Demands, control, supportive relationships and well-being amongst British mental health workers
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Transcript of Demands, control, supportive relationships and well-being amongst British mental health workers
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ORIGINAL PAPER
Demands, control, supportive relationships and well-beingamongst British mental health workers
Stephen Wood • Chris Stride • Kate Threapleton •
Elizabeth Wearn • Fiona Nolan • David Osborn •
Moli Paul • Sonia Johnson
Received: 26 October 2009 / Accepted: 23 June 2010 / Published online: 16 July 2010
� Springer-Verlag 2010
Abstract
Purpose Staff well-being is considered to be a potential
problem within mental health occupations, and its vari-
ability is in need of investigation. Our starting point is to
assess the role of demands, control and supportive rela-
tionships that are at the core of Karasek’s model. The study
aims to assess the relationship amongst mental health
workers of job demands, control and support (from peers
and superiors) with multiple measures of well-being.
Method Data were obtained through a self-completion
questionnaire from mental health staff in 100 inpatient
wards, 18 crisis resolution/home treatment teams and 18
community mental health teams. The data was analysed
using multilevel regression analysis.
Results Job demands (negatively), control (positively)
and supportive relationships (positively) are each uniquely
associated with the five measures of well-being included in
the study: namely intrinsic satisfaction, anxiety, depres-
sion, emotional exhaustion and personal accomplishment.
Non-linear and interaction effects involving these
demands, control and supportive relationships are found,
but vary in type and strength across well-being measures.
Conclusions The combination of low levels of demands
and high levels of control and supportive relationships is
good for the well-being of mental health staff. Our results
suggest that management initiatives in mental health ser-
vices should be targeted at creating this combination within
the working environment, and particularly at increasing
levels of job control.
Keywords Well-being � Job satisfaction �Work demands � Job control � Karasek
Background
Staff well-being has figured prominently in the discussion
of the effectiveness of mental health services. It is widely
accepted that the well-being of staff and their associated
morale are vital for both ensuring a reliable and cost-
effective service and reaping benefits from investments in
training, new management methods, innovative service
models, and initiatives intended to improve quality and
safety. Problems within mental health services such
as safety issues, inadequate resources, large caseloads,
excessive administrative work and constraints on imple-
menting novel therapies, with the attendant overreliance
on medication, are seen as sources of low morale and
high stress levels [11, 23–25]. Equally, low levels of staff
S. Wood (&)
School of Management, University of Leicester,
Leicester LE1 7RH, UK
e-mail: [email protected]
C. Stride
Institute of Work Psychology, University of Sheffield,
Sheffield S10 2TN, UK
K. Threapleton
Division of Rehabilitation and Ageing,
University of Nottingham, Nottingham NG7 2UH, UK
E. Wearn � D. Osborn � S. Johnson
Research Department of Mental Health Sciences,
University College London, London W1W 7EJ, UK
F. Nolan
Centre for Outcomes Research and Effectiveness,
University College London, London WC1E 7HB, UK
M. Paul
Health Sciences Research Institute, University of Warwick,
Coventry CV4 7AL, UK
123
Soc Psychiatry Psychiatr Epidemiol (2011) 46:1055–1068
DOI 10.1007/s00127-010-0263-6
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well-being and satisfaction are often implicated in specific
problems facing mental health inpatient services in the
British National Health Service (NHS), such as insufficient
and limited patient–staff contact, negative experiences of
the hospital environment, worries about safety among
inpatients, and high rates of labour turnover, vacancy and
sickness [7, 21]. Given such diagnoses, investment in
teams and staff were at the heart of the government’s
modernisation agenda to improve mental health services
and the quality of care during the past decade [5, 6].
Against this background, the National Institute of Health
Research Service Delivery and Organisation (SDO) pro-
gramme funded a systematic review of literature by Cahill
et al. [2] on staff morale within inpatient mental health
units. This concluded that, on the basis of the research so
far, it was not possible to give an accurate picture of staff
morale. Burnout, job satisfaction and overall psychological
well-being were the principal measures of morale used in
the studies and the available evidence suggested that job
satisfaction was high and burnout at moderate levels. The
studies often had very small samples, and many were in
single sites. It did not appear from Cahill et al.’s review [2]
that any diversity in the results was systematically related
to the measures used; the overall conclusion was that high-
quality research on staff morale and well-being in mental
health was lacking in this area, and the evidence base on
the antecedents of well-being was weak. Although the
report centred on inpatient services, these arguments apply
equally to other mental health services, such as community
mental health teams where there has been less research.
In light of this, the SDO commissioned a research pro-
ject to investigate a range of factors that might impact upon
well-being using a variety of measures applicable to all
types of mental health service provision. This paper reports
the part of this study examining the effect of three key job
or organisational characteristics: the influence of the degree
of control individual staff members have in their work, the
extent of the demands placed on them, and the degree of
support they receive to do their job. This triad has been at
the centre of well-being theory within work psychology for
the past two decades, particularly following Karasek’s
demand and control model of work-related stress [15, 16],
and extensions of it that include support from immediate
line managers or colleagues.
Whilst several recent research papers have suggested
that portrayals of a crisis in staff morale in Britain may be
exaggerated [1], they continue to show that there is con-
siderable variation in staff well-being and thus we need to
investigate what explains this variability. The trinity of
demands, control and support provides a good starting
point for an assessment of the factors affecting staff well-
being amongst mental health workers. Some of the factors
commonly associated with mental health settings that have
been linked to low staff well-being—such as excessive
administrative duties, shift working, high labour turnover,
acutely ill and uncooperative patients, violent incidents and
drug use on the wards, high patient turnover, and con-
straints on creating an adequate therapeutic environment—
may be subsumed under the more general factors, e.g. high
demands, low control or managerial support. We can then
assess the relevance of constructs that cannot be subsumed
under these more generic headings by estimating the
additional explanatory power they add relative to Karasek’s
triad of concepts. Similarly, the Karasek model can be used as
a benchmark for assessing the role of other non-mental
health-specific factors, including recent managerial initia-
tives such as the introduction of appraisal or mentoring
systems, or more longstanding ones such as training and
development. This paper thus presents the first study that
systematically tests the applicability of Karasek’s model to
a range of mental health staff (though Tummers et al. [28]
compared mental health nurses with general nurses in the
Netherlands using the Karasek framework).
Research studies, both prior and subsequent to the Cahill
et al. report, have infrequently included one or more of
support, demands and control, but the range of antecedents
to well-being explored has remained wide, encompassing a
mixture of mental health-specific measures (e.g. the threat
of violence from acutely ill patients), and general factors
(e.g. good pay, task clarity). Cahill et al.’s study [2, p. 58]
concluded that it was not possible to determine which
factors are most likely to increase levels of satisfaction and
morale of mental health workers on the basis of the existing
studies of inpatient care, though workloads, job charac-
teristics and social support dominated the lists of potential
determinants suggested by the evidence so far.
Reid et al. [25], in one of the few qualitative studies
comparing community and ward staff, indicated that lack of
autonomy was a source of dissatisfaction amongst ward staff,
whereas the excessive demands associated with client care
were more significant for community staff. Edwards et al.
[9], reviewing evidence from outside the UK, included high
autonomy (in decision-making), co-worker support and
good supervision (which we can take to mean supportive
management) amongst significant factors explaining low
levels of stress. Demands or workload, autonomy and col-
league support were also included in Michie and Williams’
[20] review of morale amongst health workers.
Theory
Support, control and demands
Karasek’s [15] theory is centred on the notion that psy-
chological strain results from the demands of a work
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situation and the range of decision-making freedom avail-
able to the worker facing those demands. His model thus
identifies job characteristics as the principal source of dis-
tress in the workplace, since it proposes that psychological
strain is caused by the combination of high job demands and
low job control (for this reason it is also called the
demands–control model). The underlying rationale of the
model is that workers experience distress when this com-
bination of circumstances exists because they are prevented
from formulating effective responses to deal with the
challenges of the job. Conversely, low demands and high
control are associated with high levels of well-being.
The importance of supportive relationships in organisa-
tions is a recurring theme in work psychology; the
increasing salience given to bullying illustrates the renewed
concern about extreme forms of non-supportive behaviour.
Payne [22, see also 14 and 18], in particular added the
concept of support to the demand and control model, and
suggested that support, particularly when interpersonal,
could reduce the level of adaptive energy needed to cope
with high demands under conditions of low control.
Karasek (along with Theorell [17, pp. 68–76]) incorporated
such thinking into his model so that social support buffers or
protects the individual against the worse effects of strain;
hence, social support and decision latitude buffer the
adverse effects of high job demands. Such social support
manifests itself in a variety of ways. For example, it may
help people manage their feelings better so they resolve
problems more easily, or offer motivation so that they are
reassured that extra effort or persistence with a problem will
pay off [31]. It may also contribute positively to role clarity.
This demand–control–support model for well-being can
be formulated in two ways: initially in an additive form,
and then extended to an interactive form. The additive form
states that high demands, low control and low social sup-
port each cause psychological strain: i.e. the unique
(independent) effects of each of the three constructs have a
significant impact upon strain. For example, supportive
management or colleagues have a beneficial effect irre-
spective of the whether people are facing stressful demands
or have limited control.
The interactive form of the demand–control–support
model further predicts that control and social support
buffer the negative impact of high demands on well-being
(i.e. they interact with demands to reduce its negative
impact). Under both the additive or interactive forms of the
model, we would expect that psychological strain will be
greatest given the combination of high demands with low
control and low social support (in the demand–control–
support model), or conversely that well-being will be
greatest when employees have low demands, high control
and high support. The Karasek model can therefore be
tested in two ways: initially by examining whether job
demands, job controls and support independently predict
well-being, and then by assessing whether there is a further
interactive (i.e. multiplicative) relationship between them.
A further consideration when probing this model is the
possibility of non-linear effects of job demands or job
control upon well-being. For instance, just as high demands
may be overwhelming, or ‘‘toxic’’ to use Warr’s [31,
p. 181] word, low demands may also be so unchallenging
as to create feelings of frustration and monotony. Likewise,
just as high control may prove beneficial to well-being,
very low control may act positively in freeing employees
from a sense of responsibility. And, though increasing
support initially benefits well-being, there may come a
point where extra amounts of support offer no further
benefit.
A review by de Lange et al. [4] of research testing the
Karasek model revealed that just below half of the pub-
lished studies provide support for the additive version, and
that the corroboration does not vary with the quality of the
study. It also concluded that additional interactive rela-
tionships are rare. Another review confined to the longi-
tudinal studies, Van der Doef and Maes [29] showed there
was considerable support from these for the additive
model, but again—partly because it is infrequently tested,
and no doubt also often under-powered and subject to a
greater debilitating effect of measurement error—less
convincing evidence for the interactive model. These
reviewers concluded that the use of specific measures of
control that correspond directly to the demands in the jobs
being studied is more likely to yield results that support the
buffering role of control. Subsequent studies with more
focused measures have also failed to find strong interaction
affects, and the purely additive model looks the more
robust [31, p. 205]. Nonetheless, a general survey of
employees in Britain using a two-item measure of demands
did offer support for the interactive demands and control
model of job satisfaction and contentment–anxiety [33].
The concept of well-being
Previous investigations of Karasek’s model have tended to
focus on just one or two measures of well-being, and the
range of different measures applied across studies is quite
broad. The review of such work as of the late 1990s by Van
der Doef and Maes [29] showed that job satisfaction and, to
a lesser extent, depression and anxiety have been the most
widely used well-being outcomes.
In work psychology well-being is a multi-dimensional
construct. Warr [30, 31] conceptualises job-related
well-being in terms of three dimensions: dissatisfaction to
satisfaction, anxiety to contentment (or comfort), and
depression to enthusiasm. His model of well-being is based
on what is known as the circumplex model of affect [27],
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that describes it in terms of two orthogonal dimensions of
pleasure and arousal, derived from the more general
models of emotions of Watson and Tellegren [32] and
others [for instance 10, 26]. Pleasure relates to emotional
feelings about whether one is feeling good or bad about
one’s job or aspects of it. As such, it is independent of
arousal, since arousal may provoke positive or negative
feelings. Mental arousal ranges from activation to deacti-
vation and includes varying states, from feeling alert to
sluggish, calm to tense, contented to anxious, depressed to
enthusiastic.
Positive ends of the continuum in both the anxiety–
contentment and depression–enthusiasm dimensions are
identified by a state of high pleasure or positive affect. But
their negative ends are differentially related to arousal.
Anxiety entails high arousal and depression entails low
arousal. The traditional emphasis on job satisfaction mea-
sures only the pleasure dimension, the extent of pleasure
that one gains from one’s job.
Equally, Maslach’s [19] concept of burnout has been
used in several tests of the Karasek model—for example, in
a sample of social workers and construction workers—and
much of the research on mental health staff [1]. It is seen as
especially applicable to the caring professions [19]. The
overall term refers to the experience of long-term exhaus-
tion and diminished interest, but Maslach identified three
dimensions. The core factor is that of emotional exhaus-
tion, which ‘‘refers to feelings of being emotionally over-
extended and depleted of one’s emotional resources’’ [19,
p. 69]. The measures of it in Maslach’s inventory cover
tension, anxiety and other factors that are mainly, but not
exclusively, related to the anxiety–contentment dimension
in Warr’s terms. The other two dimensions of burnout are a
reduced sense of personal accomplishment and deperson-
alisation. Personal accomplishment refers to feelings of
low self-efficacy and competency, and is associated nega-
tively with depression and positively with active partici-
pation in job-related decision-making; hence, it is related
to the depression–enthusiasm dimension in Warr’s terms.
Depersonalisation refers to the extent to which workers
become distant from and cynical about their work. In the
case of human service work, it is particularly reflected in
the extent to which workers are critical of their clients or
customers.
Methodology
Our study aims to assess the relationship amongst mental
health workers of job demands, control and support (from
peers or superiors) with well-being, using multiple mea-
sures of well-being. Multicentre research ethics approval
was obtained from the Brighton and Mid-Sussex Research
Ethics Committee and research governance approval
obtained from each participating NHS Trust.
Study design, sampling and data collection
Data reported in this paper are derived from the Inpatient
Staff Morale Study, funded by the SDO programme to
inform policy and service planning regarding the mental
health inpatient workforce. Subsequent papers will
describe overall levels of morale measured by various
indicators, explore relationships between indicators, and
test the effects of adding variables describing built envi-
ronment, ward organisation, area and service-user demo-
graphics, and adverse incidents to the demand–support–
control model. This study consists of a large sample of
mental health staff working in psychiatric wards, together
with smaller comparison samples of community mental
health teams for adults of working age and staff in crisis
resolution teams (teams available throughout England for
short-term assessment and management of crises, also
known as crisis and home treatment teams, intensive home
treatment teams or crisis assessment teams). The study
covers all occupational groups, full and part-time workers,
and qualified and unqualified workers. Managers, however,
are excluded from the analysis sample within this paper as
our measure of managerial support is based on workers’
assessment of them.
The sample was recruited from 19 NHS Trusts and,
within these, from 100 inpatient wards, 18 community
mental health teams and 18 crisis resolution and home
treatment teams. The inpatient sample was taken from 50
acute wards, 10 rehabilitation wards, 10 forensic wards, 10
mental health care of older people wards, 10 child and
adolescent mental health services (CAMHS) wards and 10
psychiatric intensive care units (PICUs). We will use the
term service units to refer collectively to the inpatient
wards and outpatient services.
Trusts were selected on the basis of the proximity to the
four universities involved in the research team (University
College London, Sheffield, Warwick and Bristol) and to
hubs of the Mental Health Research Network, which
greatly facilitated access as anticipated. We also purpo-
sively selected Trusts with wards covering a wide range of
(a) geographical and socio-demographic characteristics
(including some with close links to academic centres) and
(b) mental health sub-specialties.
Our self-completion questionnaire was distributed to all
workers within the 100 wards and 36 outpatient service
teams directly to staff or their manager by a member of the
research team. The completed questionnaires were returned
via post to a member of the research team. Questionnaire
distribution was preceded by a presentation of the research
at ward or team meetings for both the day and night shifts.
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Follow-up visits were made by a member of the research
team to stimulate responses. Ward or team managers were
also encouraged to rally staff to complete the
questionnaires.
Each service unit’s number of responses varied from 4
to 40, with an average of 14 employees. Out of a total of
3,545 people who received the questionnaire, 2,258 people
responded, yielding a response rate of 63.7%. Within the
wards, the response rate varied from 21.95 to 100% with a
median rate of 62.28%. Within the Trusts, the response rate
varied from 51.91 to 71.75% with a median rate of 60%.
Measures
We describe only those study measures on which the paper
reports.
Well-being outcomes
Six distinct measures of work-related well-being were
included in the questionnaire: intrinsic satisfaction, anxi-
ety, depression, emotional exhaustion, personal accom-
plishment and depersonalisation.
Intrinsic satisfaction was measured by a five-item scale
based on asking respondents how satisfied they were with
the following aspects of their job: ‘‘the sense of achieve-
ment I get from my work’’, ‘‘the scope for using my own
initiative’’, ‘‘the amount of influence I have over my job’’,
‘‘my involvement in decision making’’, ‘‘the opportunities
that I have to use my abilities’’. The first four of these items
were taken from the 2004 Workplace Employment Rela-
tions Survey, the last from the NHS National Staff Survey
of 2006. Respondents rated their satisfaction with each of
these facets on a five-point scale: 5 = ‘‘very satisfied’’,
4 = ‘‘satisfied’’, 3 = ‘‘neither satisfied nor dissatisfied’’,
2 = ‘‘very dissatisfied’’, or 1 = ‘‘dissatisfied’’.
Complementary three-item measures of anxiety and
depression [30] were employed, and respondents were
asked, ‘‘Thinking of the past few weeks, how much of the
time has your job made you feel’’ each of six negative
states: tense, uneasy, worried (for anxiety), miserable,
depressed and gloomy (for depression). For both scales, the
response categories were coded as 1 = ‘‘never’’, 2 =
‘‘occasionally’’, 3 = ‘‘some of the time’’, 4 = ‘‘most of the
time’’, 5 = ‘‘all of the time’’.
Maslach’s [19] concept of burnout is examined by 22
items formed by three subscales: emotional exhaustion,
personal accomplishment and depersonalisation. Levels of
emotional exhaustion are measured by a nine-item subscale
based on asking how often the respondent feels the fol-
lowing states: ‘‘emotionally drained from my work’’, ‘‘used
up at the end of the working day’’, ‘‘fatigued when I get up
in the morning’’, ‘‘burned out from my work’’, ‘‘frustrated
by my job’’, ‘‘like I’m at the end of my tether’’, ‘‘working
too hard on the job’’, ‘‘working with people involves too
much stress’’, and ‘‘working with people all day is a
strain’’. Personal accomplishment is similarly designed as
an eight-item subscale, asking about the extent to which the
respondent: ‘‘can easily understand patients’ feelings’’,
‘‘deals effectively with the patients’ problems’’, ‘‘positively
influences people’s lives’’, ‘‘feels very energetic’’, ‘‘can
easily create a relaxed atmosphere’’, ‘‘feels exhilarated
after working with patients’’, ‘‘has accomplished worth-
while things in job’’, and ‘‘deals with emotional problems
calmly’’. Finally, the depersonalisation subscale consists of
five items: respondents were asked the extent to which they
‘‘treat patients as impersonal objects’’, ‘‘become more
callous toward people’’, ‘‘worry that the job is hardening
emotionally’’, ‘‘don’t really care what happens to patients’’,
and ‘‘feel patients blame me for their problems’’. For all
three subscales, the response coding ranges from
0 = ‘‘never’’ to 6 = ‘‘everyday’’, with 3 = ‘‘a few times a
month’’ as the mid-point.
Hypothesised work characteristics antecedents
The measures of the principal independent variables were
based on those designed by Haynes et al. [12] to be
applicable to health workers.
Demand was measured by seven items asking respondents
how often they met each of the following problems in car-
rying out their work: ‘‘I don’t often have enough time to carry
out my work’’, ‘‘I cannot meet all the conflicting demands
made on my time’’, ‘‘I never finish work completing every-
thing I should have’’, ‘‘I am asked to do work without ade-
quate resources to complete it’’, ‘‘I cannot follow best
practice in the time available’’, ‘‘I am asked to do basic tasks
stopping me completing more important ones’’, and ‘‘vary-
ing levels of demands on my time’’. Respondents answered
the question on a five-point response coding, ranging from
1 = ‘‘not at all’’ to 5 = ‘‘a great deal’’, with 3 = ‘‘moderate
amount’’ as the mid-point.
Control was again measured by Haynes et al.’s [12] six-
item measure of this construct; the questions asked were to
what extent the respondent could ‘‘determine the methods
and procedures I use in my work’’, ‘‘choose what work I
will carry out’’, ‘‘decide when to take a break’’, ‘‘vary how
I do my work’’, ‘‘plan my own work’’, and ‘‘carry out my
own work in the way I think best’’. In addition, an item that
we designed on patient interaction was included: ‘‘To what
extent do you choose how you interact with patients?’’,
giving a seven-item scale. The response coding employed
was the same as that for demand.
Ward or team manager support was based on three
questions about the extent to which the individual can
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count on their ward or team manager to ‘‘listen when I need
to talk about problems at work’’, ‘‘help me with a difficult
task’’, and ‘‘provide effective leadership for the ward or
team’’. The five-point response coding used was 1 = ‘‘not
at all’’ and 5 = ‘‘completely’’, with 3 = ‘‘to a moderate
extent’’ as the mid-point.
Colleague support was measured by a four-item scale
based on the degree to which the individual can count on
colleagues to ‘‘listen when I need to talk about problems at
work’’, ‘‘help me with a difficult task’’, ‘‘back me up at
work’’, and ‘‘help me in a crisis situation at work, even
though they would have to go out of their way to do so’’.
The response categories were as for manager support.
Control variables
We also collected data on a number of potentially con-
founding demographic variables, namely each respondent’s
age, gender, marital status (coded as single, married or co-
habiting, and divorced, separated or widowed), ethnic
origin (coded as white, Asian, African or Caribbean, and
mixed or other ethnic group), and number of dependants.
Further work-related control variables that were collected
and examined were a respondent’s occupational group
(mental health nurse, social worker, nursing assistant,
occupational therapist, psychiatrist, clinical psychologist,
or mental health nurse who also fulfilled a further role),
their length of service in the service unit and in the mental
health service, employment status (permanent, second-
ment, fixed-term contract, and locum, bank or agency),
the Trust they worked for and the type of ward or team
they worked in (acute, crisis teams, PICU, CAMHS, or
forensic).
Analysis
Our analyses comprised three stages. First, we assessed the
validity and reliability of our proposed measures via a
combination of exploratory factor analysis, confirmatory
factor analysis and reliability analysis. Having derived
valid and internally consistent measures from our ques-
tionnaire items, we then created mean scores across each
scale to give us a single measure of each antecedent and
outcome construct of interest. Secondly, we explored the
sample characteristics and the bivariate relationships
between our measures of support, demands, control and
well-being.
The third stage involved building models to test the
additive and interactive models for the effects of support,
demands and control upon each outcome in turn. Due to
the three-level structure of our data, with employees
nested within services, which in turn were nested within
Trusts, and the subsequent potential to model and explain
random variation at each of the employee, service unit
and Trust levels, we used multilevel regression analysis to
build models for each outcome. This also gave us the
means to test for variation by service unit or Trust in the
effects of employee’s support, demands and control upon
well-being.
For each outcome, sets of variables were entered hier-
archically. Having first partitioned the variance into within-
and between-service unit components, the proposed control
variables were entered as predictors, with those exhibiting
significant effects upon at least one outcome retained for
the further steps of the modelling process. These steps were
first a test of the additive model, via the simultaneous
assessment of the main linear effects of demands, control
and support, and the investigation of potential curvilinear
effects. Following this, the interactions between demands,
control and support were added to test the interactive
version of the Karasek model.
Our analyses were carried out on a maximum working
or ‘analysis’ sample of 1,870 employees from 136 service
units located within 17 different Trusts. This sample was
denuded slightly for each analysis performed due to low
levels of attritional missing data across the study variables
and the subsequent listwise deletion of cases. Given this
large sample size at the employee level, all analyses testing
employee-level effects utilised the p \ 0.005 level of
statistical significance.
Results
Stage 1
To test the validity of the measurement model posited to
underlie the well-being, support, control and demands
constructs, a confirmatory factor analysis was initially run
on the whole sample, with items grouping together to load
onto 10 factors as per their 10 respective scales. This
resulted in a less than adequate fit to the data (chi-square =
6,893 on 1,332 df; with fit indices of CFI = 0.890,
RMSEA = 0.052 and SRMR = 0.060) according to the
widely accepted standards outlined in Hu and Bentler [13].
Furthermore, 6 of the 54 items exhibited particularly weak
communalities with R-squared statistics (the proportion of
their variance explained by the model) of less than 0.30.
In an attempt to find the optimal measurement model for
these items and constructs, we then split the data into
random halves, enabling us to create and test a putative
model on different subsamples whilst sidestepping the
danger of an upward bias on measures of fit caused by
building and testing a model on the same set of data. An
exploratory factor analysis was performed on one half of
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the data, using principal axis factoring as the extraction
method, and assessing the number of factors to be extracted
by a combination of Kaiser’s criterion and Cattell’s scree
plot method, as recommended by Conway and Huffcutt [3].
Oblique rotation was carried out to aid interpretation; the
results suggested the removal of a handful of low-loading
or cross-loading items, specifically ‘‘to what extent do you
choose how you interact with patients?’’ from the job
control scale, ‘‘working too hard on the job’’, ‘‘working
with people involves too much stress’’ and ‘‘working with
people all day is a strain’’ from emotional exhaustion, and
‘‘can easily understand patients’ feelings’’ and ‘‘feel very
energetic’’ from personal accomplishment. Further inves-
tigation of the item frequencies indicated that four of the
five measuring depersonalisation had extremely limited
variability in scores across the sample, with between 70
and 90% of respondents reporting no or very low levels of
depersonalisation; hence, we chose to exclude this entire
subscale.
This resulted in a nine-factor measurement model for the
remaining 43 items, which exhibited an improved fit to the
validation half of the data when tested using a confirmatory
factor analysis; the recommended fit indices were all
enhanced, with CFI = 0.925, RMSEA = 0.051 and
SRMR = 0.050. Communalities for all items were above
0.3. Estimated correlations between factors were primarily
of small to medium size, with the only large correlations
present existing between anxiety, depression and emotional
exhaustion and between satisfaction and control (r \ 0.73).
Potential alternative item-factor configurations motivated
by these stronger correlations, such as the six depression
and anxiety items loading onto a single factor, did not
produce a better fitting model. When this model was
applied to the full dataset the fit was stronger still
(CFI = 0.931, RMSEA = 0.049 and SRMR = 0.046), and
a clear improvement over that of the originally postulated
measurement model.
We concluded the scale validation and construction
analyses by calculating the internal consistency reliability
coefficients for each potential scale. All nine sets of items
showed high internal consistency, with Cronbach’s alpha
[0.75 in each case. We then calculated scale scores for
each set of items for use as our measure of the respective
construct in our subsequent analyses, taking the unweigh-
ted average across all items within the scale. Reliability
statistics are given in Table 1.
Stage 2
This stage entailed exploring the sample characteristics and
the bivariate relationships between our hypothesised ante-
cedents and outcomes. Of the 1,870 cases in our analysis
sample, the average age was 41, and 63% of respondents
were female. Sixty-six percent of cases were married, a
third of the remainder were widowed or divorced, and the
remainder single; just under half (49%) had dependants.
The ethnic makeup of the sample was largely white (75%),
with black (16%) and Asian (8%) workers comprising
almost all of the remainder.
The predominant occupation was nursing: 51% of the
sample was mental health nurses, 26% worked as nursing
assistants or support workers, and a further 5% described
themselves as both mental health nurses and a further
occupation. The remainder of the cases within our analysis
sample were occupational therapist (4%), psychiatrist
(7%), clinical psychologist (2%), and social worker (4%).
The vast majority (94%) of the sample were on permanent
contracts. The mean total hours worked per week was 40
(SD = 11 h); 67% worked shifts, but only 7% reported
permanent working of night shifts. Respondents had spent
an average of 4 years’ service in their respective service
units (median = 3 years) and 8 years in the mental health
service (median = 9 years).
The bivariate correlations between the antecedent and
outcome measures for the analysis sample are given in
Table 1, alongside means and standard deviations.
Stage 3
This final stage of the analyses tested our hypotheses for
each outcome via a series of multilevel regression models.
The initial models simply partitioned the variability in the
respective outcome into that within service units, between
service units and between Trusts, providing a baseline
against which to compare subsequent models containing
predictor variables, and enabling the calculation of the
ICC(1) (Intraclass Correlation Coefficient). For four of the
five outcomes, the proportion of variation due to differ-
ences between Trusts was found to be trivial, falling
below 0.005 in each case (i.e. \0.5% of the total varia-
tion). For intrinsic satisfaction it corresponded to a small
effect (=0.015). Given these findings, and the small
sample size at the Trust level (N = 17), we used a two-
level model of employees nested within service units,
with between-service unit differences hence absorbing
the small amount of between-Trust variability. All five
outcomes exhibited a non-trivial between-service unit
variance component ranging from 5 to 8% of the total
variability (i.e. 0.05 \ ICC(1) \ 0.08), justifying the
exploration of both service unit and employee levels of
variability via a multilevel modelling strategy. The esti-
mates of within- and between-service unit variance for
each outcome are given in Table 2; to enable model
comparison, these values were retrospectively calculated
for the subsample of cases which responded to all vari-
ables used in subsequent models.
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We then examined the relative contribution of the
demographic and background variables to explaining
within- and between-service unit variability in each out-
come. For continuous variables age and tenure, squared
effects were also considered. Of the control variables used,
service unit type, occupational group and ethnic back-
ground were found to be statistically significant predictors
of one or more of the outcomes.
Specifically, service unit type had a statistically signif-
icant impact on all outcomes. Employees within crisis
teams were typically the most satisfied and felt the most
personal accomplishment, with those within acute and
PICU wards least satisfied, and those within acute, older
patient and forensic wards most likely to feel lower levels
of accomplishment. For anxiety, depression and emotional
exhaustion, respondents from within acute wards and
CMHT were most likely to report high levels, and those
working within rehabilitation the least. Ethnic group was
related to intrinsic satisfaction, personal accomplishment
and emotional exhaustion; for the first two of these out-
comes, black and Asian workers were most likely to report
higher levels; conversely, white workers were most likely
to be emotionally exhausted, with Asians least likely. The
effect of occupational group also varied by outcome:
therapists and psychologists were more likely to report
high levels of intrinsic satisfaction with their jobs and
lower levels of depression, particularly compared to mental
health nurses and social workers. However, mental health
nurses and social workers had higher levels of emotional
exhaustion and anxiety, with nursing assistants and support
workers having the lowest predicted levels when other
control variables were held constant. However, nursing
assistants and support workers, along with social workers,
were also the most likely to report low levels of personal
accomplishment; the highest levels were associated with
the distinct group of mental health nurses who also fulfilled
other occupational group roles.
These three variables, service unit type, occupational
group and ethnic background, were retained for the next
stage of the modelling process; their impact on each out-
come in terms of individual effects is summarised in
Table 2, and in terms of model improvement in Table 1,
Table 1 Internal consistency reliability of, summary statistics for, and correlations between outcome and antecedent measures
Scale Number
of items
in scale
Reliability
(Cronbach’s
alpha)
Mean
scale
score
(N)
Mean
scale
score
(mean)
Mean
scale
score
(SD)
Pearson’s correlation coefficient
1 2 3 4 5 6 7 8
1. Intrinsic job
satisfaction (high
score = high
satisfaction)a
5 0.88 1,836 3.39 0.80
2. Anxiety (high
score = high anxiety)a3 0.77 1,807 2.53 0.75 -0.40
3. Depression (high
score = high
depression)a
3 0.83 1,807 2.08 0.84 -0.53 0.64
4. Emotional exhaustion
(high score = high
exhaustion)b
6 0.91 1,828 2.51 1.50 -0.48 0.57 0.66
5. Personal
accomplishment (high
score = high
accomplishment)b
6 0.79 1,804 4.31 1.11 0.33 -0.15 -0.28 -0.14
6. Job control (high
score = high control)a6 0.89 1,837 3.13 0.87 0.56 -0.21 -0.31 -0.25 0.26
7. Work demands (high
score = high work
demands)a
7 0.92 1,839 2.81 1.01 -0.33 0.43 0.38 0.58 -0.11 -0.13
8. Colleague support (high
score = high support)a4 0.95 1,835 3.60 0.92 0.32 -0.19 -0.26 -0.22 0.23 0.24 -0.16
9. Ward or team manager
support (high
score = high support)a
3 0.92 1,819 3.40 1.16 0.41 -0.22 -0.29 -0.29 0.19 0.27 -0.24 0.39
a Scale range: 1–5b Scale range: 0–6
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again using the subsample that also responded to the sub-
sequently examined measures of demands, control and
support. Across the five outcomes, they accounted for a
small amount of variability (between 1 and 3%) within
service units, i.e. intra-individual differences, and for a
more substantial 15–47% of the variability between service
units. The other control variables considered did not have
any statistically significant unique effects upon the well-
being outcomes.
Variables measuring demands, control and support
were then entered to test the model of additive effects; each
was first standardised to avoid collinearity issues in the
subsequent testing of the multiplicative model. The main
effects each of job control, ward or team manager support,
and colleague support had negative linear effects upon each
of anxiety, depression, and emotional exhaustion. Simi-
larly, significant positive effects of work demands were
also found for each of these outcomes. Likewise, both
support variables and control had a positive impact upon
intrinsic satisfaction, with demands having a negative
impact. Only for personal accomplishment were the effects
of our antecedents less powerful, with only colleague
support and job control attaining significant positive effects
at the p \ 0.005 level. The estimated coefficients for fixed
and random effects for each outcome at this stage of the
analysis are given in Tables 3 and 5 respectively. Note that
demands, support and control together reduced both the
initial unexplained within-service unit and between-service
unit variance for each outcome by a further substantial
amount on top of that already explained by the control
variables (between 9 and 42%, and between 29 and 48%).
The model deviance statistics, given by the -2log likeli-
hood statistic, were also dramatically reduced, though due
to the estimation method applied (residual maximum
likelihood) and the non-nested nature of these models with
respect to those (control variables only) emerging from the
first stage of the model building process, formal tests of the
reduction in deviance (i.e. the improvement in the fit of the
model as a whole) were not possible.
Allowing slope variation for each predictor in turn (i.e.
the effect of each predictor to vary by service unit) did not
produce statistically significant slope variance coefficients,
nor significantly improve the fit of the model for any out-
come, indicating that the effects of demands, control and
support described above were applicable across the service
units within the sample.
Table 2 Effects of statistically significant control variables upon each outcome
Predictors B Coefficients for each predictor’s fixed effect upon each outcome
INTSAT
(N = 1,694)
ANX
(N = 1,664)
DEP
(N = 1,664)
EE
(N = 1,688)
PA
(N = 1,672)
Occupational group dummies (vs. ref cat: mental health nurses)
Social worker -0.137 0.077 0.034 -0.039 -0.201
Nursing assistant/support worker -0.015 -0.163* -0.081 -0.490* -0.086
Occupational therapist 0.253 -0.183 -0.213 -0.138 0.092
Psychiatrist 0.183 0.023 -0.162 -0.051 0.097
Clinical psychologist 0.278 -0.058 -0.368 -0.359 0.120
Mental health nurse plus other occupation 0.146 -0.079 -0.154 -0.214 0.254
Occupational group: total fixed effect F = 3.575* F = 3.145* F = 2.653 F = 5.564* F = 1.961
Service unit type dummies (vs. ref cat: crisis teams)
Acute -0.272* 0.169 0.276* 0.439* -0.327*
PICU -0.241 0.117 0.226 0.252 -0.224
CAMHS -0.080 -0.054 0.060 0.100 -0.018
Forensic -0.205 -0.082 0.082 0.149 -0.601*
Rehabilitation -0.026 -0.141 -0.037 -0.345 -0.258
Older adult -0.172 -0.111 0.111 0.223 -0.395
CMHT -0.153 0.226 0.323* 0.624* -0.053
Service unit type: total fixed effect F = 2.190 F = 4.988* F = 3.402* F = 4.546* F = 4.788*
Ethnic group dummies (vs. ref cat: White)
Black 0.257* 0.049 0.108 -0.076 0.296*
Asian 0.255* -0.084 -0.096 -0.487* 0.167
Mixed/other 0.135 -0.047 -0.020 -0.553 -0.091
Ethnic group: total fixed effect F = 9.068* F = 0.932 F = 1.789 F = 5.751* F = 5.535*
* p \ 0.005
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We then added squared effects of each of these pre-
dictors to test for any curvilinear relationships with each
outcome. The only statistically significant effects at the
p \ 0.005 level were for colleague support on intrinsic
satisfaction and depression, and of job control on personal
accomplishment. In the first two instances the curvilinear
relationship reflected a ceiling or basement effect. In other
words, the benefit of support diminished in strength as the
amount of support increased and was not apparent for very
high levels of support, suggesting that there comes a sat-
uration point at which giving further support is no longer
worthwhile. Conversely, the benefits of control on personal
accomplishment only became apparent once control
reached moderate levels, suggesting that a very small
amount of control is no better than none at all.
Finally, we examined the multiplicative effects between
demands, support and control. These consisted of the five-
two-way interactions and the two- to three-way interactions
between either support variable, and one or both of
demands and control. Following the advice of Edwards [8],
we retained the squared effects of each variable within the
model to ensure that any interactions effects detected were
not caused by underlying polynomial effects of a single
variable.
In contrast to the additive model, the support for the
multiplicative Karasek model, according to which the
interactions amongst demands, control and support impact
upon well-being, is less strong. Table 4 reports the models
that include the key interaction terms; for these analyses we
have also indicated results at the p \ 0.05 level due to the
increased measurement error and reduced power inherent
in testing such multiplicative effects.
Across the different measures of well-being the pattern
of statistically significant interaction effects varied. How-
ever, for four of the outcomes the combined effects of the
multiplicative predictors reduced the unexplained within-
service unit variability by a small amount (1%) on top of
that already explained by the control variables and main
effects; further details for each outcome are given in
Table 5.
The only support from the tests for two-way interactions
at the p \ 0.005 level is for the combination of ward or
team manager support and control on intrinsic satisfaction;
specifically, the importance of having control in terms of
boosting satisfaction diminished when ward or team man-
ager support was high. There is weaker evidence that the
importance of job control in increasing satisfaction was
amplified when work demands were high. This effect was
repeated when the outcome variable was depression, i.e.
the importance of job control on reducing depression was
enhanced when work demands were high. Likewise, the
predicted positive impact of work demands on anxiety
diminished slightly as ward or team manager support
increased. Taking these results together, it appears that
high levels of support and control, respectively can
mitigate the worst effects of demands on anxiety and
depression.
Finally, for both anxiety and depression, similar three-
way interactions between demands, control and colleague
support were found. These both indicated that, as colleague
support decreased, the importance of job control in miti-
gating the impact of demands upon well-being (both anx-
iety and depression) was enhanced, as illustrated in Figs. 1
and 2. This suggests that support and control are, to an
extent, interchangeable buffers against the negative impact
of demands.
In the cases of personal accomplishment and emotional
exhaustion, none of the interaction effects were found to be
statistically significant.
Discussion
This research has shown that the demand, control and
support model of well-being is applicable to mental health
workers. However, only the additive model is supported
Table 3 Additive model main effects of demands, support and control, having controlled for retained control variables
Predictors B Coefficients for support, demands and control fixed effect upon each outcome
INTSAT
(N = 1,694)
ANX
(N = 1,664)
DEP
(N = 1,664)
EE
(N = 1,688)
PA
(N = 1,672)
(Occupational group: total fixed effect) F = 1.048 F = 0.845 F = 1.674 F = 0.787 F = 1.147
(Service unit type: total fixed effect) F = 1.370 F = 3.444* F = 2.075 F = 1.750 F = 3.395*
(Ethnic group: total fixed effect) F = 8.797* F = 1.716 F = 3.362 F = 3.121 F = 5.985*
Work demands (standardised) -0.144* 0.275* 0.258* 0.768* -0.056*
Colleague support (standardised) 0.110* -0.059* -0.115* -0.115* 0.191*
Ward or team manager support (standardised) 0.133* -0.049* -0.084* -0.148* 0.051
Job control (standardised) 0.442* -0.118* -0.192* -0.257* 0.250*
* p \ 0.005
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across all five measures of well-being. The multiplicative
model is only partially supported.
The diversity of results for the models that include
interaction terms highlights the discreteness of the different
measures. Further theoretical and empirical work (perhaps
of a different nature to this) is required to explain why the
multiplicative model is seemingly most applicable to the
anxiety and depression dimensions of well-being, whilst
not fitting the personal accomplishment or emotional
exhaustion elements of burnout. This study’s strength is the
large working sample of mental health workers, which was
drawn from all occupational groups and not confined to
inpatient services. The extent to which it is representative
of the population of British mental health workers cannot
be tested, but we have no reason to suspect that it is not
representative of staff in inpatient care, nor that the range
of service units and community services are exceptional,
since we selected them to cover rural and urban areas, large
and small Trusts. Our response rate compares favourably
with studies in the mental health area or more generally,
but there are studies that have achieved higher rates. The
main weakness of the study is its cross-sectional nature,
and we cannot be certain that perceptions of demands,
support and even control are independent from levels of
well-being, though the diversity in the interaction results
gives us some optimism that this is not the case.
Conclusions
By showing that a major theory of work psychology is
applicable to mental health workers, this study is interest-
ing in its own right. It illustrates that the demands made on
people, the amount of control or discretion they have in
their jobs, and the support from both their managers and
colleagues are important focal points for understanding and
helping to improve the well-being of mental health work-
ers. Whereas past studies of the antecedents of morale have
Table 4 Multiplicative model: main and curvilinear effects, and interaction effects of demands, support and control, having controlled for
retained control variables
Predictors B Coefficients for support, demands and control fixed effects upon each
outcome
INTSAT
(N = 1,694)
ANX
(N = 1,664)
DEP
(N = 1,664)
EE
(N = 1,688)
PA
(N = 1,672)
(Occupational group: total fixed effect) F = 1.209 F = 0.858 F = 1.476 F = 0.837 F = 1.558
(Service unit type: total fixed effect) F = 1.311 F = 3.498* F = 2.092 F = 1.761 F = 3.803*
(Ethnic group: total fixed effect) F = 8.795* F = 1.597 F = 3.695 F = 3.460 F = 6.262*
Work demands (standardised) -0.144* 0.274* 0.249* 0.752* -0.064
Colleague support (standardised) 0.106* -0.047 -0.096* -0.114* 0.191*
Ward or team manager support (standardised) 0.130* -0.036 -0.075* -0.130* 0.063
Job control (standardised) 0.441* -0.122* -0.185* -0.247* 0.262*
Work demands (standardised) squared -0.029� -0.018 0.012 0.025 0.009
Colleague support (standardised) squared -0.044* 0.027 0.055* 0.034 0.007
Ward or team manager support (standardised) squared 0.003 0.022 0.010 0.022 0.025
Job control (standardised) squared 0.015 -0.021 0.002 0.022 0.100*
Interaction
Work demands (standardised) 9 job control (standardised) 0.044� -0.026 -0.045� -0.060 0.002
Work demands (standardised) 9 colleague support (standardised) -0.011 0.001 0.005 0.036 -0.056
Work demands (standardised) 9 ward or team manager support
(standardised)
-0.016 -0.032� 0.003 -0.016 -0.043
Job control (standardised) 9 colleague support (standardised) 0.036 -0.003 -0.047� -0.063 0.015
Job control (standardised) 9 ward or team manager support
(standardised)
-0.048* 0.011 0.035 0.026 -0.047
Work demands (standardised) 9 job control (standardised) 9
ward or team manager support (standardised)
0.019 -0.017 -0.025 -0.025 0.007
Work demands (standardised) 9 job control (standardised) 9
colleague support (standardised)
0.023 0.036� 0.050� 0.048 -0.012
* p \ 0.005� p \ 0.05 (marked for multiplicative effects only)
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Table 5 Random effects and model deviance statistics for models at each stage of the model building process
INTSAT
(N = 1,694)
ANX
(N = 1,664)
DEP
(N = 1,664)
EE
(N = 1,688)
PA
(N = 1,672)
Step 0
Baseline (unconditional) variance components model with no predictor variables
Baseline re2 0.602 0.522 0.664 2.134 1.152
Baseline ru2 0.040 0.034 0.042 0.140 0.051
Baseline -2LL (deviance)a 4,032 3,722 4,121 6,153 5,044
Step 1
Retained control variables only
re2 0.590 0.518 0.657 2.071 1.138
% baseline re2 explained at this step 2 1 1 3 1
ru2 0.034 0.018 0.031 0.097 0.031
% baseline ru2 explained at this step 15 47 26 31 39
-2LL 4,023 3,718 4,120 6,097 5,029
Step 2
Retained control variables and main linear effects of demands, support and control
re2 0.335 0.420 0.514 1.367 1.036
% baseline re2 further explained at this step 42 19 22 33 9
ru2 0.015 0.008 0.012 0.031 0.014
% baseline ru2 further explained at this step 48 29 45 47 33
-2LL 3,086 3,375 3,710 5,393 4,874
Step 3
Retained control variables, main linear, curvilinear and multiplicative effects of demands, support and control
re2 0.329 0.416 0.508 1.364 1.027
% baseline re2 further explained at this step 1 1 1 0 1
ru2 0.015 0.008 0.012 0.031 0.012
% baseline ru2 further explained at this step 0 0 0 0 0
-2LL 3,117 3,418 3,747 5,436 4,905
re2 = unexplained variance within wards, ru
2 = unexplained variance between wards -2LL, F model deviance on F degrees of freedoma Models fitted using residual maximum likelihood, hence precluding direct comparison/testing of change in model deviance between competing
non-nested models (i.e. those containing different predictors)
Low Colleague Support
Work Demands (Standardised)
3.002.001.000.00-1.00-2.00
Pre
dic
ted
An
xiet
y (a
dju
sted
for
cova
riat
es)
4.00
3.00
2.00
1.00
High Colleague Support
Work Demands (Standardised)
3.002.001.000.00-1.00-2.00
Pre
dic
ted
An
xiet
y (a
dju
sted
for
cova
riat
es)
4.00
3.00
2.00
1.00
High Control
Low Control Low Control
High Control
Fig. 1 The multiplicative effect of demands, control and colleague support upon anxiety
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often investigated a mixture of general and mental health
service-specific indicators without being rooted in an over-
arching theoretical framework, the results of our study sug-
gest that future research should begin from this very well-
established model of occupational stress. This study thus
provides a benchmark model for testing organisational fac-
tors including personnel management methods, top man-
agement leadership styles, and mechanisms for extending
employee involvement beyond the role level. This also
enables future assessment of whether mental health-specific
factors, such as the violence of patients, have their own
discrete influence, or rather are mediated by general demand,
control or support factors. Alternatively, this model could be
developed to assess whether there are aspects of community
work that differentiate it from inpatient service, over and
above any differences in typical demands, control or support
levels. Our survey includes questions on such factors and
future work will be directed at such issues.
Within the NHS, strategies for alleviating mental health
staff stress have traditionally focused on managerial or peer
support, for example via supervision and appraisal, peer
support groups or training. Our findings suggest that such
strategies have the potential to improve morale if they are
experienced as supportive. Colleague support is important
as well as that from managers; thus interventions to
enhance team cohesion and relationships may have
potential, as well as more formal manager support ones.
Current initiatives in inpatient settings (e.g. protected
engagement time) are highly focused on staff–patient
interaction, improving the quality of this and getting staff
out of clinical offices into wards. However, opportunities
for staff to spend more time together may also be necessary
to improve staff morale and thus performance.
That demand is important for well-being seems intui-
tively likely and is unsurprising given a range of studies
that have cited various mental health service-specific
demands as important to staff stress. How demands may be
reduced is rather less obvious, given high demands for
mental health services in general. A beginning might be the
identification of specific tasks or roles where staff are under
high levels of pressure with a view to redesigning the way
teams work to try and alleviate stress experienced in these.
Involvement of staff in these processes should increase
their success. Initiatives to reduce the demands on services
as a whole are difficult to design, but might include clearer
intake criteria and protocols that allow staff to focus on the
activities that are their core roles, rather than expending
effort on, for example, working with patients on matters
that could be better dealt with elsewhere.
The strong association between autonomy and well-
being suggests that this relatively neglected factor should
be given more prominence in policy. We are not aware of
strategies for improving staff morale that have specifically
focused on autonomy. However, interventions for
improving job design have been developed in other set-
tings. A first step would be to investigate in more detail the
organisation of jobs and teams to identify areas in which
autonomy might be increased, especially in groups that
report low levels. Job redesign or changes in management
practices to allow greater autonomy in deciding how to
work would then be feasible, though training initiatives
may well be needed to support these.
Acknowledgments This project was funded by the National
Institute for Health Research Service Delivery and Organisation
Programme (project number/08/1604/142). The views and opinions
expressed in this paper are those of the authors and do not neces-
sarily reflect those of the Department of Health. We wish to
acknowledge the contribution of the other members of the Inpatient
Staff Morale Study research team, and are also very grateful for
extensive support received from the North and South London,
South-West, East of England and Heart of England hubs of the
Mental Health Research Network, and for the helpfulness of staff in
the 136 participating services.
Low Colleague Support
Work Demands (Standardised)
3.002.001.000.00-1.00-2.00Pre
dic
ted
Dep
ress
ion
(ad
just
ed fo
r co
vari
ates
)
4.00
3.00
2.00
1.00
High Colleague Support
Work Demands (Standardised)
3.002.001.000.00-1.00-2.00Pre
dic
ted
Dep
ress
ion
(ad
just
ed fo
r co
vari
ates
)
4.00
3.00
2.00
1.00
High Control
Low Control
Low Control
High Control
Fig. 2 The multiplicative effect of demands, control and colleague support upon depression
Soc Psychiatry Psychiatr Epidemiol (2011) 46:1055–1068 1067
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