors, and discrete emotions in predicting decisions about intentions and

participation in colorectal cancer screening. A unifying aim of two related studies was

to examine the comparative role of screening bias and discrete emotion (embarrassment,

fear, and disgust) in CRC screening decisions against the more commonly measured

health behaviour processes and influences, with the goal of adding to an emerging

research focus and body of literature on the importance of both heuristics and discrete

emotions in cancer screening and related health decisions. The findings support the

suitability of incorporating biases and emotions that disable or inhibit decisions to

screen for CRC, into the approaches of major health behaviour models.

There is compelling evidence that emotions and feeling states are crucial in

decision-making (Bechara, 2004; Damasio, 1996; Isen, 1999), and that they impact

284

decisions alongside (or integrated with) social-cognitive processes. Affective

associations and cognitive beliefs inform attitudes towards, or evaluation of, an event

(Giner-Sorolla, 2004) but there are surprisingly few theoretical models providing an

integrated perspective of the collective influence of discrete emotion and cognition in

decision-making (Ashby, Isen, & Turken, 1999), including in health behaviour research.

The limitations of the present project do not detract from the broader value of

the current findings, which denote an important position in the health behaviour

literature for negative emotion (based on their targets and sources), and biases related to

judgements about the importance and necessity of screening (based on the

representativeness heuristic). Indeed, the present findings are not at odds with the

mainstream health behaviour models that are largely employed to examine cancer

screening intention and behaviour, but instead incorporate a dual-process theoretical

approach to complement these existing health behaviour models and their key variables.

With a biological link between emotion and cognition established in the decision-

making literature (Wagar & Thagard, 2004), the opportunity to develop a conceptual

and empirical link between emotion and heuristics, and their effects on health decision-

making and behaviour, is immense.

Portraying the combination of variables that best predict CRC screening

reluctance, Study 2 results show that particular negative emotions (interpersonal

embarrassment, bodily embarrassment, fear of procedural aspects) predict individuals

with no screening intentions, low decisional conflict, and no engagement in prior CRC

screening practices. Study 1 suggests that these influences may be partly reduced or

eliminated by confidence to participate in screening (self-efficacy) and a perception of

social support. The prediction that screening bias would also contribute to screening

reluctance and avoidance was endorsed by the results of Study 2, which demonstrated

that biases also have the capacity to discriminate between individuals with high and low

screening intentions and decisional conflict. Probably not surprisingly, most emotions

were not predictive of screening participation, materialising only in the intention phase

of screening as determinants of non-intention. Instead, fixed factors, such as age,

gastrointestinal health, and screening advice play a more prominent role in

differentiating screeners from non-screeners, with screening bias emerging as the most

important cognitive factor (followed by risk perception and worry).

285

An additional finding of unique importance was the varying role of emotion in

predicting low intentions separately for the two main screening tests, stool-testing and

colonoscopy. The types of emotions predicting low FOBt intention included

embarrassment about conversations with the doctor (interpersonal embarrassment),

bowel disgust, and bodily embarrassment, while fear of procedural aspects predicted

low intention to screen by colonoscopy. Decisional conflict about screening was

predicted by each of these areas (except bodily embarrassment). The present studies

therefore augment areas of the current literature, while adopting and supporting a lateral

but complementary theoretical framework encompassing the dual-processes of the

cognitive system. Epstein has argued that the “outcome of experiential processing is

usually more compelling than of the rational system” (cited in McCaul & Mullins,

2003). Biases of the experiential system are likely to be instrumental in cancer

screening reluctance.

The variables defined and conceptualised in the current project have often been

labelled ‘barriers’ to screening. Terms such as ‘screening barriers’ or ‘screening

motivators’ offer little utility in understanding more precise influences on screening

intention. Commonly identified barriers in the literature comprise embarrassment, fear,

cancer knowledge, cancer worry, and cost, all assessed across a diverse range of

definitions and measures, and within diverse health behaviour models. A number of

researchers have sought an integration of cognitive and emotional processes in specific

areas of health research (see Peters, Lipkus et al., 2006), however it remains an area that

offers much investigative scope. Along with exploring the influence of cognitive bias,

unpacking the effects of general emotion ‘barriers’ into more meaningful variables has

been a constructive and useful aim of the present research. Broken down into

observable variables that arise from specific physical or psychological sources, for

example ‘bodily embarrassment, ‘fear of screening procedural aspects’, ‘bowel disgust’,

these variables provide clarity about the various but specific kinds of emotional

obstacles to CRC screening.

Further research should continue to capitalise on the value that a discrete

emotions approach provides to the understanding of cancer screening behaviour and its

antecedents, while a dual-cognitive processing approach also affords an investigation of

mental shortcuts and heuristic biases. However, there is a need for further conceptual

and measurement developments in order to better assess these variables. Hopefully the

286

current project provides a useful platform for furthering this approach in the explanation

of colorectal cancer screening reluctance.

287

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338

APPENDIX A

SWINBURNE UNIVERSITY HUMAN RESEARCH ETHICS COMMITTEE

(SUHREC) ETHICAL CLEARANCE FOR STUDY 1 AND STUDY 2

339

Appendix A: Study 1 Ethical Clearance

SUHREC Project 0708/190 Testing the structure of a cognitive, emotive, and social model

of colorectal cancer screening decision making

Dr E Hardie FLSS Ms Victoria Hamilton

Approved Duration: 28/05/2008 To 31/12/2008

I refer to the ethical review of the above project revised protocol undertaken by Swinburne's

Human Research Ethics Committee (SUHREC). Your responses to the review as emailed on 23

May 2008, including an attached updated consent information statement, were put to a delegate

of SUHREC for consideration/endorsement.

I am pleased to advise that the project (as submitted to date) has approval to proceed in line with

standard on-going ethics clearance conditions here outlined.

- All human research activity undertaken under Swinburne auspices must conform to Swinburne

and external regulatory standards, including the National Statement on Ethical Conduct in

Human Research and with respect to secure data use, retention and disposal.

- The named Swinburne Chief Investigator/Supervisor remains responsible for any personnel

appointed to or associated with the project being made aware of ethics clearance conditions,

including research and consent procedures or instruments approved. Any change in chief

investigator/supervisor requires timely notification and SUHREC endorsement.

- The above project has been approved as submitted for ethical review by or on behalf of

SUHREC. Amendments to approved procedures or instruments ordinarily require prior ethical

appraisal/ clearance. SUHREC must be notified immediately or as soon as possible thereafter of

(a) any serious or unexpected adverse effects on participants and any redress measures; (b)

proposed changes in protocols; and (c) unforeseen events which might affect continued ethical

acceptability of the project.

- At a minimum, an annual report on the progress of the project is required as well as at the

conclusion (or abandonment) of the project.

- A duly authorised external or internal audit of the project may be undertaken at any time.

Please contact me if you have any queries about on-going ethics clearance. The SUHREC

project number should be quoted in communication.

Best wishes for the project.

Yours sincerely

Keith Wilkins

340

Appendix A: Study 2 Ethical Clearance

SUHREC Project 2009/041 An Australian Community Study about Bowel Cancer

Screening Decisions: Cognitive and Emotive Factors

Prof Susan Moore, FLSS; Ms Victoria Hamilton et al Approved Duration: 06/04/2009 To 30/06/2010 I write to confirm standard ethics clearance issued for the human research activity conducted as per conditions here outlined. - All human research activity undertaken under Swinburne auspices must conform to Swinburne and external regulatory standards, including the National Statement on Ethical Conduct in Human Research and with respect to secure data use, retention and disposal. - The named Swinburne Chief Investigator/Supervisor remains responsible for any personnel appointed to or associated with the project being made aware of ethics clearance conditions, including research and consent procedures or instruments approved. Any change in chief investigator/supervisor requires timely notification and SUHREC endorsement. - The above project has been approved as submitted for ethical review by or on behalf of SUHREC. Amendments to approved procedures or instruments ordinarily require prior ethical appraisal/ clearance. SUHREC must be notified immediately or as soon as possible thereafter of (a) any serious or unexpected adverse effects on participants and any redress measures; (b) proposed changes in protocols; and (c) unforeseen events which might affect continued ethical acceptability of the project. - At a minimum, an annual report on the progress of the project is required as well as at the conclusion (or abandonment) of the project. - A duly authorised external or internal audit of the project may be undertaken at any time. Please contact me if you have any queries about the ethics clearance issued, citing the SUHREC project number. Copies of email clearances should be retained as part of project record-keeping. As before, best wishes for project. Yours sincerely Keith Wilkins Secretary, SUHREC Research Ethics Officer Swinburne Research (H68) Swinburne University of Technology P O Box 218 HAWTHORN VIC 3122 Tel: 9214 5218

341

APPENDIX B

LIST OF VARIABLES AND THEIR RELIABILITY COEFFICIENTS FROM

STUDY 1 AND STUDY 2 SURVEY QUESTIONNAIRES

342Table B.1 List of Cognitive and Emotion Variables and their Reliability Coefficients in Study 1 and Study 2

Variable Study 1 (α)a Study 2 (α)

Cognitive variables

Risk perception Cameron and Diefenbach (2001) (2 items); Kremers et al. (2000) (1 item)

.78 Based on Study 1 (4 items) .97

Self-efficacy Endoscopy Confidence Questionnaire (ECQ; Gattuso, Litt, & Fitzgerald, 1992)

.84 Identical to Study 1 .80

Test-efficacy Myers et al. (1996) (4 items); Tiro, Vernon et al. (2005) (1 item)

.76 Based on Study 1 (4 items) and 2 additional items (Myers et al., 1996).

.82

Cancer worry Breast Cancer Worry Scale (BCWS, Lerman et al., 1991) (4 items); McCaul, Schroeder, and Reid (1996) (2 items)

.85 Identical to Study 1 (refined version) .91

Knowledge PROCASE knowledge index; Radosevich et al., 2004 .53 Identical to Study 1 .71

Screening bias Busch, 2003 (1 item); Harewood et al. 2002 (2 items); Hynam et al., 1995 (2 items); Perez-Stable et al., 1992 (2 items); Schnoll et al., 2003 (1 item);

.87 Identical, refined scale from Study 1 (6 items)

.92

Fatalism Powe Fatalism Inventory (PFI); Powe, 1995b NA NA

Emotion variables

Disgust Disgust Scale – Revised (DS-R), Haidt, McCauley, & Rozin, 1994, modified by Olatunji, Williams et al. (2007)

.85 Disgust Emotion Scale (DES) .92

Medical

embarrassment

MEQ, Consedine, Krivoshekova, & Harris, 2007 (31 items)

.95 MEQ (17 items, refined from original scale, Consedine, Krivoshekova, & Harris, 2007)

.95

Fear of

screening

Harewood, Wiersema, & Melton, 2002 (9 items); Hynam et al., 1995 (1 item); Smith, Pope, & Botha, 2005 (10 items)

.91 Refined from Study 1 scale (13 items) .88

aCronbach’s alpha generated after factor analysis.

343

Table B.2

List of Social and Outcome Variables and their Reliability Coefficients in Study 1 and Study 2

Variable Study 1 (α)a Study 2 (α)

Social variables

Social support Kremers et al. (2000) .60 Identical to Study 1 .67

Social norms Tiro, Vernon et al., 2005 .79 NA NA

Outcome variables

Screening

intention

Ajzen (2004); Emmons et al. (2008); McCaffery, Wardle, and Waller (2003).

.70 Based on Study 1 scale (5 items) .79

Decisional

conflict

Decisional Conflict Scale (DCS; O’Connor, 1995) .93 DCS (10 items, refined from original, DCS; O’Connor, 1995

.96

aCronbach’s alpha generated after factor analysis.

344

APPENDIX C

STUDY 1 ANNOTATED SURVEY QUESTIONNAIRE

Bowel cancer is a relatively common cancer in Australia. The Cancer Council Australia recommends national screening in adults aged 50 and above,

however this is partly dependent on a range of individual risk factors, and some adults are advised to begin screening at age 40. Although home screening tests are being trialed in Australia, there is currently no national screening programme for bowel cancer. The purposimprove our understanding of Australians’ thoughts and feelings about bowel cancer screening.

Participation involves filling in a survey about your beliefs and feelings about screening for bowel cancer. The questionnaire takes approximately 30 minutes to complete, and is entirely ANONYMOUS. You will not be asked to provide your name anywhere on thicommencing the survey, participants should be aware that the nature of the survey means that some people may find certain questions confronting or discomforting. These may include questions about your emotions in medical settings, your bodisgusting scenarios. These are standard scales that are used in medical studies and your anonymous answers are completely confidential.

Your completion of the survey implies that you have consented to take part, howeparticipation is VOLUNTARY

time.

This research project is being conducted as part of a Doctor of Philosophy degree. In the event that conference presentations or publications ariidentifiable. Your responses will be completely will include only aggregate responses.

If you have any questions about this project, please contact: Victoria Hamilton, Tel: (03) 9214 5553, or email: [email protected], or: Prof Susan Moore, Tel (03) 9214 5694, or email: [email protected]. If participation in this survey causes any distress, you can discuss your concerns with a Swinburne counsellor: i) Swinburne Counselling Services, Tel: 9214 8025 (Hawthorn Campus) and 9215 7101 (Lilydale campus) ii) Swinburne Health Services, Tel: campus) iii) Swinburne Psychology Clinic (a low Should any health concerns arise during or after your participation, we ask that you contact your regular medical professional (e.g.This project has been approved by or on behalf of Swinburne’s Human Committee (SUHREC) in line with the National Statement on Ethical Conduct in Human Research. If you have any concerns or complaints about the conduct of this project, you can contact:

Swinburne University of Technology, PO Box 218, HAWTHORN VIC 3122Tel (03) 9214 5218 or +61 3 9214 5218 or [email protected]

THANK YOU FOR CONTRIBUTING YOUR TIME TO THIS RESEARCH PROJECT

APPENDIX C: Study 1 Annotated Survey

Questionnaire SWINBURNE UNIVERSITY OF TECHNOLOGY

Faculty of Life and Social Sciences Psychological Sciences and Statistics

SURVEY CONSENT INFORMATION STATEMENT

PROJECT TITLE: Attitudes toward Screening for Bowel

INVESTIGATORS: Victoria Hamilton (PhD candidate)

Prof Susan Moore (Supervisor)


partly dependent on a range of individual risk factors, and some adults are advised to begin screening at age 40. Although home screening tests are being trialed in Australia, there is currently no national screening programme for bowel cancer. The purpose of this research is to improve our understanding of Australians’ thoughts and feelings about bowel cancer screening.

Participation involves filling in a survey about your beliefs and feelings about screening for bowel cancer. The questionnaire takes approximately 30 minutes to complete, and is entirely

. You will not be asked to provide your name anywhere on thicommencing the survey, participants should be aware that the nature of the survey means that some people may find certain questions confronting or discomforting. These may include questions about your emotions in medical settings, your bowel health, and ratings of potentially disgusting scenarios. These are standard scales that are used in medical studies and your anonymous answers are completely confidential.

Your completion of the survey implies that you have consented to take part, howeVOLUNTARY, you may still withdraw from participating in this survey at any

This research project is being conducted as part of a Doctor of Philosophy degree. In the event that conference presentations or publications arise from this research, no individual’s responses will be identifiable. Your responses will be completely CONFIDENTIAL. Any resulting publications will include only aggregate responses.

If you have any questions about this project, please contact: a Hamilton, Tel: (03) 9214 5553, or email: [email protected], or:

Prof Susan Moore, Tel (03) 9214 5694, or email: [email protected].

If participation in this survey causes any distress, you can discuss your concerns with a Swinburne

Swinburne Counselling Services, Tel: 9214 8025 (Hawthorn Campus) and 9215 7101 (Lilydale

ii) Swinburne Health Services, Tel: 9214 8483 (Hawthorn campus) and 9215 7106 (Lilydale

iii) Swinburne Psychology Clinic (a low-cost service), Tel: 9214 8653

Should any health concerns arise during or after your participation, we ask that you contact your regular medical professional (e.g., your general practitioner) to seek advice. This project has been approved by or on behalf of Swinburne’s Human Committee (SUHREC) in line with the National Statement on Ethical Conduct in Human Research. If you have any concerns or complaints about the conduct of this project, you can

Research Ethics Officer, Swinburne Research (H68) rne University of Technology, PO Box 218, HAWTHORN VIC 3122

Tel (03) 9214 5218 or +61 3 9214 5218 or [email protected]


Tear off this sheet and retain for your own records

345

APPENDIX C: Study 1 Annotated Survey

BURNE UNIVERSITY OF TECHNOLOGY

SURVEY CONSENT INFORMATION STATEMENT

PROJECT TITLE: Attitudes toward Screening for Bowel Cancer

Victoria Hamilton (PhD candidate)

Prof Susan Moore (Supervisor)


partly dependent on a range of individual risk factors, and some adults are advised to begin screening at age 40. Although home screening tests are being trialed in Australia, there is

e of this research is to improve our understanding of Australians’ thoughts and feelings about bowel cancer screening.

Participation involves filling in a survey about your beliefs and feelings about screening for bowel cancer. The questionnaire takes approximately 30 minutes to complete, and is entirely

. You will not be asked to provide your name anywhere on this booklet. Before commencing the survey, participants should be aware that the nature of the survey means that some people may find certain questions confronting or discomforting. These may include

wel health, and ratings of potentially disgusting scenarios. These are standard scales that are used in medical studies and your

Your completion of the survey implies that you have consented to take part, however, as your , you may still withdraw from participating in this survey at any

This research project is being conducted as part of a Doctor of Philosophy degree. In the event that se from this research, no individual’s responses will be

. Any resulting publications

a Hamilton, Tel: (03) 9214 5553, or email: [email protected], or:

If participation in this survey causes any distress, you can discuss your concerns with a Swinburne

Swinburne Counselling Services, Tel: 9214 8025 (Hawthorn Campus) and 9215 7101 (Lilydale

9214 8483 (Hawthorn campus) and 9215 7106 (Lilydale

Should any health concerns arise during or after your participation, we ask that you contact your

This project has been approved by or on behalf of Swinburne’s Human Research Ethics Committee (SUHREC) in line with the National Statement on Ethical Conduct in Human Research. If you have any concerns or complaints about the conduct of this project, you can

rne University of Technology, PO Box 218, HAWTHORN VIC 3122 Tel (03) 9214 5218 or +61 3 9214 5218 or [email protected]


346

PART 1

Demographic: Please answer the following questions by circling the most appropriate

response, or by filling in your response in the space provided.

1. What is your gender? 5. What is your marital status?

Male 0 Single 1 Female 1 Cohabiting/De facto 2 Married 3 2. What is your employment status? Separated/Divorced 4 Full-time employment 1 Widowed 5 Part-time employment 2 Casual Employment 3 6. What year were you born? 19____ Unemployed 4 Full-time parent not in workforce 5 7a. What is your cultural/ethnic background?

________ 7b. What is your country of residence?____________ 3. Are you currently studying toward a qualification? Not studying at all 1 Studying full-time at tertiary level 2 Studying part-time at tertiary level 3 Other __________________________

4. Do you currently have private health insurance (including single, couple, or family policies)?

None 0

Hospital only 1

Extras only 2

Hospital and extras 3

Personal and Comparative Health Perceptions

8. On average, how would you rate the physical health of other people in your age group in

Australia?

1 2 3 4 5 6 7

Very poor Average Excellent

9. Overall, would you say that for someone of your age your own physical health is: (please circle):

1 2 3 4 5 6 7

Very poor Average Excellent

347

Gastrointestinal History, Screening History (general and CRC); Family History; Screening

Advice

Please circle yes or no to the following statements about your own

health:

NO YES

Have you ever been advised by your doctor or another health professional to get bowel screening?

No Yes

Have you ever had irritable bowel syndrome? No Yes

Have you ever had polyps in the rectum or colon? No Yes

Have you ever had any other bowel condition? No Yes

Do you currently have any serious medical condition? No Yes

Have you ever done, or had a doctor perform, any of the

following?

NO YES

Digital Rectal Examination (DRE)? No Yes

Barium Enema (colon X-ray)? No Yes

FOBT (stool sample test)? No Yes

Flexible Sigmoidoscopy (flexible camera tube in lower colon)? No Yes

Colonoscopy (camera tube in entire colon)? No Yes

Mammography (breast screening)? No Yes

Breast self-examination? No Yes

Cervical screening (pap smear)? No Yes

Prostate screening? No Yes

Testicular self-examination? No Yes

Has anyone in your family: No Yes Not

Sure

Ever been screened for bowel cancer using a stool test? No Yes Not sure

Ever been screened for bowel cancer by a colonoscopy? No Yes Not sure

Had non-cancerous lesions (polyps) in the rectum or colon? No Yes Not sure

Had irritable bowel syndrome (IBS)? No Yes Not sure

Had diverticulitis? (Inflamed ‘pouch’ formations on the colon)? No Yes Not sure

Please write down how many members of your family have had bowel cancer:

If you have a family history of bowel cancer, please indicate the youngest age that someone in your family developed bowel cancer

348

Knowledge (PROCASE modified, Radosevich et al., 2004)

Please respond to the following true/false statements by circling F for false

or T for true:

True False

1. Most people diagnosed as having advanced bowel cancer will die of something else instead

T F

2. People are more likely to die of bowel cancer than of heart disease T F

3. Most people diagnosed as having advanced bowel cancer can be treated and fully recover from surgery and chemotherapy

T F

4. Bowel cancer is not the most common cause of blood in the stool T F

5. Bowel cancer never actually causes problems with bowel habits T F

6. Bowel cancer is one of the most common cancers in older adults T F

7. The stool test (which detects blood in stool) will pick up all bowel cancers

T F

8. A colonoscopy can tell you with more certainty than a stool test whether you have bowel cancer

T F

9. Abdominal cramping is one possible symptom of bowel cancer T F

10. There is always a symptom of the presence of bowel cancer T F

11. Only males aged 50 and older should be screened using a stool test every year

T F

12. If detected early by a screening test, most bowel cancer patients can recover fully

T F

349

PART 2

BRIEF DESCRIPTION OF BOWEL SCREENING TESTS

Bowel cancer is one of the more commonly diagnosed cancers worldwide. We are interested in Australians’ beliefs and feelings about bowel cancer, as well as your views on the available screening tests for its detection.

STOOL TESTING

Faecal Occult Blood Tests (FOBTs) detect occult (hidden) blood in a stool sample. They are conducted by individuals in the privacy of one’s own home, and are returned by mail to the health service provider. There are no physical risks.

Two to three different stool samples are required for analysis. Each sample requires swabbing stool onto a cardboard slide.

Stool samples are examined in a laboratory for traces of blood. The presence of blood in the stool usually requires a referral for follow-up colonoscopy, which is performed in a medical clinic.

COLONOSCOPY

Colonoscopies are performed by medical professionals at a clinic or hospital, and involve the insertion of a small tube into the lower rectum, which can extend to the large bowel. They are performed to investigate and detect premalignant or malignant lesions (polyps) in the rectum and colon, which can be removed easily during the procedure. As with any invasive procedure there are rare physical risks. Risk of bowel perforation during colonoscopy is 1.96 for every 1000 colonoscopies performed.

In Australia, the colonoscopy is the most common procedure a doctor performs to detect colorectal cancers.

Please continue the survey, and don’t worry about whether your response is ‘right’

or ‘wrong’. We appreciate hearing your honest beliefs on bowel screening.

350

Please indicate your agreement or disagreement with the following statements.

If necessary, please also imagine how you would respond if you were older and

considering bowel screening

Social Norms (DiMatteo et al., 1993; Tiro, Vernon et al., 2005; Vernon et al., 1997)

When it comes to screening for

bowel cancer using a stool test or

colonoscopy:

Strongly

disagree

Mildly

disagree

Neither

agree

nor

disagree

Mildly

agree

Strongly

agree

I want to do what members of my immediate family think I should do

1 2 3 4 5

I want to do what my friends think I should do

1 2 3 4 5

I want to do what my regular doctor thinks I should do

1 2 3 4 5

I want to do what people important to me would do

1 2 3 4 5

Social Support (Kremers et al., 2000)

I believe people in my social network would suggest I get screened

1 2 3 4 5

I have someone in my social network who could accompany me to a colonoscopy

1 2 3 4 5

I believe people in my social network would understand my feelings about bowel screening

1 2 3 4 5

I know someone who has been screened for bowel cancer

1 2 3 4 5

351

Screening bias (Busch, 2003; Harewood et al., 2002; Hynam et al., 1995; Perez-Stable et

al., 1992; Schnoll et al., 2003)

Thinking about screening for bowel cancer (if necessary, imagining you are aged 40 or

older) please rate how much you agree or disagree with the following statements:

If I were considering being screened

for bowel cancer, I would be LESS

LIKELY to screen:

Strongly

disagree

Mildly

disagree

Neither

agree

nor

disagree

Mildly

agree

Strongly

agree

If I felt too healthy to have bowel cancer

1 2 3 4 5

Because being diagnosed with cancer is like getting a death sentence

1 2 3 4 5

Because it is not so important to be screened if you feel healthy

1 2 3 4 5

Because I would not rely on the results to be conclusive

1 2 3 4 5

Because there is very little one can do to prevent cancer

1 2 3 4 5

If I have no family history of bowel cancer

1 2 3 4 5

If I don’t have any bowel symptoms 1 2 3 4 5

Because my gender reduces my risk of bowel cancer

1 2 3 4 5

Because you have to be at least 60 years old to be at any real risk of bowel cancer

1 2 3 4 5

Because other important health risks run in my family which make me less concerned about bowel cancer

1 2 3 4 5

352

aCancer Fatalism (PFI; Powe, 1995)

bTest-efficacy (Myers et al., 1996; Tiro, Vernon et al., 2005)

Please state how much you agree or

disagree with the following statements:

Strongly

disagree

Mildly

disagree

Neither

agree

nor

disagree

Mildly

agree

Strongly

agree

No matter what, if someone gets cancer they should prepare for deatha

1 2 3 4 5

I believe that if someone is diagnosed with cancer they will end up dying from ita

1 2 3 4 5

I would rather not know if I had an incurable cancera

1 2 3 4 5

The stool test effectively detects bowel polypsb

1 2 3 4 5

Overall, the results of stool tests are accurateb

1 2 3 4 5

Colonoscopy effectively detects bowel cancerb

1 2 3 4 5

Overall, the results of colonoscopy are accurateb

1 2 3 4 5

When colorectal polyps are found and removed during screening, cancer can be preventedb

1 2 3 4 5

Cancer Worry (BCWS, Lerman et al., 1991; McCaul, Schroeder, & Reid, 1996)

If you were at a screening age (40

years+), please indicate how much you

believe you would think about bowel

cancer:

Not at all A little Somewhat A lot

How much would it make you worry to think about your risk of developing bowel cancer some day?

1 2 3 4

How much would worries about bowel cancer impact your mood?

1 2 3 4

How much would worries about bowel cancer impact your daily activities?

1 2 3 4

What would be your level of concern about the results of future bowel cancer screening tests?

1 2 3 4

How often would you think about your own chances of getting bowel cancer?

1 2 3 4

Overall, how worried would you be about getting bowel cancer some day?

1 2 3 4

353

Risk Perception (Cameron & Diefenbach, 2001; Kremers et al., 2000)

Compared to the average person, please

indicate what you believe your own risk of

bowel cancer is:

Low

chance

(below

average)

High

chance

(above

average)

The chance that I will get bowel cancer at some point in my life is:

1 2 3 4

The chance that I will get bowel cancer when I am over 50 is:

1 2 3 4

The chance that anyone over 50 will get bowel cancer is:

1 2 3 4

PART 3

Please do not spend too long considering your answers to the following

questions.

Respond as quickly as you can, with the first response that comes to mind.

Fear of Screening (Harewood et al., 2002; Hynam et al., 1995; Smith et al., 2005)

How much would you be afraid of the following

if you were thinking about getting screened for

bowel cancer?

Not

at all

Very

much

The discomfort associated with colonoscopy 1 2 3 4 5 6

Risks associated with colonoscopy (e.g. perforation of bowel wall)

1 2 3 4 5 6

The bowel preparation that is required for colonoscopy

1 2 3 4 5 6

Experiencing pain during a colonoscopy 1 2 3 4 5 6

Feeling ashamed by completing a stool test and getting stool samples

1 2 3 4 5 6

Dread just from thinking about getting a colonoscopy

1 2 3 4 5 6

Dread just from thinking about doing a stool test 1 2 3 4 5 6

In general I feel dread about having any medical screening test

1 2 3 4 5 6

Having to have further tests if there is suspicion of cancer

1 2 3 4 5 6

Being seen as a time-waster, a hypochondriac, or as neurotic

1 2 3 4 5 6

Being seen as exaggerating mild or nil symptoms 1 2 3 4 5 6

That my family will think I am concerned about nothing

1 2 3 4 5 6

Being embarrassed about having to discuss my rectum and bowel

1 2 3 4 5 6

354

Not

at all Very

much

That I will appear weak to be concerned or interested about bowel cancer

1 2 3 4 5 6

Finding out that I have a fatal incurable disease 1 2 3 4 5 6

Having cancer with serious and painful symptoms 1 2 3 4 5 6

Having to undergo unpleasant treatment 1 2 3 4 5 6

Loss of sexual libido after treatment 1 2 3 4 5 6

Feeling shame associated with germs and un-cleanliness

1 2 3 4 5 6

Disgust: DS-R (Haidt, McCauley, & Rozin, 1994, modified by Olatunji, Williams et al., 2007)

Please indicate how much you

agree with each of the following

statements, or how true it is

about you

Strongly

disagree

(very untrue

about me)

Mildly

disagree

(some-what

untrue about me)

Neither

agree or

disagree

Mildly

agree

(somewhat true about

me)

Strongly

agree

(very true about me)

I might be willing to try eating monkey meat, under some circumstances

1 2 3 4 5

It would bother me to be in a science class, and to see a human hand preserved in a jar

1 2 3 4 5

It bothers me to hear someone clear a throat full of mucous

1 2 3 4 5

I never let any part of my body touch a public toilet seat

1 2 3 4 5

I would go out of my way to avoid walking through a cemetery

1 2 3 4 5

Seeing a cockroach in someone else’s house does not bother me

1 2 3 4 5

It would bother me tremendously to touch a dead body

1 2 3 4 5

If I see someone vomit, it makes me sick to my stomach

1 2 3 4 5

I probably would not go to my favorite restaurant if I found out that the cook had a cold

1 2 3 4 5

355

Strongly

disagree

(very untrue

about me)

Mildly

disagree

(some-what

untrue about me)

Neither

agree or

disagree

Mildly

agree

(somewhat true about

me)

Strongly

agree

(very true

about me)

It would not upset me at all to watch a person with a glass eye take the eye out of the socket

1 2 3 4 5

It would bother me to see a rat run across my path in a park

1 2 3 4 5

I would rather eat a piece of fruit than a piece of paper

1 2 3 4 5

Even if I was hungry, I would not drink a bowl of my favorite soup if it had been stirred by a used but thoroughly washed flyswatter

1 2 3 4 5

It would bother me to sleep in a nice hotel room if I knew that a man had died of a heart attack in that room the night before

1 2 3 4 5

How disgusting would you find

each of the following experiences?

Please circle a number (1-5) to

indicate your answer: [Medical disgust denoted by c, subsequently refined to bowel-specific disgust]

Not disgusting at

all

Slightly disgusting

Moderately disgusting

Very disgusting

Extremely disgusting

You see maggots on a piece of meat in an outdoor garbage bin

1 2 3 4 5

You see a person eating an apple with a knife and fork

1 2 3 4 5

You discuss the appearance of your stool with your regular doctorc

1 2 3 4 5

While you are walking through a tunnel under a railroad track, you smell urine

1 2 3 4 5

You take a sip of soda, and then realise that you drank from the glass that an acquaintance of yours had been drinking from

1 2 3 4 5

You swab a sample of your stool onto a cardboard slidec

1 2 3 4 5

Your friend’s pet cat dies, and you have to pick up the dead body with your bare hands

1 2 3 4 5

356

Not disgusting at

all

Slightly disgusting

Moderately disgusting

Very disgusting

Extremely disgusting

You see someone put ketchup on vanilla ice cream, and eat it

1 2 3 4 5

You see a man with his intestines exposed after an accident

1 2 3 4 5

You discover that a friend of yours changes underwear only once a week

1 2 3 4 5

A friend offers you a piece of chocolate shaped like dog poo

1 2 3 4 5

You accidentally touch the ashes of a person who has been cremated

1 2 3 4 5

You can see your own stool in the toilet bowl in your home bathroomc

1 2 3 4 5

You are about to drink a glass of milk when you smell that it is spoiled

1 2 3 4 5

As part of a sex education class, you are required to inflate a new un-lubricated condom, using your mouth

1 2 3 4 5

You are walking barefoot on concrete, and you step on an earthworm

1 2 3 4 5

You handle a swab stick and container, which holds a small swab of your own stoolc

1 2 3 4 5

A specialist examines your colon by inserting a camera attached to a flexible tubec

1 2 3 4 5

You lie on a doctor’s examination table where other people have also been examinedc

1 2 3 4 5

You attend an appointment at your doctor and have to share the waiting room with another patient who is coughing and wiping their nosec

1 2 3 4 5

357

Medical Embarrassment: MEQ (Consedine et al., 2007)

Some people have reported that

the following scenarios can be

uncomfortable or humiliating.

Please rate your agreement or

disagreement to the situations

below

Not at

all /

Never

Not often

/ seldom

Sometimes Most of

the time /

Often

Very

much /

Always

1. Showing my body to a stranger, even

to a doctor, is humiliating

1 2 3 4 5

2. I am uncomfortable when a doctor has

to examine my sexual organs or rectum

because I worry about my cleanliness

1 2 3 4 5

3. I feel shy when I have to describe my

bodily functions to a doctor or a nurse

1 2 3 4 5

4. When I have health symptoms, I avoid

the doctor because I worry that my

concerns will turn out to be nothing

1 2 3 4 5

5. I am afraid that I will embarrass

myself if something hurts in the doctor’s

office

1 2 3 4 5

6. I worry that doctors will scold me for

the bad state of my health

1 2 3 4 5

7. It is embarrassing for me when a

doctor or a nurse has to touch me

1 2 3 4 5

8. Having my sexual/reproductive organs

or rectum examined is humiliating for

me

1 2 3 4 5

9. Describing my bowel movements to a

doctor is awkward for me

1 2 3 4 5

10. I feel I must have done something

wrong when I am ill

1 2 3 4 5


doctor examines my body

1 2 3 4 5

12. When a doctor describes some

medical options and I don’t understand, I

feel humiliated

1 2 3 4 5

13. I avoid going to the doctor because I

often wait too long and feel awkward

knowing that I should have gone sooner

1 2 3 4 5

14. Talking with a doctor about how

frequently I use the bathroom and the

nature of my faeces or stool is difficult

for me

1 2 3 4 5

15. Seeing my body during medical

examinations makes me feel silly

1 2 3 4 5

16. Being naked in front of the doctor or

a nurse is embarrassing

1 2 3 4 5

358

Not at all

/ Never

Not often

/ seldom

Sometimes Most of

the time /

Often

Very

much /

Always


doctor who is not of my sex touches my

sexual/reproductive organs during

examination

1 2 3 4 5

18. Describing the colour or consistency

of my stool to a doctor is exceptionally

embarrassing for me

1 2 3 4 5

19. I find it difficult to ask a doctor to

explain something again, repeat

themselves, or use words that I can

understand

1 2 3 4 5

20. Exposing just about any part of my

body for a check up is awkward

1 2 3 4 5

21. I feel degraded when I have to show

my sexual and reproductive organs or

rectum to a doctor

1 2 3 4 5

22. The thought that a doctor might ask

for stool or urine samples is humiliating

for me

1 2 3 4 5

23. I worry that other people will judge

me when I’m sick

1 2 3 4 5

24. I feel shy showing my body to

doctors

1 2 3 4 5

25. It is awkward for me to describe

medical symptoms when they involve

my private parts

1 2 3 4 5

26. I don’t want a doctor or nurse to

think that I am one of those people who

constantly complain about their health

1 2 3 4 5

27. I feel stupid when a doctor tells me

that my symptoms are not as serious as I

thought they were

1 2 3 4 5

28. I worry about what doctors are

thinking when they examine my genitals

1 2 3 4 5

29. Answering questions about my

bodily fluids (e.g. describing the colour

of my mucous) makes me feel self-

conscious

1 2 3 4 5

30. I worry that doctors will think I’m

silly if I come in with a minor complaint

1 2 3 4 5

31. I fear that the doctor will think badly

of me because my own behaviours

probably contributed to my health

problems

1 2 3 4 5

359

PART 4

Screening Intention (Ajzen (2004); Emmons et al. (2008); McCaffery et al. (2003)

If you are (or imagine you are) 40

years or older:

Definitely not

Probably not

Un-decided

Probably yes

Definitely yes

Would you want to be screened for bowel cancer?

1 2 3 4 5

Would you want to be screened using a stool kit?

1 2 3 4 5

Would you want to be screened by colonoscopy?

1 2 3 4 5

Decisional Conflict: DCS (O’Connor, 1995)

If you had to make a decision to get

screened for bowel cancer:

Strongly

disagree

Disagree Neither

agree nor

disagree

Agree Strongly

agree

The decision is easy for me to make 1 2 3 4 5

I understand what I would need to do in this decision

1 2 3 4 5

It’s clear what choice is best for me 1 2 3 4 5

I’m aware of the options I have in this decision

1 2 3 4 5

I feel I know the pros of each option 1 2 3 4 5

I feel I know the cons of each option 1 2 3 4 5

I am clear about how important the pros are to me in this decision

1 2 3 4 5

I am clear about how important the cons are to me in this decision

1 2 3 4 5

I am clear about which is more important to me (the pros or the cons)

1 2 3 4 5

I am making this choice without any pressure from others

1 2 3 4 5

I have the right amount of support from others in making this choice

1 2 3 4 5

I have enough advice about the options 1 2 3 4 5

I feel I have made an informed choice 1 2 3 4 5

My decision shows what is important to me

1 2 3 4 5

I expect to stick with my decision 1 2 3 4 5

I am satisfied with my decision 1 2 3 4 5

360

Finally, please respond to the following questions as though you were seriously considering getting

screened for bowel cancer.

Self-Efficacy: ECQ (Gattuso et al., 1992)

Please circle the number representing the best description of your feelings:

1. How confident are you that you would attend a colonoscopy examination if referred?

1 2 3 4 5 6 7

Not at all confident Somewhat confident Extremely confident

2. How confident are you that you could continue with the preparation and the ensuing colonoscopy?

1 2 3 4 5 6 7


3. How well do you think you could relax your body during the examination (if not under general anaesthetic)?

1 2 3 4 5 6 7


4. How comfortable do you think you would be during the colonoscopy examination?

1 2 3 4 5 6 7

Not at all comfortable Somewhat comfortable Extremely comfortable

5. How much medication (sedative) do you think you would need to relax during a colonoscopy?

1 2 3 4 5 6 7

A great deal of medication Some medication No medication

6. Overall, how confident are you that you could get a colonoscopy without any difficulty?

1 2 3 4 5 6 7


7. How easily do you think you would follow the instructions of a stool test at home?

1 2 3 4 5 6 7

Not at all easily Somewhat easily Extremely easily

361

8. Compared to the average person, how much time do you think your stool test would take?

1 2 3 4 5 6 7

Less time than usual About the same as usual More time than usual

9. Overall, how confident are you that you could complete a stool test without any difficulty?

1 2 3 4 5 6 7


10. Some people may find parts of this survey discomforting. Please rate how much you

found any aspect of this survey confronting or discomforting:

1 2 3 4 5 6 7

Not at all confronting Somewhat confronting Very confronting

THANK YOU FOR YOUR PARTICIPATION

If filling in this survey has raised any health concerns, please contact your GP, or Swinburne Student Health Services at Hawthorn on 9214 8483 (or Lilydale campus:

9215 7106)

If it has raised any emotional issues, please contact Swinburne Counselling on 9214 8025 (Hawthorn) or 9215 7101 (Lilydale campus)

362

APPENDIX D

STUDY 1 RECRUITMENT ADVERT AND PROMOTIONAL MATERIALS

363

APPENDIX D.1 Recruitment advert: Research about bowel cancer

screening attitudes

AN INVESTIGATION OF AUSTRALIANS’ BOWEL

SCREENING ATTITUDES Dr Elizabeth Hardie, Victoria Hamilton

What is the study about?

The study is a survey of attitudes about being screened for bowel cancer,

your own personal medical experiences, the ways you usually deal with

illness, and the feelings that people have in medical settings.

What will I be asked to do?

The project involves responding to a survey, which will take approximately

30 minutes. The personal nature of the questionnaire means that some

participants may find some aspects of the survey confronting. Questions that

might cause discomfort generally refer to personal bowel health history,

having to rate ‘disgusting’ scenarios; and rating how one feels in particular

medical settings.

On the other hand, participants may find the survey interesting, and gain

clearer insight into their own health practices and attitudes toward screening

for serious illness. The findings may also contribute to cancer prevention

programmes and improved bowel cancer screening attendance.

Participation is entirely voluntary, confidential, and anonymity is

guaranteed. Participants may withdraw at any stage of the questionnaire if

they wish to.

Who can participate?

Males and females aged 18 or older.

Australian citizens by birth or immigration

How to take part:

If you would like to find out more about the study or how to take part by

paper-and-pencil format, please email the investigator, Victoria Hamilton, at

[email protected].

To take part online, please click on the link below and follow the prompts:

www.opinio.swin.edu.au.

Completed paper surveys can be returned via pigeon-hole number 48 on floor

10 of the BA building. Thank you for your consideration.

364

APPENDIX D.2 REP (Research Experience Program) Advert for

submission to REP coordinator following ethical approval

REP ProjectREP ProjectREP ProjectREP Project

Australians’ bowel cancer

screening attitudes

Researcher: Victoria Hamilton

Participants Required: 80 Males/Females

Participation Time: 30 minutes

Participants are sought for a study investigating bowel cancer screening attitudes.

The study includes questions about your medical history and experiences, how you usually deal with illness, and the feelings that you might have in certain medical settings. It also asks you to imagine how you might feel and think about future medical decisions.

The personal nature of the questionnaire may be confronting, such as asking you to rate ‘disgusting’ scenarios. However, you may also find the survey interesting, and gain some insight into your attitudes toward various medical experiences and screening for serious illness. Findings may eventually contribute to improving bowel cancer screening uptake rates.

If you would like to participate, you need to be 18 years of age or older. Please email me to get the Weblink to take part online, or to get the survey in hard copy format:

[email protected]

365

APPENDIX E

STUDY 1 RESULTS OF PRECAUTIONARY HYPOTHESES

366

Appendix E: Study 1 Precautionary hypotheses

Precautionary hypothesis (i): Relationship between dependent variables

As expected, higher screening intention was significantly related to lower

decisional conflict, r = -.21, p < .01).

Precautionary hypothesis (ii): Examination of age differences on predictor

variables

It is predicted that there will not be significant differences between a younger

subset of the sample and older participants on the major predictor variables.

A multivariate between-groups analysis of variance (MANOVA) was

conducted to assess differences between younger and older participants on the

dependent, cognitive and emotion variables. This was due to the recruitment of a

predominantly youthful sample where many participants have yet to seriously

consider participation in bowel cancer screening. Participants were collapsed into

two age groups (18-30 years; 31-75 years, respectively). On the outcome variables,

older participants reported higher screening intention, F(1,200) = 5.31, and less

decisional conflict, F(1,200) = 8.69. Against predictions, on the predictor variables

older people reported higher self-efficacy, F(1,200), = 10.47 and less medical

embarrassment, F(1,200) = 8.78 (see Table E.1 for descriptive statistics, p-values,

and effect size).

This hypothesis was mostly supported, with no differences found on eight of

the 10 predictors, however younger people reported lower self-efficacy and greater

medical embarrassment. Given the few differences, it was deemed satisfactory to

explore the data with age collapsed across the sample.

367

Table E.1

Means, SDs, and Differences of an Age-Stratified Sample on Predictor Variables

Age-Stratified Groups

18 – 30 years (n = 154) 31 – 75 years (n = 48)

Variable M SD M SD p-value η2

Risk perception 7.36 2.17 7.15 2.15 .57 .002

Knowledge 7.47 1.49 7.31 1.74 .56 .002

Test-efficacy 14.58 2.35 15.31 2.82 .07 .016

Self-efficacy 35.60 7.85 39.50 10.65 .00** .036

Worry 7.62 2.73 7.69 2.90 .89 .000

Screening bias 12.79 4.86 11.31 5.48 .08 .016

MEQ total 43.67 14.52 36.19 17.48 .00** .042

FEAR total 44.69 14.24 40.23 16.35 .07 .016

DS-R total 29.60 10.47 26.13 10.13 .05 .020

Social support 14.17 3.20 14.90 3.68 .19 .009

Note. MEQ = Medical Embarrassment Questionnaire. DS-R = Disgust Scale-Revised.

N = 202. * p < .05. ** p < .01. η2= partial eta squared effect sizes.

Precautionary hypothesis (iii): Relationship between gender and the predictor

variables

Given that gender was not correlated with the dependent variables (see Table

7.11, Chapter 7) a correlation matrix was explored for the relationship between

gender and the predictor variables (see Table E.2), in order to support the case for

collapsing gender and examining the data as a whole. Being male was associated

with greater screening bias, but with lower animal-reminder and core disgust, and

lower fear of cancer and fear of procedure. Being male was also associated with

bodily embarrassment and interpersonal embarrassment, however this relationship

was weak. With significant differences of only weak to moderate magnitude (two

cognitive variables: being female correlated with lower screening bias and lower

self-efficacy; and seven emotion subscales: animal and core disgust, fear cancer, fear

368

of procedural aspects, bodily embarrassment, and interpersonal embarrassment,

where being female was associated with higher levels of these emotions), the data set

remained collapsed across gender for the purposes of Study 1 for developing and

refining measures in a sample of convenience.

Precautionary hypothesis (iv): Relationship between screening bias and fatalism

There was a moderate relationship between fatalistic thinking styles in

relation to cancer, and screening bias, r = .32, p < .001. To ensure that the

relationship between screening bias and screening intentions still held, a partial

correlation was performed while controlling for cancer fatalism, indicating a strong

negative relationship between bias and intention, r = -.43, p < .001 (the relationship

without controlling for fatalism was r = -.45, p < .001), supporting the precautionary

hypothesis that screening bias and fatalism are unidimensional.

369

Table E.2

Correlations by Gender for the Predictor Variables (Post Factor Analyses, Study 1)

Predictor

Variables

gendera 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

1 Risk -.09

2 TE .021 -.04

3 SE -.19** .02 .35**

4 Know .07 .12 .16* .29**

5 Worry .00 .42** -.06 -.10 -.06

6 Bias -.19** -.09 -.17* -.29** -.27** -.13

7 Norms -.04 .10 .03 -.02 .07 .04 .16*

8 Support .09 .15* .15* .32** .28** .12 -.34** .22**

9 DS-R .32** .00 .03 -.27** -.11 .19** .11 .10 -.04

10 Animal .34** -.01 .07 -.24** -.03 .12 .09 .17* -.01 .89**

11 Core .26** -.02 .04 -.23** -.16* .14 .08 .02 -.07 .83** .60**

12 Cont .10 .05 -.09 -.17* -.10 .26** .13 -.00 -.04 .65** .40** .37**

13 Bowel .12 -.00 -.06 -.54** -.20** .17* .26** .08 -.16* .62** .54** .58** .34**

14 Fear .23** .18* -.09 -.49** -.16* .31** .29** .14* -.12 .50** .46** .40** .32** .57**

15 FC .25** .15* -.10 -.33** -.06 .27** .11 .15* .03 .42** .41** .29** .29** .40** .78**

16 FE .07 .18* -.17* -.39** -.23** .24** .34** .09 -.14 .28** .21** .29** .19** .46** .75** .38**

17 FP .18* .12 -.01 -.48** -.12 .23** .26** .11 -.17* .47** .45** .37** .29** .52** .88** .59** .48**

18 MEQ .16* .14* -.08 -.44** -.20** .24** .32** .09 -.17* .57** .50** .51** .33** .69** .65** .46** .57** .54**

19 JC .05 .10 -.15* -.32 -.25** .28** .34** .08 -.10 .38** .24** .40** .29** .49** .49** .32** .57** .34** .81**

20 BE .18** .17* -.01 -.39 -.12 .21** .26** .08 -.18** .57** .56** .46** .30** .59** .59** .44** .44** .52** .89** .54**

21 IE .16* .10 -.06 -.46** -.17* .15* .26** .07 -.17* .52** .47** .57** .28** .72** .61** .42** .51** .54** .92** .63** .75**

Note. 1. Risk perception; 2 = Test-efficacy; 3 = Self-efficacy; 4 = Knowledge; 5 = Worry; 6 = Screening bias; 7 = Social norms; 8 = Social support; 9 = Disgust scale – Revised; 10 =

Animal-reminder disgust; 11 = Core disgust; 12 = Contamination disgust; 13 = bowel-specific disgust; 14 = Fear total score; 15 = Fear of cancer; 16 = Fear of embarrassment; 17 = Fear of

procedural aspects; 18 = Medical Embarrassment Questionnaire total score; 19 = Judgement concern; 20 = Bodily embarrassment; 21 = Interpersonal embarrassment. aMen = 0, Women =

1.

370

APPENDIX F

STUDY 1 CORRELATION MATRICES FOR ALL COGNITIVE, SOCIAL AND

EMOTION VARIABLES PRIOR TO FACTOR ANALYSIS

371

Table F.1

Correlation Matrix of Predictors with the Outcome Variables, Intention and Decisional Conflict (Prior to Factor Analyses, Study 1)

Intent 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

DCS 1 -.32** 1.00

DS-R 2 -.14* .10 1.00

Core 3 -.06 .06 .90** 1.00

Animal 4 -.14* .17* .88** .73** 1.00

Contam 5 -.17* .02 .84** .65** .54** 1.00

Medical 6 -.36** .24** .70** .61** .60** .61** 1.00

MEQ 7 -.28** .23** .55** .46** .50** .48** .70** 1.00

BE 8 -.31** .23** .57** .48** .55** .45** .69** .96** 1.00

JC 9 -.15* .15* .39** .21** .29** .43** .56** .84** .67** 1.00

Fear10 -.26** .27** .51** .48** .47** .39** .66** .70** .69** .56** 1.00

FP1 1 -.32** .29** .52** .49** .47** .39** .65** .65** .68** .46** .94** 1.000

FC 12 -.13 .16* .44** .41** .43** .31** .47** .51** .51** .38** .83** .70** 1.00

FE 13 -.16* .19** .33** .32** .28** .28** .55** .65** .58** .64** .80** .64** .52** 1.00

Support 14 .36** -.24** -.06 -.00 -.02 -.14 -.18** -.19** -.21** -.12 -.17* -.20** -.04 -.18* 1.00

Norms 15 .08 -.00 .08 .07 .15* -.02 .08 .07 .08 .05 .13 .11 .16* .08 .22** 1.00

Risk 16 .18** .01 .01 -.01 .00 .02 -.00 .13 .13 .10 .17* .11 .15* .19** .15* .10 1.00

SE 17 .49** -.32** -.33** -.30** -.33** -.23** -.53** -.48** -.51** -.29** -.55** -.57** -.40** -.40** .24** -.04 .03 1.00

TE 18 .22** -.19** .04 .04 .06 .01 -.03 -.04 -.01 -.09 -.08 -.01 -.11 -.13 .08 .02 -.04 .21** 1.00

Worry 19 .13 .01 .24** .17* .17* .29** .26** .26** .20** .30** .34** .29** .32** .29** .06 .04 .37** -.11 -.09 1.00

Know 20 .24** -.13* -.17* -.10 -.12 -.21** -.26** -.22** -.19** -.23** -.18** -.15* -.11 -.22** .27** .03 .12 .19** .02 -.14* 1.00

Bias 21 -.46** .34** .19** .10 .12 .28** .32** .40** .34** .44** .39** .36** .23** .42** -.34** .16* -.06 -.24** -.18** .03 -.40** 1.00

Fatalism 22 -.16* .18* .20** .17* .12 .24** .27** .27** .21** .33** .28** .26** .23** .24** -.07 .10 .01 -.22** -.15* .16* -.16* .39**

Note: DCS = Decisional Conflict Scale; DS-R = Disgust Scale – Revised; Core = core disgust; Animal = animal-reminder disgust; Medical = medical-related disgust; MEQ =

Medical Embarrassment total scores; BE = bodily embarrassment; JC = judgement concern; FT = fear of procedural aspects; FC = fear of cancer; FE = fear embarrassment;

Risk = risk perception; SE = self efficacy; TE = test-efficacy; Know = bowel cancer knowledge.

N = 202; Two-tailed significance *p < .05; **p < .01.

372

Table F.2

Correlation Matrix on Outcome Variables by Gender (Males in Upper Quadrant, Females in Lower Quadrant) (Prior to Factor Analyses, Study

1)

Variable Intention to

Screen

Decisional

Conflict

Certain (DCS) Informed (DCS) Clarity (DCS) Support (DCS) Effective (DCS)

Intention to Screen -.31* -.37* -.16 -.23 -.31* -.29

DCS (Total) -.32** .83** .86** .89** .74** .87**

Certain subscale -.48** .76** .62** .65** .50** .71**

Informed subscale -.16* .88** .51** .81** .53** .61**

Clarity subscale -.17* .88** .51** .87** .55** .69**

Support subscale -.32* .81** .54** .67** .63** .65**

Effective subscale -.28** .88** .67** .66** .68** .68**

N = 202. Two-tailed significance * p < .05. **p < .01.

373

Table F.3

Correlations by Gender for the Predictor Variables (Males in Upper Quadrant, Females in Lower Quadrant) (Prior to Factor Analyses, Study 1)

Demographic/Health

Variables

1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 Intention to screen -.31* -.36* -.34* -.33* -.26 -.44** .07 .50** -.02 .20 -.09 .07 -.01

2 Decisional conflict -.32** .47** .25 .45** .38** .53** -.18 -.53** -.16 -.28 .14 -.45** -.01

3 Fear -.24** .25** .54** .59** .43** .49** -.10 -.48** .17 -.33* .58** -.22 .05

4 Medical embarrassment -.27** .24** .74** .75** .75** .39** -.07 -.32** .19 -.30* .47** .01 .07

5 Medical disgust -.38** .21** .67** .68** .78** .28 -.17 -.35** .12 -.34* .48** -.10 -.09

6 Disgust-Scale Revised -.12 .09 .50** .48** .67** .32* -.18 -.21 .16 -.29* .52** .11 -.01

7 Screening bias -.47** .28** .42** .44** .37** .20* -.23 -.43** -.15 -.43** .19 -.30* .23

8 Test-efficacy .27* -.19* -.08 -.03 .01 .09 -.17* .19 -.05 .11 -.16 .25 .31*

9 Self-efficacy .51** -.31** -.54** -.50** -.55** -.31** -.25** .22** .05 .39** -.28 .26 -.06

10 Risk perception .25** .04 .20* .14 -.02 .01 -.06 -.04 .00 -.05 .43** .30* -.08

11 Knowledge .25** -.08 -.16* -.21** -.25** -.15 -.37** -.01 .16* .15 -.34** .09 .18

12 Worry .20* -.03 .28** .19* .20* .17* -.02 -.07 -.07 .35** -.08 .03 -.06

13 Social support .44** -.17* -.19* -.27** -.22** -.12 -.34** .03 .27** .13 .32** .07 .30*

14 Social norms .12 -.01 .17* .08 .13 .13 .13 -.06 -.05 .14 -.00 .08 .21**

Note. Pre-factor analysis correlations.


374

APPENDIX G

EXPLORATORY AND CONFIRMATORY FACTOR ANALYSES FOR STUDY

1: FULL DESCRIPTION OF MEASUREMENT MODEL AND SCALE

REFINEMENT

375

APPENDIX G: Exploratory and Confirmatory Factor Analysis for Study 1

Variables

TABLE OF CONTENTS

G.1 Measurement Model Overview ..............................................................................379

G.2 Measurement Models of Emotion Factors .............................................................379

G.2.1 Disgust factors ............................................................................................379

G.2.1.1 Step 1: Congeneric measurement models of disgust .....................381

G.2.1.2 Step 2: Measurement model of the four factors of disgust ............387

G.2.1.3 Step 3: Exploratory factor analysis of Disgust ..............................390

G.2.1.4 Step 4: Final confirmatory analysis of the new factors of the

DS-R ...........................................................................................................393

G.2.2 Medical Embarrassment ..........................................................................397

G.2.2.1 Step 1: Congeneric measurement models of medical

embarrassment ............................................................................................397

G.2.2.2 Step 2: Two-factor measurement model of medical

embarrassment ............................................................................................402

G.2.2.3 Step 3: Exploratory factor analysis of bodily embarrassment .......403

G.2.2.4 Step 4: Final three-factor measurement model for medical

embarrassment ............................................................................................406

G.2.3 Fear of screening ........................................................................................407

G.2.3.1 Step 1: Congeneric measurement models of fear of screening .....408

G.2.3.2 Step 2: Full measurement model of Fear .......................................413

G.3 Measurement Models of Cognitive Factors ...........................................................415

G.3.1 Congeneric measurement model of risk perception ...................................415

G.3.2 Congeneric measurement model of self-efficacy .......................................416

G.3.2.1 Step 1: Congeneric model of self-efficacy. ...................................417

G. 3.2.2 Step 2: Re-specification of the self-efficacy model. .....................417

G3.3 Congeneric measurement model of worry ..................................................420

G.3.4 Congeneric measurement model of screening bias ....................................422

G.3.5 Congeneric measurement model of test efficacy .......................................423

G.3.6 Knowledge .................................................................................................424

G.4 Measurement Models of Social Factors .................................................................425

G.4.1 Congeneric model of social support for screening .....................................425

G.4.2 Congeneric model of social norms .............................................................425

G.5 Measurement models of outcome variables ...........................................................427

G.5.1 Measurement model of screening intention ...............................................427

G.5.2 Congeneric measurement model of decisional conflict .............................427

G.5.2.1 Step 1: Effective Decision subscale ...............................................428

G.5.2.2 Step 2: Measurement model of the decisional conflict subscales .429

G.5.2.3 Step 3: Exploratory factor analysis (EFA) of the decisional

conflict scale ...............................................................................................432

G.5.2.4 Step 4: Final confirmatory factor analysis on DCS .......................434

376

LIST OF TABLES

Table G.1 Checklist for Interpreting CFA Output ................................................... 380

Table G.2 Summary of the Steps Taken in the Development of the Disgust

Measurement Model .............................................................................. 380

Table G.3 Confirmatory Analysis Factor Loadings and Communalities for Bowel

Disgust .................................................................................................... 391

Table G.4 Factors Loadings and Communalities on Factors of the Disgust Scale–

Revised (DS-R) ....................................................................................... 392

Table G.5 Goodness-of-Fit Indices for Disgust Measurement Models (CFA) ........ 396

Table G.6 Summary of the Steps Taken in the Development of the Medical

Embarrassment Questionnaire (MEQ) Measurement Model .................. 397

Table G.7 Goodness-of-Fit Indices for Medical Embarrassment Measurement

Models (CFA) ......................................................................................... 401

Table G.8 Factors Loadings, Communalities of the Two Factors of Bodily

Embarrassment Subscale ......................................................................... 405

Table G.9 Summary of the Steps Taken in the Development of the Medical

Embarrassment Questionnaire (MEQ) Measurement Model .................. 407

Table G.10 Goodness-of-Fit Indices for Fear of Screening Measurement Models

(CFA) ...................................................................................................... 411

Table G.11 Summary of the Steps Taken in the Development of the Self-Efficacy

Measurement Model ............................................................................... 416

Table G.12 Goodness-of-Fit Indices for Cognitive Measurement Models (CFA) .... 419

Table G.13 Polychoric Correlations for Worry ......................................................... 421

Table G.14 Summary of the Steps Taken in the Development of the DCS

Measurement Model ............................................................................... 428

Table G.15 Goodness-of-Fit Indices for Dependent Variable Measurement Models

(CFA) ...................................................................................................... 431

Table G.16 Factors Loadings and Communalities on New Factors of Decisional

Conflict.................................................................................................... 435

377

LIST OF FIGURES

Figure G.1. Initial measurement model of core disgust with standardised factor

loadings. ........................................................................................................................382

Figure G.2. Re-specification of the core disgust measurement model with

standardised factor loadings. .........................................................................................382

Figure G.3. Initial measurement model of animal reminder disgust with standardised

factor loadings. ..............................................................................................................384

Figure G.4. Re-specification of the measurement model for animal reminder disgust. 384

Figure G.5. Measurement model of contamination disgust. .........................................385

Figure G.6. Hypothesised measurement model of medical disgust. .............................386

Figure G.7. Re-specified model of bowel disgust with standardised estimates. ...........387

Figure G.8. Hypothesised model of disgust with standardised estimates. ....................389

Figure G.9. Final measurement model of animal-reminder disgust. .............................393

Figure G.10. Final measurement model for core disgust. .............................................394

Figure G.11. Final model of contamination disgust correlated with animal-reminder

disgust. ...........................................................................................................................394

Figure G.12. Final second-order measurement model of disgust with standardised

factor loadings. ..............................................................................................................395

Figure G.13. Hypothesised measurement model of bodily embarrassment with

standardised factor loadings. .........................................................................................399

Figure G.14. Initial measurement model of judgement concern with standardised

factor loadings. ..............................................................................................................400

Figure G.15. Final measurement model of judgement concern with standardised

factor loadings. ..............................................................................................................400

Figure G.16. Initial measurement model of the Medical Embarrassment

Questionnaire. ................................................................................................................403

Figure G.17. Final 3-factor measurement model of medical embarrassment. ..............407

Figure G.18. Initial measurement model of fear of procedural aspects. .......................409

Figure G.19. Final measurement model of fear of procedural aspects with

standardised estimates. ..................................................................................................409

Figure G.20. Hypothesised measurement model of fear of cancer with standardised estimates. .......................................................................................................................410

Figure G.21. Final measurement model of Fear of Cancer with standardised estimates. .......................................................................................................................410

Figure G.22. Hypothesised measurement model of fear of embarrassment with

standardised estimates. ..................................................................................................412

Figure G.23. Final measurement model of fear of embarrassment with standardised estimates. .......................................................................................................................412

Figure G.24. Final modified measurement model of fear with standardised estimates.414

378

Figure G.25. Final model of risk perception, correlated with social support. .............. 416

Figure G.26. Hypothesised measurement model of self-efficacy with standardised

estimates. ....................................................................................................................... 417

Figure G.27. Final measurement model of self-efficacy with standardised estimates. 418

Figure G.28. Hypothesised congeneric model of worry using polychoric correlations.421

Figure G.29. Final congeneric model of worry using polychoric correlations. ............ 421

Figure G.30. Initial measurement model of screening bias with standardised

estimates. ....................................................................................................................... 422

Figure G.31. Final modified model of screening bias with standardised estimates. ..... 423

Figure G.33. Final congeneric model of test efficacy with standardised estimates. ..... 424

Figure G.32. Hypothesised congeneric model of test efficacy with standardised

estimates. ....................................................................................................................... 424

Figure G.34. Final congeneric model of social support with standardised estimates. .. 426

Figure G.35. Congeneric model of subjective norms with standardised estimates. ..... 426

Figure G.36. Congeneric model of the primary outcome variable: screening

intention. ....................................................................................................................... 427

Figure G.37. Hypothesised model of the effective subscale of the DCS. ..................... 428

Figure G.38. Final model of the effective subscale of the DCS. .................................. 429

Figure G.39. Final attempted 4-factor model of DCS. .................................................. 432

Figure G.40. Final congeneric model of Decisional Conflict with standardised

coefficients. ................................................................................................................... 435

379

G.1 Measurement Model Overview

Congeneric measurement models illustrate a regression analysis whereby a

set of indicators (items) is regressed onto the underlying latent variable, which is

posited to explain a degree of variance in each indicator. Confirmatory factor

analysis is commonly applied to test the measurement model, and is used in each of

the models tested in this study. CFAs are commonly used in scale development and

construct validity (Brown, 2006, cited in Jackson, Gillaspy, & Purc-Stephenson,

2009). Along with the chi-square test, which is currently the most appropriate test of

power (Cunningham, 2008), the normed chi-square and root mean square error of

approximation (RMSEA) were primarily used to assess model fit.

Non-normality can be determined by Mardia’s statistic. When values of

Mardia’s test of multivariate normality are further away from zero, they reflect

greater non-normality (Kline, 2005). Generally, values of 3.0 or greater are

considered significant departures from multivariate normality (Wothke, 1996, cited

in Raykov & Marcoulides, 2006). In instances of non-normality, the Bollen-Stine

bootstrap p-value (Bollen & Stine, 1992) was used to assess model fit. Bollen-Stine

p-values are based on a bootstrapped modification of the model chi-square and are

suitable when sample sizes are not sufficient (N < 1000) to use alternative estimation

for analysing non-normal data, such as the Asymptotically Distribution Free (ADF)

estimation (Cunningham, 2008). Models were estimated using Amos version 17.0

with a dataset from SPSS version 17.0 and 18.0, while polychoric calculations for the

worry scale were computed in The SAS System version 9.1.2. Table G.1 outlines the

processes involved in determining best model fit.

G.2 Measurement Models of Emotion Factors

G.2.1 Disgust factors

The steps taken to achieve best model fit for the disgust scales (the DS-R and

Medical Disgust) are summarised in Table G.2. A full description of these four steps

follows in Section G.2.1.1 to G.2.1.4.

380

Table G.1

Checklist for Interpreting CFA Output

Step Process

1 Model is admissible

2 Multivariate normality is sufficiently low (< 3.0)a

3 Fit statistics within range (RMSEA, SRMR, TLI, CFI, AGFI, CAIC)

4 Standardised residual covariances (SRC) are < 2.58

5 Standardised regression weights (SRW; or factor loading) are significant

6 Relative sizes of the squared multiple correlations for latent variables (>.20)

7 Consider modification indices for statistical suggestions to the model

8 Scale inter-correlations

9 Discriminant validity between indicators of a latent variable

10 Consider removing non-significant path coefficients (SRW) and high SRC

Note. These steps are based on Cunningham (2008) and Kline (2005). aMardia’s statistic >3.0 advisable to use Bollen-Stine p to assess model significance.

Table G.2

Summary of the Steps Taken in the Development of the Disgust Measurement Model

Step Activity Model Description Section

1 Congeneric models were

tested for each of the four

subscales of Disgust

All models demonstrated poor fit. G.2.1.1

2 Measurement model of

the 4-factors of Disgust

was tested

The model showed poor fit, and

adequate fit could not be achieved on

the basis of theoretically pre-specified

factors.

G.2.1.2

3 Exploratory factor

analysis (EFA)

A stable three-factor solution was

reached.

G.2.1.3

4 Final CFA of Disgust The final confirmatory factor analysis

with some of the items loading on new

factors achieved adequate fit.

G.2.1.4

381

G.2.1.1 Step 1: Congeneric measurement models of disgust

Three congeneric measurement models (core disgust; animal reminder

disgust; contamination disgust) were tested using maximum likelihood (ML)

estimation on the covariance matrix. In addition, a fourth scale of disgust, separate

from the DS-R (medical disgust) was tested in a congeneric model.

No issues of kurtosis or other non-normality problems were detected in any

sub-scale, except for slight kurtosis in bowel-specific disgust. In each congeneric

measurement model, the unique items acted as manifest variables, predicted by the

underlying disgust factor (either core, animal reminder, contamination, or medical-

related disgust). The latent factor variance for each of the sub-scales was set to one

so that the paths could vary freely and the relative importance of each path could be

obtained.

Core Disgust. Figure G.1 displays the hypothesised 12-item congeneric

model of core disgust, in which all items need to be significant in order to obtain a

valid measurement model. The hypothesised model was an inadequate fit to the

implied model, χ2(54) = 91.28, p = .000. (Although Bollen-Stine p = .069 was non-

significant, the distribution was normal and therefore this post-hoc adjustment for

non-normality was not the appropriate indication of fit.) A number of indices also

indicated poor fit. Although the normed chi-square and RMSEA were adequate, the

CFI was too low at .92, as was the TLI at .90.

Because the initial model was a poor fit, it was re-specified based on

theoretical considerations of item importance and also on empirical criteria, which is

often recommended for poor model fit (Kline, 2005). The sample correlations were

inspected for multidimensionality and item redundancy, showing that three factors

had been produced by the model estimate, and that no items were redundant as all

inter-item correlations were below 8.0. Factor loadings were then inspected to

determine whether any indicators had failed to load on the factor, and non-significant

items were removed. Item 6 (“seeing a cockroach in someone else’s house doesn’t

bother me”) was not significant, and had low item reliability (squared multiple

correlation = .04). Standardised residual covariances were also inspected for high

inter-item error (with none > 2.58), and there were no concerns about high residuals.

This process was repeated a further four times until fit began to improve, with a total

of five indicators removed from the model (item 6, 1, 25, 3, 20, respectively). The 5-

382

item re-specified model, χ2(5)

= 3.16, p = .675 achieved adequate fit, (presented in

Figure G.2) and demonstrates unidimensionality.

Figure G.1. Initial measurement model of core disgust with standardised factor

loadings.

Figure G.2. Re-specification of the core disgust measurement model with

standardised factor loadings.

383

Animal Reminder Disgust. The initial measurement yielded a χ2(20)

= 38.08,

p = .01, suggesting that the specified indicators were not well explained by the latent

variable, animal-reminder disgust (see Figure G.3), despite almost all indices

yielding values within recommended ranges.

Initial sample correlations suggested a two-factor model, yet inspection of

factor loadings showed that not all indicators were significantly contributing to the

model. Item 10 (“it would not upset me at all to watch a person with a glass eye take

the eye out of the socket”) had a non-significant loading (p = .40), a standardised

weight of just .07, and squared multiple correlation of only .004. Although the item

may reflect some form of disgust, it did not necessarily fit within the domain of

animal-reminder disgust, the notion that reminders of our animal origins produce a

feeling of revulsion. Item 10 was removed, slightly improving model fit (p = .01),

and resulting in a unidimensional factor.

The model was revised two further times to achieve adequate fit, with the

deletion of item 2 (“it would bother me to be in a science class, and to see a human

hand preserved in a jar”) and item 5 (“I would go out of my way to avoid walking

through a graveyard”). Although the surface structure of items 2 and 5 appear to

reflect animal reminder disgust, they may arguably generate a sense of bravado,

excitement, or curiosity in a predominantly youthful sample (e.g., walking through a

graveyard at night may trigger more bravado than animal-reminder disgust). The 5-

item re-specified model, χ2(5)

= 10.95, p = .05 achieved adequate fit (see Figure

G.4). SRWs range from .60 to .76, SMCs from .36 to .58; and residual covariances

are all below 2.5. Fit indices were in the recommended ranges and verified adequate

fit (Table G.5).

384

Figure G.3. Initial measurement model of animal reminder disgust with standardised

factor loadings.

Figure G.4. Re-specification of the measurement model for animal reminder disgust.

385

Contamination Disgust. The measurement model for contamination disgust

(Figure G.5.) resulted in a significant chi-square value, indicating inadequate fit of

the data to the implied model, χ2(5)

= 17.09, p = .00. There were no concerns about

kurtosis (Mardia’s = 1.44), and no influential outliers were present, however fit

indices were poor and indicative of the significant chi-square (Table G.5). According

to its standardised residual covariances and other attributes of the model, there did

not appear to be any problematic items to indicate model re-specification, and an

eigenvalue greater than 1.0 (eigenvalue = -2.08) supported the unidimensional factor.

Attempts to re-specify the model were therefore made on the basis of item

importance according to factor loadings and squared multiple correlations, however

even the deletion of the least important indicator (item 9: “I would probably not go to

my favourite restaurant if I found out the cook had a cold”), did not improve model

fit. The high correlation between core and contamination disgust during descriptive

analyses may imply a lack of discriminant validity. Therefore, the 5-item scale for

contamination disgust was retained to examine its influence in a second-order model

for disgust.

Figure G.5. Measurement model of contamination disgust.

Medical Disgust. Seven items believed to represent disgust specific to bowel

health and medical settings were included to gauge revulsion that may be uniquely

associated with bowel screening and related medical environments (see Figure G.6).

The initial hypothesised model demonstrated a poor fit, χ2(14)

= 83.56, p = .00,

386

Bollen-Stine p = .00. Mardia’s coefficient suggested that this data were not normally

distributed, and so Bollen-Stine bootstrapping was used to adjust the chi-square for

non-normality. There were no influential outliers.

Factor loadings suggested that Item 5 (“you attend an appointment at your GP

and have to share the waiting room with another patient who is coughing and wiping

their nose”) did not strongly load on the factor and had a low standardised regression

weight of .54 and a squared multiple correlation of .29. Item 5 also shared large

residual error with item 6. The removal of item 5 improved model fit (Bollen-Stine p

= .001), while the subsequent inspection of item 6 suggested it was also a poor

indicator of medical disgust. After removing item 6 (examination table) the

remaining indicators showed adequate model fit, χ2(5)

= 18.13, Bollen-Stine p = .06

(Mardia’s coefficient was also reduced substantially to 3.72). The magnitude of ill fit

reported by the chi-square test may be small enough to be explainable by the non-

normal distribution, however the removal of item 4 led to a final adequate model (see

Figure G.7), χ2(2)

= 7.09, p = .03, Bollen-Stine p = .25 (Mardia = 2.15). Thus, items

4, 5, and 6, which had been directly unrelated to CRC screening, were removed. As

all of the remaining indicators related only to aspects of bowel testing, the factor was

re-named, “bowel disgust”. Fit indices are presented in Table G.5, and factors and

communalities for bowel disgust are presented in Table G.3.

Figure G.6. Hypothesised measurement model of medical disgust.

387

Figure G.7. Re-specified model of bowel disgust with standardised estimates.

G.2.1.2 Step 2: Measurement model of the four factors of disgust

The three factors of the DS-R and the measure of bowel disgust were

examined by CFA in Amos 17.0 to examine ‘disgust’ in a non-clinical sample of

convenience. Olatunji and colleagues (2007) arrived at the current three-factor

structure of the DS-R by similarly assessing confirmatory factor structure of the

original 32-item, 7-factor measure, in a convenience sample. A subsequent follow-up

of reliability and validity in a clinical sample (OCD patients) supported the changes

that led to the Disgust Scale-Revised, the 25-item scale examined in the present

study.

Initial Model. A second-order model tested the four factors together by CFA,

which converged to an admissible solution, but a poor fit to the data yielding a chi-

square (χ2) value of 411.55(203) and a Bollen-Stine p = .00 (see Figure G.8). In

addition, the normed chi-square (χ2/df) was used, which results in a lower value, and

can be thought to indicate reasonable fit if values are between 1.0 and 3.0 (Kline,

2005; Schumacker & Lomax, 2004). The normed chi-square value for the

hypothesised model was 2.03.

No influential outliers were found, however a Mardia’s coefficient of 31.14,

and a critical ratio of 6.81 indicated multivariate non-normality in the model. Based

on the criteria applied in the present study (non-normality indicated by Mardia >

3.0), the distribution of the Disgust data are significantly non-normal. Acutely non-

normal distributions can produce a chi-square value that is too high in addition to

distorting the fit indices that are based on χ2, which can lead to the false rejection of

a true model (a Type II error) (Kline, 2005).

388

One method of treating a non-normal distribution is to use an estimation

method that does not assume multivariate normality (Kline, 2005), such as

asymptotic distribution-free (ADF) procedures, which make no distributional

assumptions. ADF estimation requires a very large sample (Kline, 2005), and as such

ML estimation was the preferred method. As stated in the overview, another

common method in Amos that makes a post-hoc adjustment to non-normal data is

the Bollen-Stine bootstrap p, which adjusts for distributional misspecification of the

model (Bollen & Stine, 1993). It generates a critical chi-square value which is

compared against the modified p-value, and infers a good fit of the data if the Bollen-

Stine p is > .05. The ideal number of bootstrap samples generated by Amos is usually

between the range of 1,000 to 2,000 (Cunningham, 2008). Because of the significant

departure from normality (Mardia’s = 31.14, critical ratio = 6.81), the Bollen-Stine

bootstrap p is used to measure model fit on the present Disgust data. Yielding a

Bollen-Stine p = .00 in the present model, reasons for poor fit were explored firstly

by inspecting fit indices, which showed poor fit for all index values (see Table G.5).

The factor loadings, standardised residual covariances, implied correlations

(for all variables), and squared multiple correlations were next inspected. Sample

correlations showed that there were four eigenvalues > 1.0, as well as a potential fifth

(with an eigenvalue = 1.00). All indicators had significant factor loadings with

standardised regression weights > .45. Squared multiple correlations supported item

reliabilities, with values above .3 except on items 4, 9, 13, 11, 26, and 8 (which had

SMCs ranging from .2 to .3). Implied correlations suggested each indicator

correlated most with its underlying factor as specified in the model. Standardised

residual covariances (SRC) revealed excessive error between three item pairs,

ranging from 2.58 to 3.59.

389

Figure G.8. Hypothesised model of disgust with standardised estimates.

Lack of discriminant validity between disgust factors suggested the structure

in Figure G.8 had strong covariation, reflecting one underlying construct (disgust).

Core disgust and animal reminder disgust were highly correlated (r = .91).

Correlations between latent variables greater than .90 indicate issues with the

convergent validity of the model (Cunningham, 2008). With at least two correlations

approaching or above this value, the factors may not be measuring unique constructs,

despite prior empirical support for the multidimensionality of these factors on the

DS-R (Olatunji et al., 2007). Cross-loading and further re-specification of the current

model may lead to a loss of interpretability of the results, and as the structure was

clearly inappropriate in this data set, the analysis became exploratory in nature. As

such, the original scale was subjected to an exploratory factor analysis in SPSS 17.0.

390

G.2.1.3 Step 3: Exploratory factor analysis of Disgust

An exploratory approach was adopted following the poor structure and lack of

discriminant validity of DS-R factors in the proposed measurement model of

Disgust. Variables describing an overarching construct can be examined for factor

structure (Preacher & MacCallum, 2003), and even established scales should be

subjected to EFA when necessary, particularly when psychometric properties are

inadequate (Reise, Waller, & Comrey, 2000). EFA was chosen instead of Principal

Components Analysis (PCA) because PCA makes no distinction between common

and unique variance amongst the variables (Preacher & MacCallum, 2003), and

common variance would be expected for factors representing a common latent

variable. It is almost always safer to assume that underlying variables of a set of

measures are correlated (Preacher & MacCallum, 2003).

According to Cronbach’s alpha, the reliability for the DS-R in the present

study was very good (α = .89). The corrected item-total correlations were assessed

for adequacy based on the criterion of .30 (Nunnally & Bernstein, 1994), where three

items were identified as unacceptable (items 1, 6, and 10), however, according to

item-total statistics, the deletion of any single item would not improve the scale’s

Cronbach’s alpha. SPSS version 17.0 for Mac was used to explore factor structure.

Exploratory factor analysis was performed on the 25 items of the DS-R. As

one of the main aims of the analysis was to examine shared variance, Maximum

Likelihood (ML) extraction and oblique rotations were employed to allow for the

expected correlations between the factors. An initial solution suggested six factors,

based on the K1-rule. The pattern matrix was examined for simple structure, which

showed that no items loaded on the sixth factor, and this factor was therefore not

suitable for retention in the model. Seven solutions were examined before arriving at

a three-factor solution, during which a number of factors were removed due to

having no salient factor loadings. Cross-loadings were double-checked across

different factor solutions and rotations (Direct Oblimin) to ensure a stable solution.

The final analysis showed a stable and interpretable solution, although one

item (“sharing soda”) showed complex factor loadings. Factor one, Animal-Reminder

Disgust, contained six items with loadings all > .5, comprising four items from the

original DS-R animal-reminder factor. There were also two items that had originally

represented core disgust, however their content could be seen as overlapping with

animal-reminder disgust, particularly item 16 (“you see maggots on a piece of meat

391

in an outdoor garbage pail”). Likewise, item 5 (“if I see someone vomit it makes me

sick to the stomach”) could be perceived either as a reminder of illness and death, or

as a body product triggering oral repugnance. These six items were retained and the

factor was called ‘animal-reminder disgust’.

Factor two contained six items with factor loadings all > .45, consisting of

items mainly from core disgust. This second factor can therefore be conceptualised

as reflecting the Core Disgust factor. Only two items were not from the original core

disgust sub-scale and were derived instead from the contamination domain.

However, their content can also be seen as overlapping with disgust that is based on

waste products and a sense of offensiveness, respectively (“a friend offers you a

piece of chocolate shaped like dog poo” and “as part of a sex education class, you are

required to inflate a new lubricated condom, using your mouth”).

The final factor contained three items with factor loadings > .50. Two of the

items were originally from the contamination disgust factor, however one item was

from the original core disgust subscale (“even if I was hungry I would not drink a

bowl of my favourite soup if it had been stirred with a used but thoroughly washed

flyswatter”). Although it could be associated with core disgust, it may be more

reflective of a general threat of transmission or contagious disease. This three-item

factor therefore reflected the Contamination component of the DS-R. The three

factors correlated moderately well (r = .35, .50, and .37) while also showing good

discriminant validity. A total of 41.44% of the total variance was explained by the

three-factor solution. Pattern coefficients, factor loadings and communalities are

shown in Table G.4.

Table G.3

Confirmatory Analysis Factor Loadings and Communalities for Bowel Disgust

Original item number: description Factor Loading Communality

Factor 4: Bowel disgust

1: discuss stool appearance with doctor .71 .50

2: swab stool sample onto cardboard slide .83 .68

3: see stool in toilet bowl .69 .48

7: handle swab stick and container with stool sample .88 .78

Total variance explained by bowel-specific disgust 60.95%

Note. Reduced by confirmatory factor analysis from 7 original items.

N = 202.

392

Table G.4

Factors Loadings and Communalities on Factors of the Disgust Scale–Revised (DS-R)

Factor names and original item number: description

Pattern

coefficient

Factor

loading Communality

Factor 1: Animal-reminder disgust

2: would bother me in a science class to see a preserved

human hand

.46 .53 .29

7: would bother me tremendously to touch a dead body .76 .77 .60

8: seeing someone vomit makes me sick to my stomach .45 .58 .38

14: would bother me to sleep in a hotel room if I knew a

man had died of a heart attack in that room the night

before

.44 .55 .35

15: see maggots on a piece of meat in an outdoor

garbage pail

.41 .56 .40

21: see a man with his intestines exposed after an

accident

.78 .69 .50

Total variance explained by Factor 1 29.93%

Factor 2: Core disgust

20: see someone put ketchup on vanilla ice-cream and

eat it

.84 .77 .63

22: discover a friend of yours changes underwear only

once a week

.42 .60 .45

23: friend offers you piece of chocolate shaped like dog-

poo

.55 .64 .44

25: about to drink a glass of milk and smell it is spoiled .37 .43 .20

27: walking barefoot on concrete and step on an

earthworm

.26 .45 .29


Factor 3: Contamination disgust

4: never let any part of body touch toilet seat in public

rest rooms

.72 .69 .48

13: even if hungry, would not drink a bowl of favourite

soup if it had been stirred by a used but thoroughly

washed flyswatter

.64 .66 .46

18: take a sip of soda and realize drank from the glass

that an acquaintance had been drinking from

.42 .49 .31


Note. Original items are numbered between 1 and 27, with two additional items used only in

administration of the scale to detect inappropriate responding (e.g., “I would rather eat a piece of fruit

than a piece of paper”).

N = 202.

393

G.2.1.4 Step 4: Final confirmatory analysis of the new factors of the DS-R

Each subscale of the DS-R was normally distributed and therefore the

conventional chi-square p-value is reported in the following results. The final

measurement model for the modified animal-reminder disgust, based on the

exploratory analysis of the DS-R, was a superior fit to the model proposed in the

published factor analysis of the scale (see Olatunji et al., 2007), χ2(9)

= 7.56, p =

.579 (see Figure G.9). Better model fit was also attained for core disgust after the

removal of “inflate condom”, χ2(5)

= 3.16, p = .675 (see Figure G.10). As

contamination disgust was reduced to three indicators, it was assessed for fit in a

correlated model with animal-reminder disgust, achieving adequate fit, χ2(26)

=

47.06, p = .01, Bollen-Stine p=.13 (see Figure G. 11). Almost all fit indices were in

the desired ranges, and are presented in a series of tables and figures below.

A full measurement model of the DS-R achieved adequate fit, χ2(74)

=

151.57, p = .00, Bollen-Stine p = .00, CMIM/df = 2.05, RMSEA = .07. A final

second-order model incorporating bowel disgust also achieved adequate fit, and

factors were highly correlated, reflecting an underlying second-order latent variable,

Disgust, χ2(131)

= 241.31, p = .00, CMIN/df=1.84, RMSEA=.06 (see Figure G.12).

Figure G.9. Final measurement model of animal-reminder disgust.

394

Figure G.10. Final measurement model for core disgust.

Figure G.11. Final model of contamination disgust correlated with animal-reminder

disgust.

395

Figure G.12. Final second-order measurement model of disgust with standardised

factor loadings.

396

Table G.5

Goodness-of-Fit Indices for Disgust Measurement Models (CFA)

Disgust Factor χ2/df

a RMSEA

a RMSEA 90%CI SRMR CFI GFI AGFI TLI CAIC

<3.0 <.08 <.10 >.95 >.90 >.90 >.95 Lower

values

Core Initial 1.69 .06 .037, .079 .06 .92 .93 .89 .90 242.68

Final 0.63 .00 .000, .077 .02 1.00 .99 .98 1.02 66.25

Animal

Reminder

Initial 1.90 .07 .033, .099 .05 .95 .95 .92 .93 139.01

Final 0.84 .00 .000, .070 .02 1.00 .99 .97 1.01 83.26

Contamination Initial 3.42 .11 .056, .169 .06 .90 .97 .90 .80 80.17

Finald 1.81 .06 .033, .092 .06 .95 .95 .91 .93 166.92

Bowel-specific Initial 5.97 .16 .126, .191 .07 .89 .89 .78 .84 171.88

Final 3.55 .11 .031, .207 .02 .99 .98 .92 .96 57.56

4-Factor

Measurement

model

Initial 2.03 .07 .062, .081 .06 .88 .84 .81 .86 726.96

Final 1.84 .06 .052, .077 .06 .91 .89 .85 .90 493.64

aMain criteria used to determine model fit in the present research. bBollen-Stine p-value which is used when non-normality is indicated by a Mardia’s coefficient > 3.0. cInitial model achieved adequate fit. dFinal contamination subscale was reduced to three items and was therefore correlated with animal-reminder disgust.

397

G.2.2 Medical Embarrassment

Both factors of the MEQ (Bodily Embarrassment and Judgement Concern) were

first assessed in the present investigation in separate, congeneric models. The steps

taken to achieve adequate fit for a full measurement model of the MEQ are depicted in

Table G.6.

Table G.6

Summary of the Steps Taken in the Development of the Medical Embarrassment

Questionnaire (MEQ) Measurement Model


1 Congeneric models were tested

for each of the two subscales of

the MEQ

Judgement concern demonstrated

adequate fit. Bodily embarrassment

showed inadequate fit.

G.2.2.1

2 Measurement model (CFA) of

the 2-factor model of the MEQ

The measurement model showed

poor fit, and adequate fit could not

be achieved on the basis of

theoretically pre-specified factors.

G.2.2.2

3 Exploratory factor analysis

(EFA) of bodily embarrassment

An EFA was performed on bodily

embarrassment, resulting in a stable

two-factor solution.

G.2.2.3

4 Final CFA of the 3-factor

model of the MEQ

The final confirmatory factor

analysis, with a new third factor

called “Interpersonal

Embarrassment” achieved adequate

fit.

G.2.2.4

G.2.2.1 Step 1: Congeneric measurement models of medical embarrassment

Two measurement models of the 31-item scale of medical embarrassment

(bodily embarrassment and judgement concern) were tested using ML estimation. The

observed variables in the model (i.e., the scale items) were reflective indicators of

bodily embarrassment or judgement concern, and the variance of both latent factors was

forced to zero in order to examine the significance of the loadings on their respective

latent variable. A CFA was then employed to assess the structure of each factor.

398

Bodily Embarrassment (BE). A model with 19 reflective indicators that

represent BE is depicted in Figure G.13. The chi-square test revealed poor fit (χ2(159)

=

976.76, p = .00, Bollen-Stine p = .00), which was also indicated by inadequate fit

indices (see Table G.7). Non-normality was also an issue with a Mardia’s coefficient of

111.96, and for this reason the Bollen-Stine p = .00 was used, from which it was

concluded that non normality alone was unable to account for the large degree of poor

model fit. Outliers can contribute to non-normality, and as such the model was re-run

after the removal of case 155, which had a Mahalanobis distance five points above the

next highest value. As its removal did not alter the model, there appeared to be no

influential outliers affecting the model.

The model was inspected for structure, however all paths showed significant

factor loadings (standardised loadings ranged from .75 to .87), and good item

reliabilities (SMCs ranged from .56 to .75). There were also no residual covariances >

2.58. The problems with model specification may therefore stem from excessive inter-

item correlations and factor loadings, rather than from unimportant or irrelevant

indicators.

With no statistical or theoretically driven indications for model re-specification

of BE, it was assumed that poor fit was due to the strong inter-item correlations, and

potential singularity of some indicators within the model. The model was retained to

test with judgement concern in a full measurement model of medical embarrassment, at

which stage a decision about conducting further exploratory analysis could be made.

Judgement Concern (JC). Twelve indicators, believed to measure concern

about negative evaluation in a medical setting, were used to measure JC (Figure G.14).

The sample data did not fit the hypothesised model well, and as Mardia’s coefficient

indicated kurtosis (Mardia’s=68.40) the Bollen-Stine p was used to assess fit, χ2(54)

=

280.38, p = .000, Bollen-Stine p = .000. Inspection of the fit indices also showed that

the model was not adequate (see Table G.7).

All paths in the model were significant, and item reliabilities were sufficiently

high (SRW ranged from .59 to .80, and SMCs ranged from .37 to .63, respectively).

There was high error covariance between items 5 and 7 (= 2.78), and after removing

item 7 the model fit improved. A further three indicators were removed in three

subsequent re-specifications of the model, eventually achieving sufficient fit, χ2(20)

=

399

44.17, p = .001, Bollen-Stine p = .26, with evidence of unidimensionality (one

eigenvalue > 1.0 = 4.64) (see Figure G.15).

Figure G.13. Hypothesised measurement model of bodily embarrassment with

standardised factor loadings.

400

Figure G.14. Initial measurement model of judgement concern with standardised factor

loadings.

Figure G.15. Final measurement model of judgement concern with standardised factor

loadings.

401

Table G.7

Goodness-of-Fit Indices for Medical Embarrassment Measurement Models (CFA)

Medical Embarrassment Factor

χ2/df

a RMSEA


<3.0 <.08 <.10 >.95 >.90 >.90 >.95 Lower

values

Bodily

Embarrassment

Initialc 6.42 .16 .154, .174 .07 .80 .55 .44 .78 1215.87

Judgement

Concern

Initial 5.19 .14 .128, .161 .07 .83 .79 .69 .79 431.74

Final 2.21 .08 .046, .109 .03 .97 .95 .91 .96 145.10

Measurement

Model

Initial

2-factor 3.90 .12 .114, .126 .08 .78 .57 .51 .77 2088.06

Final

3-factorb 1.97 .07 .056, .083 .05 .96 .89 .85 .95 462.41

aMain criteria used to determine model fit in the present research.

bBollen-Stine p-value which is used when non-normality is indicated by a Mardia’s coefficient > 3.0.

cExploratory factor analysis was used to achieve stable factor structure.

402

G.2.2.2 Step 2: Two-factor measurement model of medical embarrassment

The full model of medical embarrassment, including bodily embarrassment and

judgement concern, is presented in Figure G.16. Despite achieving adequate fit for the

measurement of judgement concern, the full medical embarrassment model was

inadequate, χ2(433)

= 1690.60, p = .000, Bollen-Stine p = .000. The distributions in the

model were highly non-normal (Mardia’s coefficient = 217.72), however there were no

influential outliers. All of the fit indices were outside of their recommended ranges (see

initial model fit in Table G.7), showing a markedly poor fit of the data to the model

implied. Although the fit indices of the model were lacking, the separate structure of

judgement concern from bodily embarrassment was evident. The correlation between

the factors was r = .71, and the implied correlation matrix showed that all items loaded

strongly on their respective factor. Bodily embarrassment warranted further

examination of an exploratory nature, as such, an EFA was conducted in SPSS 17.0.

403

Figure G.16. Initial measurement model of the Medical Embarrassment Questionnaire.

G.2.2.3 Step 3: Exploratory factor analysis of bodily embarrassment

The scale reliability of Bodily Embarrassment was excellent, with a Cronbach’s

alpha of .97, and a high mean inter-item correlation of .65 (range = .47 to .85).

Corrected item-total correlations ranged from .74 to .85, confirming that no single item

in the scale was inadequate.

An exploratory factor analysis on bodily embarrassment was performed, as it

was suspected to be a multidimensional subscale. Because the distribution cannot be

404

considered normal, the unweighted least squares factor extraction method was

employed, with Promax rotation for correlated items. A total of 72% of the variance

was explained by a two-factor solution (r = .76), however this was largely made up by

one ‘super factor’ accounting for 65% of the variance. Inspection of the pattern matrix

showed simple factor structure for the two factors, with only item 13 loading below .5

(“exposing just about any part of my body for a check-up is awkward”). The analysis

was re-run without item 13, and a clean two-factor solution was produced (r = .75),

explaining 73% of the total variance in Bodily Embarrassment.

This solution was supported by inspection of the Scree plot, and sampling

adequacy was confirmed by a very high Kaiser-Meyer-Olkin mean of .96.

Communalities were all > .60, however there were two items that appeared to be cross

loading on both factors (item 17: “describing private bodily parts is awkward”; and item

5: “having my rectum examined is humiliating for me”) and thus the analysis was

performed without these items. The new two-factor solution (r = .74) showed improved

factor structure, with items clearly discriminating between two factors. Total variance

explained by the final two-factor solution was 73%; with the second factor explaining

slightly greater variance than it did in the initial solution (7.7%).

The new structure of bodily embarrassment into two separable factors was put

into the full measurement model (while factor 1, Judgement Concern, remained

unchanged). Items on the second factor related to self-consciousness about bodily

appearance, and this factor was labelled “Bodily Embarrassment”, while the second

factor depicted self-consciousness during direct conversation and interaction with the

doctor, and was labelled “Interpersonal Embarrassment”. Table G.8 depicts the new

factors.

405

Table G.8

Factors Loadings, Communalities of the Two Factors of Bodily Embarrassment

Subscale

Factor 2: Bodily embarrassment Pattern

coefficient

Factor

loading

Communality

10: Being naked in front of the doctor or nurse is

embarrassing 1.00 .92 .85

16: I feel shy showing my body to doctors .88 .90 .81

2: I am uncomfortable when a doctor has to examine my

sexual organs or rectum because I worry about my own

cleanliness

.85 .84 .71

1: Showing my body to a stranger, even to a doctor, is

humiliating .83 .84 .66

11: It is embarrassing for me when a doctor who is not

of my sex touches my sexual/reproductive organs during

examination

.78 .83 .70

7: It is embarrassing for me when a doctor examines my

body .77 .82 .71

4: It is embarrassing for me when a doctor or a nurse has

to touch me .67 .81 .68

9: Seeing my body during medical examinations makes

me feel silly .60 .77 .62

Total Variance Explained by Factor 1 65.17%

Factor 3: Interpersonal embarrassment Pattern

coefficient

Factor

loading

Communality

8: Talking with a doctor about how frequently I use the

bathroom and the nature of my faeces or stool is

difficult for me

.94 .91 .77

12: Describing the colour or consistency of my stool to a

doctor is exceptionally embarrassing for me .92 .90 .83

6: Describing my bowel movements to a doctor is

awkward for me .89 .88 .80

15: The thought that a doctor might ask for a stool

sample is humiliating for me .87 .85 .73

19: Answering questions about my bodily fluids (e.g.

describing the colour of my mucous) makes me feel

self-conscious

.81 .83 .68

3: I feel shy when I have to describe my bodily

functions to a doctor or a nurse .66 .83 .64

18: I worry about what doctors are thinking when they

examine my genitals .65 .83 .72

14: I feel degraded when I have to show my sexual or

reproductive organs or rectum to a doctor .57 .79 .74

Total Variance Explained by Factor 2 7.66%

N=202

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G.2.2.4 Step 4: Final three-factor measurement model for medical embarrassment

As the MEQ is a relatively recent measure it had been deemed appropriate to

return to an exploratory stage of the BE subscale in the present study to resolve issues

about the lack of discriminant validity and to test for multidimensionality.

Subsequently, a three-factor confirmatory model of medical embarrassment (MEQ),

consisting of Judgement Concern, Bodily Embarrassment, and Interpersonal

Embarrassment, was tested, χ2(249)

= 688.36, p = .000. Adequate fit is demonstrated by

the fit indices and normed chi-square in Table G.7.

Modifications were made to this three-factor model in Amos according to the

degree of correlated residuals between several items, which was interpreted from the

modification indices for covariances. Item pairs producing a high level of residual

covariance were inspected for content overlap (e.g., between item 10, “being naked in

front of the doctor or nurse” and item 11 “embarrassment with a doctor of the opposite

sex during examination of private parts”, and a decision was made to remove the least

relevant indicator (item 10). Seven re-specifications were made to the model until no

further changes could substantially improve fit diagnostics and structure, resulting in a

final sufficient-fitting three-factor model, χ2(116)

= 229.00, Bollen-Stine p = .05. Figure

G.17 illustrates the final re-specified model of Medical Embarrassment used in

subsequent analysis in Study 1, and re-administration in Study 2.

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Figure G.17. Final 3-factor measurement model of medical embarrassment.

G.2.3 Fear of screening

A summary of the two-step procedure in achieving adequate fit for the measurement

model of fear of screening is presented in Table G.9.

Table G.9

Summary of the Steps Taken in the Development of the Medical Embarrassment

Questionnaire (MEQ) Measurement Model


1 Congeneric models were tested

for each of the three subscales

of fear of screening.

Sufficient fit of each of the three

subscales was achieved.

G.2.3.1

2 Measurement model (CFA) of

the 3-factor model of fear of

screening


adequate fit as a 3-factor model,

subsequent to item deletion and re-

structure.

G.2.3.2

408

G.2.3.1 Step 1: Congeneric measurement models of fear of screening

Three measurement models for fear are presented below. Each factor was tested

using maximum likelihood (ML) estimation on the data covariance matrix. Non-

normality was apparent in each of the measurement models, and Bollen-Stine p-values

were used in an attempt to overcome the violation of the assumption of normality. Each

congeneric model measures the indicators as reflective manifest variables, which are

predicted by the proposed underlying factor of fear. The variance of the latent variables

in each model was set to 1.0 to obtain the significance of each indicator.

Fear of Procedural Aspects. The hypothesised measurement model for fear of

procedural aspects of screening is depicted in Figure G.18. Nine reflective indicators

were regressed on fear of procedure, yielding a poor fit to the data, χ2(27)

= 193.76, p =

.000, Bollen-Stine p = .000.

The initial model also demonstrated poor fit according to a range of standard

goodness-of-fit indices (see Table G.10.). Structural aspects of the model were checked,

showing that all paths were significant, and standardised regression weights ranged

from .59 to .89, while item reliabilities were also sufficient (ranging from .35 to .80).

Three re-specifications were made to the measurement model based on residual

covariances between items that were potentially overlapping. Items 7 and 8 (“dread over

stool test” and “dread over any testing”, respectively) had a high level of residual error,

and these concepts may have been captured best by items 6 “dread of colonoscopy” and

9 “fear needing further tests”. The model was re-specified by removing three items one

at a time in the following order: 7, 8, and 5; until adequate fit was obtained for a six-

item factor (see Figure G.19). Fit indices were in recommended ranges (see Table

G.10.). The final model showed adequate fit, χ2(9)

= 13.90, p = .13, Bollen-Stine p =

.78.

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Figure G.18. Initial measurement model of fear of procedural aspects.

Figure G.19. Final measurement model of fear of procedural aspects with standardised

estimates.

Fear of Cancer. The initial measurement model for fear of cancer produced a

poor fit to the data, χ2(5) = 34.22, p = .00, Bollen-Stine p = .00 (Mardia’s coefficient =

10.56) (see Figure G.20). Fit indices such as RMSEA and AGFI, which are based on the

model chi-square, confirmed the model’s poor fit (see Table G.10).

Initial sample correlations supported the unidimensionality of fear of cancer, and

all paths in the model were significant, however not all of the standardised residual

covariances were sufficiently low. The residual error between items 4 and 5 (= 3.53)

410

suggested that the removal of one of these items may benefit the model fit, and as item

5 (“shame associated with uncleanness”) showed particularly low reliability (SMC =

.20) and was arguably more suited to the fear of embarrassment factor, it was removed.

The model subsequently showed reasonable fit, χ2(2)

= 1.97, p = .37, Bollen-Stine p =

1.00 (see Figure G.21), also indicated by the goodness-of-fit indices (see Table G.10).

Figure G.20. Hypothesised measurement model of fear of cancer with standardised

estimates.

Figure G.21. Final measurement model of Fear of Cancer with standardised estimates.

411

Table G.10

Goodness-of-Fit Indices for Fear of Screening Measurement Models (CFA)

Fear of Screening Factor

χ2/df

a RMSEA


<3.0 <.08 <.10 >.95 >.90 >.90 >.95 Lower

values

Fear of Procedural

Aspects

Initial 7.18 .17 .153, .199 .07 .86 .81 .69 .81 307.31

Final 1.54 .05 .000, .103 .03 .99 .98 .95 .99 89.60

Fear of Cancer Initial 6.84 .17 .119, .227 .07

.94 .94 .82 .89 97.30

Final 0.99 .00 .000, .139 .01 1.00 .99 .98 1.00 52.44

Fear of

Embarrassment

Initial 9.09 .20 .150, .256 .06 .94 .91 .72 .88 108.52

Final 7.29 .18 .099, .267 .04 .98 .97 .82 .93 65.04

3-Factor

Measurement

Model

Initial 2.80 .09 .080, .110 .06 .93 .87 .82 .91 402.89

Final 2.33 .08 .064, .099 .06 .95 .90 .86 .94 327.54

aMain criteria used to determine model fit in the present research.

bBollen-Stine p-value which is used when non-normality is indicated by a Mardia’s coefficient > 3.0.

cInitial model achieved adequate fit.

412

Fear of Embarrassment. The five items posited to represent fear of

embarrassment in medical settings are depicted in Figure G.22. The hypothesised model

displayed a poor fit to the data, and reasons for this were explored via a range of fit

indices and attributes of the model (surface structure of items, path significance, item

reliability, and residual error). Item four contained the poorest reliability (SMC = .33)

and lowest path to fear of embarrassment (SRW = .57), and the removal of this item

substantially improved fit, χ2(2)

= 14.58, p = .000, Bollen-Stine p = .08 (Mardia’s =

13.82) (Figure G.23, Table G.10).

Figure G.22. Hypothesised measurement model of fear of embarrassment with

standardised estimates.

Figure G.23. Final measurement model of fear of embarrassment with standardised

estimates.

413

G.2.3.2 Step 2: Full measurement model of Fear

The three factors of fear were then tested by confirmatory factor analysis in a

second order measurement model in Amos 17.0.

Initial Model. The solution of the analysis converged, however the fit of the

model was poor and displayed inadequate properties on all of the standard indications of

model fit, χ2(74)

= 21.74, p = .000, Bollen-Stine p = .000, see indices for the initial 3-

factor model in Table G.10. The ninth item of Fear of Procedural Aspects loaded best

on Fear of Cancer according to the implied correlations and the high levels of residual

covariances (item 3 = 3.15, item 2 = 3.11, and item 1 = 3.51 on Fear Cancer). The

fourth item of Fear Cancer, “fear of losing libido”, also loaded poorly on its original

factor and was removed from the model.

Final Model. When the item FP9 (“fear of needing further tests”) was loaded

onto Fear of Cancer, the model was re-run, achieving better fit, χ2(62)

= 144.60, p =

.00, Bollen-Stine p = .01 (Mardia’s = 55.09, which was still sufficiently high to impede

best fit of the data). Sample correlations supported the three-factor structure with three

eigenvalues > 1.00, and inspection of implied correlations showed discriminant validity

between factors and no further cross-loadings (correlations between factors were strong

without approaching singularity: r = .65, .44, and .52). All paths were significant on

their latent factor, standardised regression weights ranged from .67 to .94, and items

showed good reliability (ranging from .45 to .88). There were no standardised residual

covariances > 2.58. Figure G.24 depicts the final model.

414

Figure G.24. Final modified measurement model of fear with standardised estimates.

415

G.3 Measurement Models of Cognitive Factors

G.3.1 Congeneric measurement model of risk perception

Scale reliability was satisfactory with a Cronbach’s alpha of .78. Risk perception was

then correlated with social support because it is not viable to perform a congeneric

analysis on a 3-indicator variable (D. Meyer, personal communication, November 18,

2009).

The initial model of risk perception, correlated with social support, revealed an

error message about negative variance for item 2 (“risk of bowel cancer when over 50

years of age”), which is statistically nonsensical. To address this problem, all possible

reasons for the negative variance were considered to enable the most appropriate

response. The first consideration was multicollinearity, however the highest correlation

(between items 1 and 2) was .78, which was not high enough to produce this statistical

outcome. Secondly, all items of risk perception were positively correlated, ruling out

any negatively correlated observations within risk perception. Thirdly, the SDs for each

item were checked and were similar in size (.88, .92, .78) with no single item ‘drowning

out’ the other items. Finally, the Mahalanobis distances of observations were inspected

to confirm the absence of outlying cases, and with the furthest three cases having only

small Mahalanobis distances from the previous observations, they were not considered

influential outliers. Without being able to delete the item from risk perception (as two

items are inadequate), the decision was made to impose a measurement error variance

of zero on the error term for item two, which is not ideal but occasionally justifiable

(Byrne, 2001).

The subsequent model achieved reasonable fit according to the chi-square test,

χ2 = 24.94(13), p = .034, Bollen-Stine p = .21 (Mardia’s coefficient = 4.36) (see Figure

G.25, Table G.12). Sample and implied correlations showed good discriminant validity

between risk perception and social support, and from this point on only the values

relating to risk perception are assessed (social support is examined separately). All path

coefficients were significant and there were no concerns about large residual

covariances (all < 1).

416

Figure G.25. Final model of risk perception, correlated with social support.

G.3.2 Congeneric measurement model of self-efficacy

A summary of the three-step procedure to achieve adequate factorability for self-

efficacy is in Table G.11.

Table G.11

Summary of the Steps Taken in the Development of the Self-Efficacy Measurement

Model


1 A congeneric model for self-

efficacy was tested

Inadequate fit resulted. The decision

was made to re-draw the paths to

reflect a three-factor model.

G.3.2.1

2 Re-specification of the

measurement model (CFA) of

self-efficacy


adequate fit as a 3-factor model.

G.3.2.2

417

G.3.2.1 Step 1: Congeneric model of self-efficacy.

Self-efficacy was tested by confirmatory factor analysis (see Figure G.26). The

initial model was a poor fit, χ2 = 322.06(27), p = .000, Bollen-Stine p = .000 (Mardia’s

= 20.44) (see Table G.12). The regression weights showed that the path to item five

(“sedative needed”) was non-significant and that the standardised regression weights

and item reliability for this item were deficient (standardised regression weight) = .14,

and squared multiple correlation = .02). Item five also had a large residual covariance

with item eight (5.28) (“amount of time needed for the test compared to other people”)

and the removal of this item improved the model fit.

Figure G.26. Hypothesised measurement model of self-efficacy with standardised

estimates.

G. 3.2.2 Step 2: Re-specification of the self-efficacy model.

Based on factor loadings, paths were re-specified to reflect a three-factor model of self-

efficacy, with the new latent variables of FOBt self-efficacy, self-efficacy related to

colonoscopy, and self-efficacy related to preparation for colonoscopy (see Figure G.27),

χ2 = 38.49(17), p = .000, Bollen-Stine p = .02 (Mardia’s = 18.49). Table G.12 outlines

the fit indices for the initial and final models of self-efficacy.

418

Figure G.27. Final measurement model of self-efficacy with standardised estimates.

419

Table G.12

Goodness-of-Fit Indices for Cognitive Measurement Models (CFA)

Cognitive Variables χ2/df

a RMSEA


<3.0 <.08

<.10 >.95 >.90 >.90 >.95 Lower

values

Risk

perception

Initialc 1.78 .06 .016, .101 .07 .97 .96 .93 .96 113.26

Self-efficacy Initial 11.93 .23 .211, .256 .12 .64 .72 .54 .51 435.61

Final 2nd order 2.26 .08 .046, .113 .05 .97 .96 .91 .95 158.34

Worry Initial 6.53 .17 .127, .207 .06 .91 .92 .80 .85 134.53

Final 1.55 .05 .000, .159 .02 1.00 .99 .96 .99 53.57

Screening bias

Initial 5.88 .16 .136, .177 .07 .84 .83 .73 .80 332.05

Final 1.87 .07 .000, .130 .03 .99 .98 .95 .98 72.43

Test-efficacy Initial 18.56 .30 .245, .350 .13 .79 .87 .60 .58 155.91

Final 1.18 .03 .000, .147 .03 .10 .99 .97 .10 52.82 aMain criteria used to determine model fit in the present research. bBollen-Stine p-value which is used when non-normality is indicated by a Mardia’s coefficient > 3.0. cInitial model achieved adequate fit.

420

G3.3 Congeneric measurement model of worry

The psychometric properties of Lerman and colleagues’ (1991) Breast Cancer

Worry Scale (BCWS) are difficult to appraise as it has been modified often to meet

requirements for different study objectives. Reviews of its changeable use, mainly in

breast cancer research, suggest that item 2 could be negatively associated with

screening, while items 1, 3, and 4 are positively associated with screening, however this

argument is still speculative. Thus, although it is the most widely used and adapted

measure of cancer worry, the scale is in need of further construct validation (Hay et al.,

2005). The inconsistencies in the use and the validity of the scale, and the application of

it to bowel cancer screening, warranted a confirmatory factor analysis. The CFA

suggested that the final model is best specified with four items, which relate to worry

about future disease, future test results, frequency of concerns, and overall worry. The

items regarding daily activities and mood contributed to a poorly fitting model.

The BCWS has a four-point response format with ordered categorical response

options (Not at all; A little; Somewhat; and A lot). When response categories are

reduced, a bias in Pearson’s R increases because it progressively underestimates the true

correlation of .05 (Cunningham, 2008). Therefore ordered categories below five should

not be treated as continuous, and polychoric correlations should be computed to

circumvent the underestimation of factor loadings from Pearson’s R. Factor loadings

when using polychoric correlations are consistently higher than those produced by

Pearson R, however the fit statistics are invariably poorer (Cunningham, 2008). Using

The SAS System Version 9.1.2, polychoric correlations were computed, entered into a

covariance matrix in Excel, and used in Amos instead of the raw dataset (see Table

G.13). The removal of item three, and then of item two, led to an adequate fit of the

sample data to the implied model, χ2 = 6.54(2), p = .04, Bollen-Stine p = .28 (see Figure

G.28 for the initial model and Figure G.29 for the final model of cancer worry). Fit

statistics for both models are presented in Table G.12.

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Table G.13

Polychoric Correlations for Worry

rowtype_ varname_ worry1 worry2 worry3 worry4 worry5 worry6

cov worry1 1

cov worry2 0.666 1

cov worry3 0.499 0.771 1

cov worry4 0.651 0.608 0.674 1

cov worry5 0.634 0.625 0.613 0.705 1

cov worry6 0.738 0.594 0.546 0.706 0.757 1

Note. Computed in SAS Version 9.1.2

Figure G.28. Hypothesised congeneric model of worry using polychoric correlations.

Figure G.29. Final congeneric model of worry using polychoric correlations.

422

G.3.4 Congeneric measurement model of screening bias

The initial 10-item model (Figure G.30) of biases about bowel cancer screening

showed poor fit, χ2 = 205.89(35), p = .000, Bollen-Stine p = .000. The sample

correlations suggested issues with the unidimensionality of the construct and although

all regression paths were significant, item four (“I would not rely on the results to be

conclusive”) was removed as it had the lowest loading (.59) and reliability (.34). In fact,

inspection of items 2, 4, and 5, which were all the poorest indicators in the model,

seemed to reflect a second factor of fatalistic thinking styles. Therefore, item 2 was

removed (“being diagnosed is like getting a death sentence”), the model re-inspected,

and then item 5 removed (“there is very little one can do to prevent cancer”). At this

stage there was clearly a unidimensional construct and further modifications

(modification indices showed items 1 and 3 might have overlapping content and

therefore item 3 was removed to achieve fit) produced a final, adequate fitting model, χ2

= 9.35(5), p = .10 (Mardia’s coefficient = 4.96, and Bollen-Stine p = .75) (see Figure

G.31 and Table G.12 for fit indices).

Figure G.30. Initial measurement model of screening bias with standardised estimates.

423

Figure G.31. Final modified model of screening bias with standardised estimates.

G.3.5 Congeneric measurement model of test efficacy

Items on the test efficacy measure were a combination of items from two

unstandardised scales used in the health screening literature (Myers et al., 1996; Tiro et

al., 2005). A congeneric model was hypothesised as representing test efficacy (see

Figure G.32). As Mardia’s coefficient (= 10.21) was greater than the advised cut-off of

3.0, it was justified to use Bollen-Stine probability, however the initial model of test

efficacy resulted in a poorly fitting model indicated by both significant Bollen-Stine and

chi-square values. Initial inspection of the standardised regression weights showed that

although all were significant, the least important was Item 1, which also had the lowest

squared multiple correlation (.09), and produced a very high residual error of 7.05 with

Item 2. There was no other error greater than 2.0, and the subsequent removal of Item 1

produced sufficient model fit (see Figure G.33), (χ2 = 2.36(2), p = .31).

Unidimensionality was supported by a single eigenvalue > 1 (eigenvalue = 2.4). A

range of regularly measured fit indices also supported the fit of the model (see Table

G.12).

424

Figure G.33. Final congeneric model of test efficacy with standardised estimates.

G.3.6 Knowledge

There was an important issue associated with constructing a congeneric model

of knowledge in Amos. The knowledge scale used a binary response format (‘correct’

or ‘incorrect’), and there is debate about the use of such scales in structural equation

modelling techniques such as CFA (E. Cunningham, personal communication, January

16, 2009), which assumes a continuous response format. Special techniques are

available for the analysis of latent class (categorical) variables, and Muthén (2001)

describes a procedure for analysing ‘mixture models’, containing both continuous and

categorical latent variables. However this approach cannot be performed using the

Figure G.32. Hypothesised congeneric model of test efficacy with standardised

estimates.

425

modelling program adopted in present analyses, and was beyond the scope of the

present study.

Instead, the most reliable knowledge variable was constructed using reliability in

SPSS (Cronbach’s alpha coefficient), which was improved step-wise by removing three

items that were inhibiting the best possible reliability coefficient for the scale. The three

items that were removed included items 4 (“reason for blood in stool”), 2 (“heart

disease”) and then 3 (“advanced bowel cancer is treatable”), improving alpha from .45

(for the 12 items), to .48, then .51, and finally .53. The nine-item scale was then treated

as a single-indicator variable.

G.4 Measurement Models of Social Factors

G.4.1 Congeneric model of social support for screening

Social support was specified using Maximum Likelihood in Amos 17.0, and the

four-item congeneric achieved satisfactory fit, χ2 = 2.91(2), p = .23 (Figure G.34). There

were no influential outliers, and Mardia’s coefficient was close to the recommended

cut-off of 3.0 (at 3.19). Standardised regression weights indicated that item 4 was the

least important item with a weight of .26. Items 1, 2, and 3 had strong weights ranging

from .44 to .81 respectively, while residual error covariances were not meaningful (all <

2.58). All indices considered important in gauging model fit were in acceptable to

excellent ranges and are presented in Table 7.4, Chapter 7.

G.4.2 Congeneric model of social norms

Subjective norms about screening were analysed in a congeneric model,

suggesting an adequate fit of the data to the estimated model using non-normed Bollen

Stine p values (χ2 = 9.07(2), p = .01, Bollen-Stine p = .10) (see Figure G.35). Mardia’s

coefficient was greater than 3.0 (4.61) and thus Bollen-Stine’s statistic was used to

assess model fit. Sample correlations also indicated a unidimensional variable, while

standardised residual error was not meaningful with all values < 2.58. Standardised

regression weights were all above .7 except for item 3 (“I want to do what my regular

doctor thinks I should do”) which was only .24, and did not have a significant path with

the latent variable (.19, p = .00). Squared multiple correlations also indicated that this

item had only minimal importance (.06). The standard range of indices was also

examined and is presented in Table 7.4, Chapter 7.

426

Figure G.34. Final congeneric model of social support with standardised estimates.

Figure G.35. Congeneric model of subjective norms with standardised estimates.

427

G.5 Measurement models of outcome variables

G.5.1 Measurement model of screening intention

As was the case with risk perception, the three-items of screening intention

could not be analysed in a congeneric model. One constraint was applied to the variance

of the latent variable, screening intention. The output revealed that the variance for the

first indicator was negative (-.12), suggesting an arbitrary solution was produced. An

identical approach as that used for addressing the negative variance in risk perception

was adopted, leading to the same solution: forcing the error term variance to zero on the

problematic item. After ensuring that there was sufficient discriminant validity between

screening intention and test efficacy, output relating only to screening intention was

assessed (see Table G.15 for fit statistics). All path coefficients were significant, and

standardised residual covariances were all low (< 2.58). The final model achieved

reasonable fit, χ2 = 17.75(14), p = .22, Bollen-Stine p = .79 (Mardia’s coefficient =

13.26).

Figure G.36. Congeneric model of the primary outcome variable: screening intention.

G.5.2 Congeneric measurement model of decisional conflict

The decisional conflict scale (DCS) contained five sub-scales, and only the effective

decision subscale had more than three indicators and was analysed separately to begin

428

assessing model fit. A correlated measurement model of the entire scale was then

created in Amos, Version 17. A summary of these steps is presented in Table G.14.

Table G.14

Summary of the Steps Taken in the Development of the DCS Measurement Model


1 Congeneric model for the

Effective subscale of the

DCS

The subscale demonstrated poor fit and

was refined. Re-specification improved

fit.

G.5.2.1

2 Measurement model of

the 5-factors of decisional

conflict

The model showed poor fit, and

adequate fit could not be achieved on

the basis of theoretically pre-specified

factors.

G.5.2.2

3 Exploratory factor

analysis (EFA)

A stable eight-item, one-factor solution

was reached.

G.5.2.3

4 Final CFA of DCS A final five-item CFA achieved

adequate fit after the removal of three

items.

G.5.2.4

G.5.2.1 Step 1: Effective Decision subscale

The initial model of the effective decision subscale of the DCS did not fit the

sample data well according to the chi-square test and a number of goodness-of-fit

indices, χ2 = 39.38(3), p = .000, Bollen-Stine p = .000. Item 1 (“making informed

choices”) showed the largest residual, and smallest portion of explained variance, along

with the lowest factor loading (.77). The deletion of item 1 produced an adequate-fitting

model, χ2 = 15.14(1), p = .000, Bollen-Stine p = .000.

Figure G.37. Hypothesised model of the effective subscale of the DCS.

429

Figure G.38. Final model of the effective subscale of the DCS.

G.5.2.2 Step 2: Measurement model of the decisional conflict subscales

The initial, full correlated model of decisional conflict subscales showed poor

fit, χ2 = 258.43(80), p = .000, Bollen-stine p = .010. Assessment of normality indicated

extreme kurtosis (Mardia’s coefficient = 160.26), however the most distant observations

did not show a large departure from the centroid according to Mahalanobis’ distance.

Goodness-of-fit indices were inadequate for this model, and further inspections of the

model were made for indications of model failure. Standardised residual covariances

were first inspected, and item 11 to item 10 had the larger of the two residuals in the

matrix at 3.43 (with the association between 4 and 2 having the second large residual of

2.86).

Sample moments did not support 5 unique factors being present, and instead

suggested only two factors according to the K-1 rule (eigenvalues > than 1.0). The

standardised regression weights (SRWs) were all significant, although items 10 (“choice

is without pressure”) and 11 (“right amount of support”) were relatively weaker than the

other factor loadings. Squared multiple correlations indicated that item 11 explained the

smallest portion of variance (.19) followed by item 10 (.21). Coupled with its low

standardised regression weight and large residual, it was increasingly evident that item

11 was problematic.

The model was then re-run without item 11, improving fit slightly but not

satisfactorily, χ2 = 211.51(67), p = .000, Bollen-stine p = .010 (Mardia’s = 141.97). Item

10 remained an issue, with a very low portion of explained variance (SMC = .17) and

the lowest factor loading (.42). Fit indices had also not improved sufficiently (see Table

G.15), and therefore item 10 was deleted. The single item remaining on support was not

adequate to represent that sub-scale, and because this subscale had shown considerable

430

overlap in its inter-correlations, its final item was also removed to leave four subscales

on the DCS model. Fit indices improved slightly but again, not adequately, while model

fit did improve, χ2 = 155.68(48), p = .000, Bollen-stine p = .02 (Mardia’s = 119.29).

Further inspection of the model revealed that item 4 had a residual of 2.9,

coupled with the second lowest variance explained, and smallest factor loading (after

item 1). This degree of error was poor, and a final model was run without item 4,

achieving adequate fit, χ2 = 114.39(38), p = .00, Bollen-stine p = .07 (Mardia’s =

105.69). Final goodness-of-fit indices are displayed in Table G.15.

As the final correlated subscales in a full measurement model of decisional

conflict were not an ideal fit (see Figure G.39), and further re-specification of the model

may reduce its interpretability, further analysis became exploratory and the original

scale underwent an exploratory factor analysis in SPSS 17.0.

431

Table G.15

Goodness-of-Fit Indices for Dependent Variable Measurement Models (CFA)


Model χ2/df

a RMSEA


<3.0 <.08 <.10 >.95 >.90 >.90 >.95 Lower values

Screening

Intentionc

Initial 1.27 .04 .000, .082 .05 .99 .97 .95 .99 106.07

Effective

subscale of

DCS

Initial 13.13 .25 .181, .387 .08 .92 .92 .72 .84 85.54

Final 15.14 .26 .158, .390 .05 .96 .95 .72 .87 46.68

Decisional

Conflict

Initial (pre-

EFA) 8.32 .19 .179, .203 .11 .73 .59 .46 .69 1067.32

Pre-EFA best-

fit 3.01 .10 .079, .121 .05 .96 .91 .85 .95 291.03

Post-EFA 7.82 .18 .158, .212 .03 .92 .83 .70 .88 257.25

Final 1.59 .05 .000, .121 .01 .10 .98 .95 .99 71.02 aMain criteria used to determine model fit in the present research. bBollen-Stine p-value which is used when non-normality is indicated by a Mardia’s coefficient > 3.6. cInitial model achieved adequate fit; no modification.

432

Figure G.39. Final attempted 4-factor model of DCS.

G.5.2.3 Step 3: Exploratory factor analysis (EFA) of the decisional conflict scale

According to its Cronbach’s alpha, the reliability for the DCS in the present

study was excellent (α = .94). The corrected item-total correlations were assessed, and

based on a lower limit of .30 (Nunnally & Bernstein, 1994), the lowest item-total

correlation (for item 11, “right amount of support”) at .43, was acceptable. The

factorability of the 16 items of the DCS was examined using SPSS for Mac, Version

17.0. As described during the analysis of the disgust scale, variables comprising an

overarching construct can be examined for factor structure (Preacher & MacCallum,

2003), and established scales can and should be subjected to EFA when necessary (D.

Meyer, personal communication, August 12, 2008). As common variance would be

expected for factors representing a common latent variable, EFA was chosen over

Principal Components Analysis, with Maximum Likelihood (ML) extraction and Direct

Oblimin rotation.

433

Factorability was examined using several standard criteria. An initial solution

showed that item 11 (“right amount of support”) had four correlations with other items

< .3, indicating poor factorability of that item. The Kaiser-Meyer-Olkin (KMO)

measure of sampling adequacy at .91 was well above a recommended cut-off of .6, with

a significant chi-square for Bartlett’s test of sphericity, 2873.64(120), p < .00. Shared

variance amongst the items was checked by their communality values, revealing items

10 and 11 had values < .3 and therefore shared little variance with other items in the

scale. At this stage a further factor analysis was conducted without items 10 and 11.

The second solution for the DCS with just 14 items again indicated a two-factor

structure. The KMO remained high at .91, with a significant Bartlett’s test of sphericity,

2734.97(91), p < .00. Item 1 “decision is easy” and item 5 “know pros of each option”

had a low correlation of .28. Item 1 also had low correlations with a number of other

items in the scale. The pattern matrix was examined for simple factor structure, where

item 2 (“understand steps”) failed to load above .5 on either of the two factors. Item 2

was also shown to have factor loadings across both factor 1 and 2 (.59 and .66),

supporting the removal of this item.

A third solution was produced on the 13 items of the DCS. Item 1 (“decision is

easy”) still indicated poor correlations with other items (with one correlation < .3 and

six other correlations < .4). Item 1 and 3 (“choice is clear”) had the lowest shared

variance with other items, with communalities < .5. Item 3 also indicated cross loadings

on the factors, and only just loaded sufficiently on factor 2 (.51). Items 1 and 3 were

removed from the scale, leaving a fourth solution of the DCS scale (11 items). This

solution indicated one main factor with an eigenvalue of 7.27 and explaining 63.5% of

variance and a smaller factor with an eigenvalue of just 1.38, and explaining 10.5% of

variance. The factor structure indicated that all but 3 of the items loaded > .70 on factor

one, while item 13 (“making informed choice”) showed complex loadings (.77 on factor

one, and .61 on factor two). Factor one appeared to be representing the decision process

(feeling informed and comprehending the choices), while factor two seemed to reflect

post-decision beliefs about sticking with, and feeling satisfied about, the decision. Two

items were deleted due to indications that they were cross-loading on the factors,

including “making an informed choice”, which cross-loaded on factor one and two (.77

and .61, respectively) and “decisions show my priorities”, which also loaded above .6

on both factors (.63 and .82, on factors one and two, respectively).

434

Of the 9 items remaining on the DCS, the scree plot and K-1 rule suggested one

main factor was present, in addition to a smaller factor reflecting a smaller portion of

the variance. To try to capture decisional conflict with the most succinct factor

structure, items were reviewed for their surface structure and item overlap, and their

communalities and correlations with other items. The item “have enough advice” had

the lowest communality, however, “expect to stick with choice”, offered a less unique

item in surface structure as it was deemed to capture a similar post-decision response to,

“am satisfied with choice”. The removal of ‘expect to stick with choice’ led to the final

8-item, unidimensional model of decisional conflict, explaining 69.38% of variance.

Factor loadings and communalities are presented in Table G.16.

G.5.2.4 Step 4: Final confirmatory factor analysis on DCS

To confirm the factor structure of the 8-item DCS, a confirmatory factor analysis was

conducted in Amos, Version 17. The 8-item model produced a poor fit to the implied

model, χ2 = 156.32(20), p = .000, Bollen-Stine p = .000, Mardia’s coefficient = 72.17.

All paths were significant, and standardised regression weights ranged from .55 to .93.

Item reliability ranged from .31 to .86, while standardised residual covariances were all

below < 2.58. The correlated indicators suggested by the modification index, revealed

that items 9 and 16 were satisfactory in the model and no modifications were suggested

for these items. Item 5 appeared in four of the modification suggestions, and the model

was re-run without it, producing an improved fit, Bollen-Stine p = .01. Standardised

residual errors did however increase after this change between item 4 (“aware of

options”) and item 12 (“have enough advice”), and when these items were removed, a

final 5-item model of decisional conflict was retained.

The final 5-item DCS model had adeqaute fit, χ2 = 7.94(5), p = .16, Bollen-Stine

p = .61 (see Figure G.40), reflecting the most accurate model of decisional conflict in

the present dataset. All fit indices were excellent and in the desired ranges (see Table

G.15).

435

Table G.16

Factors Loadings and Communalities on New Factors of Decisional Conflict

Item (description) Factor loading Communality

4 (aware of options) .75 .57

5 (know pros of each option) .89 .79

6 (know cons of each option)* .92 .84

7 (importance of pros clear)* .93 .86

8 (importance of cons clear)* .92 .85

9 (clear about which more important)* .88 .77

12 (have enough advice) .74 .55

16 (am satisfied with choice)* .55 .31

Total variance explained 69.38%

*indicates an item retained subsequent to confirmatory analysis.

N = 202.

Figure G.40. Final congeneric model of Decisional Conflict with standardised

coefficients.

436

APPENDIX G REFERENCES

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Illinois: Smallwaters Corporation.

Bollen, K.A., & Stine, R. (1992). Bootstrapping goodness of fit measures in structural

equation models. Sociological Methods & Research, 21, 205-229.

Byrne, B. M. (2001). Structural equation modeling with Amos: basic concepts,

applications, and programming. Mahwah, N.J: Lawrence Erlbaum Associates.

Consedine, N. S., Krivoshekova, Y. S., & Harris, C. R. (2007). Bodily embarrassment

and judgment concern as separable factors in the measurement of medical

embarrassment: Psychometric development and links to treatment-seeking

outcomes. British Journal of Health Psychology, 12 (3), 439-462.

Cunningham, E. (2008). A practical guide to structural equation modelling using

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Herting, J. R., & Costner, H. L. (2000). Another perspective on "The proper number of

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confirmatory factor analysis: An overview and some recommendations.

Psychological Methods, 14 (1), 6-23.

Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.).

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Muthén, B. (2001). Second-generation structural equation modelling with a

combination of categorical and continuous latent variables. New opportunities

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methods for the analysis of change (pp. 291-322). Washington, D.C: APA.

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McGraw-Hill.

Olatunji, B. O., Williams, N. L., Tolin, D. F., Abramowitz, J. S., Sawchuk, C. N., Lohr,

J. M., & Elwood, L. S. (2007). The Disgust Scale: Item analysis, factor

structure, and suggestions for refinement. Psychological Assessment, 19 (3),

281-297.

Preacher, K. J., & MacCallum, R. C. (2003). Repairing Tom Swift's electric factor

analysis machine. Understanding Statistics, 2 (1), 13-43.

Reise, S. P., Waller, N. G., & Comrey, A. L. (2000). Factor analysis and scale revision.

Psychological Assessment, 12 (3), 287-297.

Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation

modeling (2nd ed.). New Jersey: Lawrence Erlbaum Associates.

SPSS-PASW (Statistics Package for the Social Sciences) (Versions 17.0 and 18.0 for

Mac) [Computer software]. (2009). Chicago, Illinois: SPSS, Inc.

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invariance of a survey measuring psychosocial correlates of colorectal cancer

screening among African Americans and Caucasians. Cancer Epidemiology


438

APPENDIX H

HYPOTHESIS 5 (STUDY 1) MEDIATION ANALYSES: FULL DESCRIPTION

439

Appendix H: Mediation Analyses of Hypotheses 5(b) and 5(c): Study 1

H.1 Hypothesis 5(b) Mediation of fear and screening intention by self-efficacy and

social support

Mediation of fear by self-efficacy. The measurement models of screening fear, self-

efficacy and screening intention were first depicted in a mediation model in Amos

version 18.0 (see Figure H.1). This model (model 1) achieved adequate fit, χ2 =

605.36(243) (CMIN/df = 2.49; RMSEA = .09). A second model (model 2) was then

depicted with the direct link between screening fear and intention removed (see Figure

H.2), indicating adequate fit, χ2 = 613.90(244) (CMIN/df = 2.52; RMSEA =.09). Note

that all the coefficients in model 2 were significant. These two models were then

compared to assess whether the direct path was significant (indicating partial mediation)

or not (indicating the presence of full mediation).

To compare the models, the following formula was entered into Excel for Mac,

version 14.1.0:

=CHIDIST([χ2M1-χ

2 M2],[df M1 – df M2])

Where M1 reflects the values for Chi-Square and degrees of freedom for model 1, and M2

reflects the chi-square and degrees of freedom values for model 2. A non-significant p-

value in Excel would indicate that the direct link is not significant, and there is full

mediation.

With χ2 = 8.54 and df=1, this test indicated that the path from fear to intention

remains significant when self-efficacy is controlled, p < .004 suggesting there is partial

mediation of self-efficacy on the relationship between fear and screening intention.

440

Figure H.1. Model 1 indicating a direct and an indirect link between fear and screening

intention, and hypothesising partial mediation by self-efficacy.

Figure H.2. Model 2 with the direct link between fear and screening intention removed,

hypothesising full mediation by self-efficacy.

441

Mediation of fear by social support. The measurement models of fear, social support,

and intention were depicted in two models, where model 3 includes the direct path

between fear and intention, and model 4 removes this direct path (see Figures H.3 and

H.4, respectively). Model 3 achieved adequate fit, χ2 = 373.83(164) (CMIN/df = 2.28;

RMSEA = .08), as did model 4, χ2 = 378.7(165) (CMIN/df = 2.29; RMSEA = .08).

Note that in model 4, the coefficient from fear to social support was barely significant

(β = -.19 (p = .05) while social support to intention was clearly significant (β = .29, p

< .01).

The difference between the chi-square and degrees of freedom between these

models was tested in Excel, indicating that there was partial mediation of the

relationship between fear and screening intention by social support, (Chi-Square = 4.9,

df = 1, p=.03), where the direct path from fear to intention remained significant.

Figure H.3. Model 3 indicating a direct link between fear and screening intention,

hypothesising partial mediation by social support.

442

Figure H.4. Model 4 with the direct link between fear and screening intention removed,

hypothesising full mediation by social support.

H.2 Hypothesis 5(c) Mediation of medical embarrassment (MEQ) and screening

intention by self-efficacy and social support

Mediation of medical embarrassment by self-efficacy. Self-efficacy was examined to

see whether it mediates the relationship between medical embarrassment and screening

intention. Model 5 (Figure H.5) shows the model with the direct path included,

achieving adequate fit, χ2 = 746.8(341), CMIN/df = 2.19; RMSEA = .08). Model 6

(Figure H.6) shows this model without the direct path from embarrassment to intention,

achieving adequate fit, χ2 = 748.3(342), CMIN/df = 2.19; RMSEA = .08). Note that all

the coefficients in model 6 were significant. Comparing the chi-square values for these

two models, Chi-Square = 1.47, df = 1 and p = .22. This suggests full mediation where

there is no direct relationship of medical embarrassment on screening intention, only an

indirect relationship through self-efficacy.

443

Figure H.5. Model 5 indicating a direct link between embarrassment (MEQ) and

screening intention, hypothesising partial mediation by self-efficacy.

Figure H.6. Model 6 with the direct link between embarrassment (MEQ) and screening

intention removed, hypothesising full mediation by self-efficacy.

444

Mediation of medical embarrassment (MEQ) by social support. The relationship

between embarrassment and screening intention was tested to see whether support

mediates this relationship.

Model 7 (Figure H.7) shows the model with a direct path included from

embarrassment to intention, achieving adequate fit, χ2 = 387.92(246) (CMIN/df = 1.58;

RMSEA = .05). Model 8 (Figure H.8) removes this direct path from embarrassment to

intention, achieving adequate fit, χ2 = 396.1(247) (CMIN/df = .1.60; RMSEA = .05).

Note that the coefficient from MEQ to Support was barely significant (β = -.24, p

= .05). However, Chi-Square = 8.18, df = 1, (p = .004), suggests that social support is a

partial mediator of the relationship between embarrassment and screening intention.

Figure H.7. Model 7 indicating a direct link between embarrassment (MEQ) and

screening intention, hypothesising partial mediation by social support.

445

Figure H.8. Model 8 with the direct link between embarrassment (MEQ) and screening

intention removed, hypothesising full mediation by social support.

446

APPENDIX I

STUDY 1 CORRELATIONS BETWEEN ALL COGNITIVE, EMOTION AND

SOCIAL VARIABLES WITH THE DEPENDENT VARIABLES (POST FACTOR

ANALYSIS)

447Table I.1

Study 1 Correlations Between Cognitive, Emotion and Social Variables with the Dependent Variables (Post-Factor Analysis)

Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 Intention

2 DCS -.21**

Demographic 1.000

3 Gender .05 -.06 1.000

4 Age .21** -.23** .08 1.000

5 Marital .24** -.07 .02 .37** 1.000

6 Screen

history

.36** -.03 .32** .31** .28** 1.000

7 GI Health .13 -.07 .08 .23** .13 .19** 1.000

Emotion

8 FP -.28** .24** .18* -.22** -.13 -.14* -.02 1.000

9 FC -.10 .17* .25** -.21** -.13 -.10 -.08 .59** 1.000

10 FE -.12 .15* .07 -.16* -.08 -.10 -.05 .48** .38** 1.000

11 BE -.24** .19** .18** -.29** -.22** -.14 -.13 .52** .44** .44** 1.000

12 JC -.12 .08 .05 -.20** -.11 -.01 -.01 .34** .32** .57** .54** 1.000

13 IE -.28** .16* .16* -.31** -.11 -.10 -.05 .54** .42** .51** .75** .64** 1.000

14 Bowel -.34** .21** .12 -.33** -.08 -.12 -.11 .52** .40** .46** .59** .49** .73** 1.000

15 Animal -.13 .15* .34** -.25** -.12 .02 -.06 .45** .41** .21** .56** .24** .47** .54** 1.000

16 Contam -.08 .05 .10 -.10 -.00 -.05 -.11 .29** .29** .19** .30** .29** .28** .34** .40** 1.000

17 Core -.09 -.02 .26** -.11 .03 .04 -.02 .37** .29** .29** .46** .40** .47** .58** .60** .38** 1.000

Cognition

18 Bias -.45** .28** -.19** -.19** -.23** -.23** -.17* .26** .11 .34** .26** .34** .25** .26** .09 .13 .08 1.000

19 Risk .18** .04 -.09 -.05 .04 -.04 .17* .12 .15* .18* .17* .10 .10 -.00 -.01 .05 -.02 -.09 1.000

20 SE .52** -.29** -.19** .28** .27** .30** .01 -.56** -.39** -.36** -.45** -.29** -.50** -.55** -.35** -.18* -.27** -.23** .04 1.000

21 TE .26* -.20** .02 .10 .20** .15* -.06 -.01 -.10 -.17* -.01 -.15* -.06 -.06 .07 -.09 .04 -.17* -.04 .28** 1.000

22 Know .24** -.13 .07 .05 .18* .17* -.06 -.12 -.06 -.23** -.12 -.25** -.17* -.20** -.03 -.10 -.16* -.27** .12 .22** .16* 1.000

23 Worry .21** -.03 .00 .03 .06 .02 .11 .23** .27** .25** .21** .28** .15* .17* .12 .26** .14 -.13 .42** -.08 -.06 -.06 1.000

Social

24 Support .36** -.14* .09 .16* .13 .20** .14* -.17* .03 -.14 -.18** -.10 -.17* -.16* -.01 -.04 -.07 -.34** .15* .26** .15* .28** .12 1.00

25 Norms .09 .06 -.04 -.27** -.18** -.06 -.08 .11 .15* .09 .08 .08 .07 .08 -.17* -.00 .02 .16* .10 -.06 .03 .07 .04 .22**

Note. Post-factor analysis correlations. Gender (male = 0; female = 1); Marital (unpartnered = 0; partnered = 1); Screen history for any illness (none = 0; yes = 1); DCS = Decisional

Conflict Scale; FP = fear procedure; FC = fear cancer; FE = fear embarrassment; BE = bodily embarrassment; JC = judgement concern; IE = interpersonal embarrassment; Bowel =

bowel-related disgust; Animal = animal-reminder disgust; Contam = contamination disgust; Core = core disgust; FP= fear of procedural aspects; FC = fear of cancer; FE = fear

embarrassment; Risk = risk perception; SE = self efficacy; TE = test-efficacy; Know = bowel cancer knowledge.


448

APPENDIX J

STUDY 2 ANNOTATED SURVEY QUESTIONNAIRE

Community Screening Attitudes, February-May 2010

449

APPENDIX J: Study 2 Annotated survey Questionnaire

CONSENT INFORMATION STATEMENT

AUSTRALIAN ATTITUDES TOWARD SCREENING FOR BOWEL CANCER

INVESTIGATORS: Victoria Hamilton (PhD candidate) Professor Sue Moore (Coordinating Supervisor)

Thank you for considering this research. We are seeking Australian participants aged 35 or older to take part in a survey about cancer screening attitudes. The purpose of this research is to improve our understanding of Australians’ thoughts and feelings about bowel cancer screening. The opinions you provide will help us to explore some of the reasons behind participation in bowel cancer screening, and add to our knowledge of patient experiences during medical testing.

Participation involves filling in a survey that should take approximately 20 minutes of your personal time to complete, and is entirely anonymous. We will not ask you to provide your name anywhere on the survey. Although there is no risk of foreseeable harm, because of the nature of this survey some people may find certain questions discomforting. You will be asked how you feel in particular medical scenarios, and how you feel about different medical procedures. These are standard scales that are used in medical studies and each one of your anonymous answers is very important, however you may decline to answer questions you deem too intrusive.

Your completion of the survey implies that you have consented to take part, however, your participation is voluntary, and you are entitled to withdraw from participating in this survey at any time. We value your time, and as the ability to carry out this research is due to our volunteer participants, we would like to thank you in advance for sharing your opinion on bowel cancer screening. We hope you find the survey interesting.

PUBLICATION

This research project is being conducted as part of a PhD degree. In the event that conference presentations, media/news pieces, or journal publications arise from this research, no individual’s responses will be identifiable. Any resulting publications will include only group responses. If you would like to learn of the final research outcomes, you can check our Website throughout 2010: www.bowelscreenresearch.com

CONTACT US

If you have any questions about this project, please contact one of the investigators: Victoria Hamilton, Tel: (03) 9214 5553, or email: [email protected], or: Professor Susan Moore, Tel (03) 9214 5694, or email: [email protected].

SUPPORT SERVICES

If participation in this survey raises any issues you would like to discuss with a health professional, please contact the following services (counselling and health services are free to Swinburne students).

� Swinburne Counselling Services, Tel: (03) 9214 8025 (Hawthorn Campus) and (03) 9215 7101 (Lilydale campus)

� Swinburne Health Services, Tel: (03) 9214 8483 (Hawthorn campus) and (03) 9215 7106 (Lilydale campus) � Swinburne Psychology Clinic (a low-cost service), Tel: (03) 9214 8653 (Hawthorn campus)

Should any physical health concerns arise as a result of your participation, we ask that you contact your regular medical professional (e.g. your general practitioner) or Swinburne Health Services (9214 8483) to seek medical advice.

This project has been approved by or on behalf of Swinburne’s Human Research Ethics Committee (SUHREC) in line with the National Statement on Ethical Conduct in Human Research. If you have any concerns or complaints about the conduct of this project, you can contact:

Research Ethics Officer, Swinburne Research (H68) Swinburne University of Technology, PO Box 218, HAWTHORN VIC 3122

Tel (03) 9214 5218 or +61 3 9214 5218 or [email protected]

Please retain this page for your own records

Please return the survey in the pre-paid envelope provided


450

Australian Attitudes Toward Bowel Cancer Screening

DEMOGRAPHIC INFORMATION

1. What is your sex? (Please circle) Male Female

2. What is your Date of Birth? / / (Day/Month/Year)

3. What is your postcode? __________________

4. What is your highest educational level completed?

� Some secondary school

� Completed secondary school

� TAFE or College Diploma / other post-secondary education

� Degree

� Postgraduate Studies

5. What is your marital status?

� Single

� Partnered (married/de facto)

� Divorced/separated

� Widowed

6. What is your occupation?

7. Do you currently have private health insurance (including single, couple or family policies)?

� None

� Hospital Only

� Hospital and Extras

In relation to your own physical health: (Please circle)

Personal health history

8. Have you ever been advised by your doctor or another health professional to get bowel screening? No Yes 9. Do you currently have any serious medical condition(s)? No Yes

10. Do you have, or have you ever had, any of the following gastrointestinal conditions?

a) Crohn’s disease No Yes

b) Coeliac disease No Yes

c) Lactose intolerance No Yes

d) Irritable bowel syndrome (IBS) No Yes

e) Polyps in the rectum or colon No Yes

f) Diverticulitis No Yes

Have you ever done, or had a doctor perform, any of the following? (Please circle)

CRC Screening history

11. Barium Enema (a colon X-ray)? No Yes

12. FOBT (a stool sample test)? No Yes

13. Flexible Sigmoidoscopy (camera inspection of lower colon)? No Yes

14. Colonoscopy (camera inspection of entire colon)? No Yes

FEMALE ONLY RESPONSES (Questions 15 to 18) Female screening history 15. Mammography (breast screening)? No Yes

16. Breast self-examination? No Yes

17. Cervical screening (pap smear)? No Yes

18. Skin cancer checks (by a medical professional)? No Yes


451

MALE ONLY RESPONSES (Questions 19 to 21)

Male screening history

19. Prostate screening? No Yes

20. Testicular self-examination? No Yes

21. Skin cancer checks (by a medical professional)? No Yes

Please tell us about your family health history:

Family CRC / health history

Has anyone in your family (brothers, sisters, parents, children, or others by blood relation):

22. Had cancerous or non-cancerous lesions (polyps) in the rectum or colon? No Yes Don’t Know

23. Had a gastrointestinal infection or disorder (e.g. IBS)? No Yes Don’t Know

24. Please write down how many members of your family, if any, have had bowel cancer:

BRIEF DESCRIPTION OF BOWEL SCREENING TESTS

STOOL TESTING

Faecal Occult Blood Tests (FOB Tests) detect occult (hidden) blood in a stool sample. They are conducted by individuals in one’s own home, and are returned by mail to the health service provider. There are no physical risks.

Two to three separate samples are requested, and these are examined in a laboratory for traces of blood. The presence of blood in the stool usually requires a referral for follow-up colonoscopy, which is performed in a medical clinic.

COLONOSCOPY

Colonoscopies are performed by medical professionals and involve the insertion of a small camera tube into the lower rectum and small intestine, which can extend to the large intestine. They are performed to detect premalignant or malignant lesions (polyps), which can be removed easily during the procedure. As with any invasive procedure there are rare physical risks. For example, one study reported that risk of bowel perforation during colonoscopy is 1.96 for every 1000 colonoscopies performed.

As you continue the survey, please answer with the first response that comes to mind. There are no right or wrong answers

Please indicate what you believe your risk of bowel cancer to be:

Risk perception (Cameron & Diefenbach, 2001; Kremers et al., 2000)

Very Low Chance (far

below average)

Lower Chance

Average Chance

Higher Chance

Very High Chance

(far above average)

The chance that I will get bowel cancer at some point in my life is:

1 2 3 4 5

The chance that I will get bowel cancer compared to most people my age and sex is:

1 2 3 4 5

The chance I will get bowel cancer compared to anyone else is:

1 2 3 4 5

Overall, my future risk of getting bowel cancer is:

1 2 3 4 5


452

Some people have reported the following scenarios as frightening. Please

rate how much these scenarios might cause you to feel afraid.

Fear of screening (Harewood et al.,

2002; Smith, Pope, & Botha, 2005)

How frightening are the following scenarios?

Not at all frightening

Mildly frightening

Quite frightening

Fright-ening

Very frightening

Extremely frightening

Dread just from thinking about getting a colonoscopy

1 2 3 4 5 6

Discomfort associated with colonoscopy 1 2 3 4 5 6

Experiencing pain during a colonoscopy 1 2 3 4 5 6

Risks associated with colonoscopy (e.g. perforation of bowel wall)

1 2 3 4 5 6

The preparation that is required for colonoscopy

1 2 3 4 5 6

Finding out I have cancer with serious and painful symptoms

1 2 3 4 5 6

Finding out that I could have a fatal incurable disease

1 2 3 4 5 6

The thought of having to undergo unpleasant treatment

1 2 3 4 5 6

The thought of having further tests and a possible operation

1 2 3 4 5 6

Being seen as a time-waster, a hypochondriac, or as neurotic

1 2 3 4 5 6

Being seen by others as exaggerating mild or nil symptoms

1 2 3 4 5 6

That my family might think I am concerned about nothing

1 2 3 4 5 6

How I might appear for being concerned about my health, or for seeking help

1 2 3 4 5 6

The following situations are known to cause some people to experience disgust, revulsion, or repugnance. Please rate how much disgust or repugnance you would experience if you were exposed to each situation at this time.

Disgust Emotion Scale (Kleinknecht, 1995)

How much disgust or repugnance would you experience from being exposed directly to:

NO disgust or repugnance at all

MILD disgust or repugnance

CONSIDERA-BLE disgust or repugnance

INTENSE disgust or repugnance

EXTREME disgust or repugnance

A slice of bread with green mould on it

0 1 2 3 4

The smell of a public rest room

0 1 2 3 4

Having blood drawn from your arm

0 1 2 3 4

Observing an amputation operation

0 1 2 3 4

A stray cat 0 1 2 3 4

A glass of spoiled milk 0 1 2 3 4


453







The smell of human faeces 0 1 2 3 4

Discussing the appearance of your stool with your regular doctor

0 1 2 3 4

A snake 0 1 2 3 4

A bottle of your blood 0 1 2 3 4

The mutilated body of a dog that had been run over by your car

0 1 2 3 4

The smell of vomit 0 1 2 3 4

A package of hamburger mince turned green with age

0 1 2 3 4

Swabbing a sample of your stool onto a cardboard slide

0 1 2 3 4

The sight of a large slug 0 1 2 3 4

Receiving a hypodermic injection in the arm

0 1 2 3 4

A dead person unknown to you

0 1 2 3 4

A pile of rotting lettuce 0 1 2 3 4

Seeing stool in a toilet bowl in your home bathroom

0 1 2 3 4

The smell of a city dump 0 1 2 3 4

People injured in an auto accident

0 1 2 3 4

Handling a hypodermic needle

0 1 2 3 4

An old cup of coffee with mould on it

0 1 2 3 4

The sight of a mouse in your house

0 1 2 3 4

Photos of wounded soldiers 0 1 2 3 4

Receiving an anaesthetic injection in the mouth

0 1 2 3 4

A piece of rotting steak 0 1 2 3 4

The smell of body odour 0 1 2 3 4


454







A sewer rat 0 1 2 3 4

A decaying animal on the road

0 1 2 3 4

Handling a container which holds a small swab of your own stool

0 1 2 3 4

The smell of urine 0 1 2 3 4

The sight of a spider 0 1 2 3 4

A small vial of your blood 0 1 2 3 4

Some people have reported that the following scenarios can be uncomfortable or humiliating.

Medical Embarrassment Questionnaire (Consedine, Krivoshekova, & Harris, 2007)

Please rate how true this would be for you in the situations described below:

Not at all / Never

Not often / seldom

Sometimes Most of the time / Often

Very much / Always

Showing my body to a stranger, even to a doctor, is humiliating

1 2 3 4 5

It is embarrassing for me when a doctor or a nurse has to touch me

1 2 3 4 5

Describing my bowel movements to a doctor is awkward for me

1 2 3 4 5

I feel I must have done something wrong when I am ill

1 2 3 4 5

It is embarrassing for me when a doctor examines my body

1 2 3 4 5

When a doctor describes some medical options and I don’t understand, I feel humiliated

1 2 3 4 5

I avoid going to the doctor because I often wait too long and feel awkward knowing that I should have gone sooner

1 2 3 4 5

Talking with a doctor about how frequently I use the bathroom and the nature of my faeces or stool is difficult for me

1 2 3 4 5

Seeing my body during medical examinations makes me feel silly

1 2 3 4 5


455

Please rate how true this would be for you in the situations described below:

Not at all / Never

Not often / seldom

Sometimes Most of the time / Often

Very much / Always

It is embarrassing for me when a doctor who is not of my sex touches my sexual and reproductive organs or rectum during examination

1 2 3 4 5

The thought that a doctor might ask for stool or urine samples is humiliating for me

1 2 3 4 5

I worry that other people will judge me when I’m sick

1 2 3 4 5

I feel shy showing my body to doctors

1 2 3 4 5

I worry about what doctors are thinking when they examine my genitals

1 2 3 4 5

Answering questions about my bodily fluids (e.g. describing the colour of my mucous) makes me feel self-conscious

1 2 3 4 5

I worry that doctors will think I’m silly if I come in with a minor complaint

1 2 3 4 5

I fear that the doctor will think badly of me because my own behaviours probably contributed to my health problems

1 2 3 4 5

Social support (Kremers et al., 2000)

Please indicate your agreement or disagreement with the following statements

Strongly disagree

Mildly disagree

Neither agree or disagree

Mildly agree

Strongly agree

I believe people in my social network would suggest I get screened

1 2 3 4 5

I have someone in my social network who could accompany me to a colonoscopy

1 2 3 4 5

I believe people in my social network would understand my feelings about bowel screening

1 2 3 4 5

I know someone who has been screened for bowel cancer

1 2 3 4 5

Screening Bias (Busch, 2003; Harewood et al., 2002; Hynam et al., 1995)

Please rate how much the following reasons would make you LESS LIKELY to screen for bowel cancer

Strongly disagree

Mildly disagree


Mildly agree

Strongly agree

If I felt too healthy to have bowel cancer 1 2 3 4 5

Being screened is not quite so important if feeling healthy

1 2 3 4 5

If I have no family history of bowel cancer 1 2 3 4 5

If I don’t have any symptoms of bowel cancer 1 2 3 4 5

Because my gender lowers my risk of bowel cancer 1 2 3 4 5

Because other important health risks run in my family which make me less concerned about bowel cancer

1 2 3 4 5


456

How effective do you think bowel cancer screening tests are? Test-Efficacy (Myers et al., 1995; Tiro et al., 2005)

Strongly disagree

Mildly disagree


Mildly agree

Strongly agree

Overall, the results of stool tests are accurate

1 2 3 4 5

Colonoscopy effectively detects bowel cancer 1 2 3 4 5

Overall, the results of colonoscopy are accurate

1 2 3 4 5

When colorectal polyps are found and removed during screening, cancer can be prevented

1 2 3 4 5

I believe that bowel screening is an effective way to find bowel cancer early

1 2 3 4 5

I believe that I can protect myself from bowel cancer by going through bowel cancer screening

1 2 3 4 5

Knowledge (Radosevich et al., 2004)

Please respond to the following statements by circling T for True, F for False, or N for Not sure

True False Not Sure

Most people diagnosed as having advanced bowel cancer will die of something else instead

T F N

People are more likely to die of bowel cancer than of heart disease T F N

Most people diagnosed as having advanced bowel cancer can be treated and fully recover from surgery and chemotherapy

T F N

Bowel cancer is not the most common cause of blood in the stool T F N

Bowel cancer never actually causes problems with bowel habits T F N

Bowel cancer is one of the most common cancers in older adults T F N

The stool test (which detects blood in stool) will pick up all bowel cancers

T F N

A colonoscopy can tell you with more certainty than a stool test whether you have bowel cancer

T F N

Abdominal cramping is one possible symptom of bowel cancer T F N

There will always be a symptom that warns us of the presence of bowel cancer

T F N

Mostly it is males aged 50 and older who should be screened using a stool test every year

T F N

If detected early by a screening test, most bowel cancer patients can recover fully

T F N

457

Worry (Lerman et al., 1991) Please indicate how much you think or worry about bowel cancer:

Not at all Rarely Sometimes Often Almost all the time

During the past month, how often have you thought about your own chances of developing bowel cancer?

1 2 3 4 5

During the past month, how often have you worried about getting bowel cancer?

1 2 3 4 5

How often are you concerned about the results of future bowel cancer screening tests?

1 2 3 4 5

Overall, how much do you worry about getting bowel cancer some day?

1 2 3 4 5

Please think about your intentions to screen for bowel cancer:

Screening intentions (Ajzen, 2004; Emmons et al., 2008; McCaffery et al., 2003)

Definitely not

Probably not

Un-decided

Probably yes

Definitely yes

Would you want to be screened for bowel cancer?

1 2 3 4 5

Would you want to be screened using a stool kit?

1 2 3 4 5

Do you intend to get screened for bowel cancer by stool testing?

1 2 3 4 5

Would you want to be screened by colonoscopy?

1 2 3 4 5

Do you intend to get screened for bowel cancer by colonoscopy?

1 2 3 4 5

How difficult is your decision about bowel cancer screening?

Decisional conflict (O’Connor, 1995)

Considering your intentions (above) about screening:

Strongly disagree

Disagree Neither agree or disagree

Agree Strongly agree

The decision is easy for me to make 1 2 3 4 5

I am clear about the best choice for me 1 2 3 4 5

I feel sure about my decision 1 2 3 4 5

I feel I have made an informed choice 1 2 3 4 5

My decision shows what is important to me

1 2 3 4 5

I expect to stick with my decision 1 2 3 4 5

I am satisfied with my decision 1 2 3 4 5

I know which options are available to me 1 2 3 4 5

I feel I know the benefits of each option 1 2 3 4 5

I feel I know the risks and side effects of each option

1 2 3 4 5


458

These final questions relate to how you might cope leading up to and during bowel screening. Please circle the number representing the best description of your feelings:

Self-Efficacy (Endoscopy confidence questionnaire) (Gattuso et al., 1992)

1. How confident are you that you would attend a colonoscopy examination if referred?

1 2 3 4 5 6 7 Not at all confident Somewhat confident Extremely confident 2. How confident are you that you could continue with the preparation and the ensuing colonoscopy?

1 2 3 4 5 6 7 Not at all confident Somewhat confident Extremely confident 3. How well do you think you could relax your body during the examination (if not under general anaesthetic)?

1 2 3 4 5 6 7 Not at all confident Somewhat confident Extremely confident 4. Overall, how confident are you that you could get a colonoscopy without any difficulty?

1 2 3 4 5 6 7 Not at all confident Somewhat confident Extremely confident 5. Overall, how confident are you that you could complete a stool test without any difficulty?

1 2 3 4 5 6 7 Not at all confident Somewhat confident Extremely confident 6. How comfortable do you think you would be during the colonoscopy examination?

1 2 3 4 5 6 7 Not at all comfortable Somewhat comfortable Extremely comfortable 7. How much medication (sedative) do you think you would need to relax during a colonoscopy?

1 2 3 4 5 6 7 A great deal of medication Some medication No medication

8. Compared to the average person, how much time do you think your stool test would take?

1 2 3 4 5 6 7 Less time than usual About the same as usual More time than usual 9. How easily do you think you would follow the instructions of a stool test at home?

1 2 3 4 5 6 7 Not at all easily Somewhat easily Extremely easily

10. Please indicate for us whether or not you experienced discomfort in relation to the nature of this survey:

1 2 3 4 5 6 7

No discomfort Some discomfort Extreme discomfort


459

Please detach this page and retain for your records

Thank you once again for taking part in this research. We appreciate

your time and your opinions.

If filling in this survey has raised any physical health concerns,

please contact your GP

If it has raised any emotional issues, please contact:

Swinburne Counselling on: (03) 9214 8025 (Hawthorn); (03)

9215 7101 (Lilydale)

The Swinburne Psychology Clinic (a low cost service) on: (03)

9214 8653

You can also locate a psychologist or counsellor at the

Australian Psychological Society:

www.psychology.org.au/FindaPsychologist/

Further information on Bowel Cancer and Support Groups:

National Bowel Cancer Screening Program:

www.cancerscreening.gov.au/internet/screening/publishing.n

sf

Cancer Council Victoria: www.cancervic.org.au/

The Bowel Cancer & Digestive Research Institute of Australia:

www.bowelcanceraustralia.org

and: www.itscrunchtime.org

Gastroenterological Society of Australia: www.gesa.org.au/

Lets Beat Bowel Cancer: www.letsbeatbowelcancer.com

InSure (Bowel Screening Kits): http://enterix.com.au

Cancer Voices: www.cancervoicesaustralia.org.au

See our website for more information on this study: www.bowelscreenresearch.com

460

APPENDIX K

LIST OF RECRUITMENT SOURCES AND PROMOTIONAL MATERIALS

461

Appendix K: List of Recruitment Sources

Study 1:

- Research Experience Program at Swinburne University of Technology

- Swinburne psychology tutorials and lectures at Hawthorn and Lilydale

Campuses, Melbourne, Victoria, Australia.

- Word of mouth (emails via peers, friends, and family)

Study 2:

- Swinburne advertisements: Faculty of Life and Social Sciences research

participation site, staff Internet bulletins, and Swinburne newsletter (delivered

locally in Hawthorn, Melbourne).

- Targeted male-dominant audiences through peers and family (Qantas annual

training pilot’s meeting in Melbourne, 17 February 2010; Australia Post

engineering department in Brisbane, April 2010)

- Word of mouth via email survey invitations

- U3A print newsletter (October-November 2009 issue), and posters on U3A

noticeboards across Melbourne metro and Geelong campuses.

- Swinburne Press release (with subsequent media coverage in The Sydney

Morning Herald, Science Alert, and MedIndia: Networking for Health)

In addition to advertising via poster and calling cards, a Website was also developed to

provide a user-friendly interface containing basic information about the project. It also

acted to minimise loss of participants due to the potential difficulty in correctly typing

the Opinio Website address, which contained a sizeable URL address:

(http://opinio.online.swin.edu.au/admin/surveyAdmin.do?action=viewSurveyAdmin&s

urveyId=5858). The Website, “www.bowelscreenresearch.com” (see following pages

for screen shots), contained links for accessing the survey and for forwarding it to

friends; media and press releases; contact information about the researchers, and links to

further information on bowel cancer.

462

APPENDIX K: Study 2 website and poster

Homepage:

Note. Snapshot of Webpage taken on 07.07.2009

463

‘About the Project’ page:

Note. Snapshot of Webpage taken on 07.07.2009

464

Study 2 poster

465

APPENDIX L

STUDY 2 CORRELATION MATRIX OF ALL COGNITIVE, SOCIAL, EMOTIVE,

AND DEPENDENT VARIABLES IN THE COMMUNITY SAMPLE

466

Table L.1

Correlation Matrix of Cognitive, Social, Emotive and Dependent Variables in the Community Sample (Study 2)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

1 Decisional

Conflict

1.000

2 FOBt intent -.25** 1.000

3 CS intent -.39** .17** 1.000

4 Participation -.28** .21** .32** 1.000

5 Risk -.15* .06 .38** .26** 1.000

6 Knowledge -.15* .14* .10 .06 .12 1.000

7 Test-efficacy -.31** .27** .32** .16* .16* .18** 1.000

8 Self-efficacy -.39** .34** .47** .18** .06 .20** .29** 1.000

9 Cancer worry -.14* .11 .30** .24** .41** .07 .12 .04 1.000

10 Bias .40** -.24** -.38** -.27** -.19** -.13* -.12 -.43** -.17** 1.000

11 Support -.22** .13* .31** .18** .15* .18** .21** .27** .02 -.36** 1.000

12 Fear procedure .30** -.16* -.33** -.10 -.10 -.11 -.22** -.57** .15* .27** -.22** 1.000

13 Fear of cancer .13 -.04 -.12 .10 .10 .01 .06 -.27** .22** .13* -.05 .45** 1.000

14 Fear emb .06 -.07 -.12 -.04 .10 -.04 -.03 -.24** .24** .10 -.13* .32** .28** 1.000

15 Food disgust .06 -.06 .05 .03 .05 .02 .01 -.10 .26** .04 -.10 .25** .25** .17** 1.000

16 Blood draw .13* -.06 -.07 -.06 .06 -.13* -.06 -.20** .23** .11 -.12 .33** .34** .13* .29** 1.000

17 Odour .14* -.10 -.11 -.04 -.06 -.13* .04 -.25** .19** .13* -.16* .33** .34** .27** .63** .41** 1.000

18 Animal .06 -.11 -.03 .03 .04 -.07 .08 -.19** .21** .09 -.07 .31** .37** .33** .53** .45** .63** 1.000

19 Injury .11 -.07 -.07 .02 .04 -.02 .06 -.21** .17** .07 -.05 .33** .34** .16* .31** .49** .58** .53** 1.000

20 Bowel .25** -.31** -.24** -.08 .00 -.07 -.11 -.50** .12 .26** -.22** .47** .29** .39** .43** .39** .65** .51** .45** 1.000

21 Bodily emb .21** -.25** -.29** -.12 .03 -.09 -.21** -.51** .10 .23** -.17** .55** .36** .43** .24** .28** .39** .36** .42** .56** 1.000

22 Judge concern .17** -.15* -.14* .01 .13* -.04 -.13 -.33** .24** .24** -.18** .48** .36** .48** .29** .34** .34** .40** .32** .40** .62** 1.000

23 Interperson emb .26** -.31** -.25** -.14* .05 -.05 -.18** -.51** .11 .31** -.18** .54** .34** .49** .33** .32** .44** .41** .40** .69** .85** .67**

Note. Participation = CRC screening participation; risk = risk perception; bias = screening bias; emb = embarrassment; blood draw, odour, animal, injury, and bowel = disgust sub-

types.

N = 240; Two-tailed significance * p < .05. **p < .01.

467

APPENDIX M

STUDY 2 MEANS, STANDARD DEVIATIONS AND F-STATISTICS FOR THE

DISCRIMINANT FUNCTION ANALYSIS PREDICTORS

468

Table M

Means and Standard Deviations for the Discriminant Function Analysis Predictors (N=240)

Decisional conflict FOB test intention Colonoscopy intention Screening participation

Low High No Yes No Yes No Yes

Age 62.22 (10.45) 56.63 (12.50) 57.40 (12.44) 60.56 (11.36) 59.15 (12.23) 59.76 (11.46) 54.85 (12.48) 62.29 (10.47)

Gender .66 (.48) .68 (.47) .70 (.46) .65 (.48) .70 (.46) .64 (.48) .66 (.48) .67 (.47)

Screening advice .55 (.50) .25 (.43) .39 (.49) .41 (.49) .26 (.44) .53 (.50) .10 (.30) .58 (.49)

Family CRC history .41 (.49) .24 (.43) .31 (.47) .33 (.47) .21 (.41) .43 (.50) .25 (.44) .37 (.48)

Gastrointestinal condition .37 (.48) .25 (.44) .31 (.47) .31 (.46) .24 (.43) .38 (.49) .12 (.33) .43 (.50)

Risk perception 11.49 (3.83) 10.37 (3.52) 10.79 (4.00) 11.02 (3.57) 9.76 (3.44) 12.05 (3.64) 9.72 (3.43) 11.68 (3.71)

Cancer knowledge 6.80 (2.81) 5.96 (2.73) 6.13 (2.95) 6.52 (2.72) 6.26 (3.06) 6.51 (2.54) 6.18 (3.00) 6.52 (2.67)

Test-efficacy 25.52 (4.24) 23.00 (3.43) 23.23 (4.18) 24.83 (3.89) 23.39 (4.02) 25.11 (3.93) 23.47 (4.34) 24.77 (3.81)

Self-efficacy 46.98 (8.53) 39.36 (9.65) 40.01 (11.36) 44.94 (8.51) 39.66 (10.83) 46.58 (7.46) 40.99 (10.73) 44.61 (9.04)

Cancer worry 8.51 (3.61) 7.54 (3.31) 7.40 (3.29) 8.37 (3.56) 7.03 (3.14) 8.97 (3.56) 6.98 (3.21) 8.68 (3.51)

Screening bias 11.99 (6.62) 17.49 (6.10) 16.28 (7.03) 13.86 (6.73) 16.84 (6.92) 12.69 (6.32) 17.12 (6.39) 13.21 (6.83)

Social support 16.14 (3.70) 14.51 (3.58) 14.85 (3.49) 15.59 (3.83) 14.42 (3.85) 16.19 (3.40) 14.46 (3.79) 15.87 (3.60)

Fear of procedure 8.82 (3.19) 11.76 (5.87) 10.82 (5.49) 9.97 (4.58) 11.45 (5.48) 9.16 (4.05) 10.89 (5.51) 9.88 (4.50)

Fear of cancer 14.62 (4.93) 15.91 (5.32) 15.73 (5.25) 15.01 (5.10) 15.76 (4.99) 14.79 (5.29) 14.46 (3.79) 15.66 (5.09)

Fear of embarrassment 6.21 (4.37) 6.71 (4.43) 6.77 (4.78) 6.29 (4.19) 6.79 (4.76) 6.14 (4.02) 6.67 (5.12) 6.32 (3.90)

Blood draw disgust 8.17 (2.69) 8.91 (5.32) 8.65 (3.01) 8.48 (2.84) 8.81 (2.88) 8.28 (2.90) 8.77 (2.89) 8.40 (2.90)

Odour disgust 13.93 (3.78) 15.14 (4.67) 14.77 (4.53) 14.39 (4.14) 14.78 (4.40) 14.28 (4.16) 14.72 (4.57) 14.40 (4.09)

Bowel disgust 6.21 (2.27) 7.63 (3.18) 7.79 (3.25) 6.45 (2.48) 7.47 (4.40) 6.39 (2.46) 7.19 (3.00) 6.74 (2.73)

Bodily embarrassment 10.47 (5.09) 12.88 (6.30) 13.16 (6.69) 10.87 (5.17) 12.72 (6.27) 10.66 (5.22) 12.55 (6.41) 11.11 (5.40)

Judgement concern 8.11 (3.13) 9.41 (4.41) 9.40 (4.47) 8.41 (3.46) 9.02 (3.88) 8.51 (3.84) 8.70 (3.89) 8.78 (3.85)

Interpersonal embarrassment 7.28 (3.42) 9.58 (5.07) 9.90 (5.27) 7.62 (3.74) 9.21 (4.74) 7.66 (4.04) 9.18 (5.04) 7.94 (4.00)

Decisional conflict 14.57 (8.83) 9.01 (7.65)

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