HEALTH POLICY AND SYSTEMS

Impact of Nursing Unit Turnover on PatientOutcomes in HospitalsSung-Heui Bae, RN, MPH, PhD1, Barbara Mark, RN, PhD, FAAN2, & Bruce Fried, PhD3

1 Research Assistant Professor, School of Nursing, University at Buffalo, State University of New York, Buffalo, NY2 Sarah Frances Russell Distinguished Professor, School of Nursing, University of North Carolina at Chapel Hill, NC3 Associate Professor, Health Policy and Administration, School of Public Health, University of North Carolina at Chapel Hill, NC

Key wordsNursing turnover, workgroup processes,

patient safety, patient satisfaction

CorrespondenceDr. Sung-Heui Bae, School of Nursing, University

at Buffalo, State University of New York, 211

Wende Hall, 3435 Main Street, Buffalo, NY

14214-3079. E-mail: sbae7@buffalo.edu

Accepted: July 31, 2009

doi: 10.1111/j.1547-5069.2009.01319.x

Abstract

Purpose: The aim of this study was to examine how nursing unit turnoveraffects key workgroup processes and how these processes mediate the impactof nursing turnover on patient outcomes.Methods: A secondary data analysis was used to test the hypothesized model.This study used registered nurse and patient data from 268 nursing units at 141hospitals collected as part of the Outcomes Research in Nursing Administra-tion (ORNA II) project. Nursing units provided monthly nursing unit turnoverrates for 6 consecutive months, and registered nurses completed question-naires measuring workgroup processes (group cohesion, relational coordina-tion, and workgroup learning). Patient outcome measures included unit-levelaverage length of patient stay, patient falls, medication errors, and patient sat-isfaction scores.Results: Nursing units with moderate levels of turnover were likely to havelower levels of workgroup learning compared to those with no turnover(p<.01). Nursing units with low levels of turnover were likely to have fewerpatient falls than nursing units with no turnover (p<.05). Additionally, work-group cohesion and relational coordination had a positive impact on patientsatisfaction (p<.01), and increased workgroup learning led to fewer occur-rences of severe medication errors (p<.05).Conclusions: The findings of this study provide specific information on theoperational impact of turnover so as to better design, fund, and implementappropriate intervention strategies to prevent registered nurse exit from nurs-ing units. Further investigation is needed to assess the turnover-outcomesrelationship as well as the mediating effect of workgroup processes on thisrelationship.Clinical Relevance: Managing nursing unit turnover within appropriate lev-els at the nursing unit is critical to delivering high-quality patient care.

The adverse impact of nursing turnover on quality of pa-tient care is a long-standing assumption, yet there is littleunderstanding of the turnover-quality relationship or itsunderlying mechanisms. When turnover occurs, the re-maining staff must adjust to newcomers, and turnovermay affect the interaction and integration among staffmembers who remain (Price, 1977; Staw, 1980). Re-searchers have also suggested potential positive conse-

quences of turnover, such as introducing fresh ideasand keeping the organization from becoming stagnant(Staw). Most empirical research on nursing turnoverhas focused on a direct relationship between turnoverand patient outcomes; the underlying mechanisms of theturnover-outcomes relationship have not been explored(Alexander, Bloom, & Nuchols, 1994; Castle & Eng-berg, 2005; Voluntary Hospital Association Inc., 2002).

40 Journal of Nursing Scholarship, 2010; 42:1, 40–49.c© 2009 Sigma Theta Tau International

Bae et al. Impact of Nursing Unit Turnover

In order to understand the mechanisms by whichnursing turnover is related to patient outcomes, it isnecessary to explore the impact of nursing turnoveron the nursing unit, which is the proximal contextfor individuals and a bounded interactive context cre-ated by nurses’ attributes, interactions, and responses(Kozlowski, Steve, & Bell, 2003).

We applied a conceptual framework at the nursing unitlevel to examine the impact of nursing unit turnover onworkgroup processes (workgroup cohesion, relational co-ordination, and workgroup learning) as well as on patientoutcomes (patient satisfaction, average length of patientstay, patient falls, and medication errors).

Model of Turnover Consequences

The proposed model was formulated around the input-process-outcome (IPO) framework (McGrath, 1964). Theframework provides a model to assess workgroup behav-ior and performance effectiveness, and most models ofworkgroup effectiveness incorporate it (Kozlowski et al.,2003). Below, we provide more details about the model.

Workgroup Processes

Workgroup processes represent mechanisms that en-able or inhibit the ability of team members to combinetheir capabilities and behavior (Kozlowski et al., 2003).Workgroup cohesion, relational coordination, and work-group learning represent the domains of affective, be-havioral, and cognitive workgroup processes, respectively(Kozlowski et al.). Workgroup cohesion is commonly de-fined as the overall attraction or bond among membersof a group (Mullen & Cooper, 1994). Distinct from for-mal coordinating mechanisms such as rules and manuals,relational coordination is a spontaneous form of coordi-nation, encompassing patterns of communication and re-lationships (Gittell, 2002).

Turnover may negatively affect both workgroup co-hesion and relational coordination. As increasing num-bers of nurses leave the unit, those who remain may feelabandoned and question their own motivations for stay-ing. It may trigger additional turnover, detachment, anda search for salient alternative memberships (Staw, 1980;Kovner, Brewer, Greene, & Fairchild, 2009; Brewer,Kovner, Greene, & Cheng, 2009). Similarly, when valuedemployees leave, communication flow and establishedrelationships are disrupted (Price, 1977). In a nursing unitwith frequent turnover, increased adjustment time is re-quired for new staff, and the remaining nurses may needto be particularly careful when supervising new staff.Thus, as nursing unit turnover increases, relational co-ordination is not easily achieved. Therefore, we hypoth-

esized that higher nursing turnover would lead to lowerworkgroup cohesion and lower relational coordination.

Workgroup learning. refers to relatively perma-nent changes in the knowledge associated with the expe-rience of an interdependent set of individuals (Kozlowskiet al., 2003). Researchers have suggested that turnoverimpacts learning both positively and negatively. Individ-uals leaving a workplace cannot transfer personal expe-rience and knowledge to those who remain; therefore,workplace history lessons are lost and a portion of work-group memory disappears (Carley, 1992). On the otherhand, learning requires both stability and change in theenvironment. Too much stability and unchanging behav-ior within a workgroup can lead to stagnation rather thancognitive growth (Hedberg, 1981). We hypothesized that,relative to nursing units with high or low levels of regis-tered nurse (RN) turnover, nursing units with moderatelevels of RN turnover would experience greater work-group learning.

Unit-Level Patient Outcomes

Patient satisfaction. has been proposed by many asan indicator of nursing care quality (e.g., Ervin, 2006).Mrayyan (2006) defined patient satisfaction as the de-gree to which nursing care meets patients’ expectationsof ideal care, including the art, technical quality, physi-cal environment, availability, continuity, and efficacy ofcare. Greater cohesion among employees in customer ser-vice settings strengthens employee motivation to provideexcellent service, which may lead to higher levels of cus-tomer (patient) satisfaction (Meterko, Mohr, & Young,2004). Gittell (2002) found a strong relationship betweenrelational coordination among care providers and patientsatisfaction. Therefore, we hypothesized that lower nurs-ing unit cohesion and lower levels of relational coordi-nation would be associated with lower levels of patientsatisfaction.

Length of stay. (LOS) is often used as a measureof hospital efficiency (Halter, 2006). Well-coordinatedworkgroups are expected to produce higher-quality out-comes and to do so more efficiently (Gittell, 2002). Sev-eral researchers have concluded that improved nurse-physician coordination and communication can reducehospital LOS (e.g., Halter). In highly cohesive and coordi-nated nursing units, healthcare providers are able to bet-ter communicate information about patients and provideresponsive care for patients’ clinical conditions. There-fore, we hypothesized that lower relational coordinationwould be related to longer LOS.

Patient falls and medication errors. harm pa-tient safety and cause hospitals to lose millions of dol-lars (Bates et al., 1997). A well-coordinated staff may

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Impact of Nursing Unit Turnover Bae et al.

recognize potential patient risks early and provide in-terventions to lower risks for patient falls. Coordina-tion (e.g., openness and mutual understanding) betweennurses and physicians and nurses and pharmacy person-nel have been found to influence appropriate drug se-lection and administration (Schmidt & Svarstad, 2002;Kopp, Erstad, Allen, Theodorou, & Priestley, 2006).Workgroup learning is also important for patient safety.Workgroups learn from their failures through assessingand changing work processes in response to past errorsand thus perform better in the long run (Kaissi, 2006;Edmondson, 1999). However, when learning from errorsdoes not occur, it not only discourages nurses from re-vealing and discussing them, but it can allow errors toremain uncorrected. We hypothesized that lower levelsof relational coordination and workgroup learning wouldbe associated with increased patient falls and medicationerrors.

Mediating Effects of Workgroup Processes

We hypothesized that workgroup processes mediatethe nursing unit turnover–patient outcomes relationship.Nursing turnover leads to changes in workgroup pro-cesses such as decreased member attraction to the nursingunit, ineffective coordination, and inaccurate communi-cation. Such inefficient workgroup processes in turn neg-atively affect patient outcomes (e.g., patient satisfactionand LOS). On the other hand, compared to nursing unitswith very low or very high levels of turnover, units withmoderate levels experience a balance between constancyand change (Hedberg, 1981). Workgroups can both learnfrom past mistakes and be creative and flexible. This mayhelp prevent patient falls and medication errors.

Nursing Unit, Hospital, Nurse, and PatientCharacteristics

Nursing unit, hospital, nurse, and patient characteris-tics were included as control variables. Work complexityat the unit level has implications for workgroup processes(Argote, Insko, Yovetich, & Romero, 1995; Gittell, 2002)and it also contributes to work conditions that affect ef-ficiency and quality of care (Dunton, Gajewski, Taunton,& Moore, 2004). Unit size has been linked to nursing unitperformance (Mark, Salyer, & Wan, 2003). In this study,characteristics of the hospital included hospital size, tech-nological sophistication, and teaching status. Larger organiza-tions with better support systems for patient care havebeen shown to increase the resources dedicated to im-proving quality of care and efficiency (Kuhn, Hartz, Got-tlieb, & Rimm, 1991). Advanced technological servicesalso have been linked to quality of care (Kuhn et al.).For common conditions, teaching hospitals generally pro-

vide better care than do nonteaching hospitals (Ayanian& Weissman, 2002). Evidence suggests that patient out-comes are positively associated with nurse education level,unit tenure, and care hours provided by RNs (Kane, Sham-liyan, Mueller, Duval, & Witt, 2007). In order to controlfor patient severity, this study used patient age, perceivedhealth status, and prior year hospitalizations.

Methods

Design and Sample

A secondary data analysis was conducted with datafrom the Outcomes Research in Nursing Administra-tion Project II (ORNA II), a nonexperimental, longitudi-nal causal modeling study (Mark et al., 2007) that usedthe nursing unit as the unit of analysis. The ORNA IIstudy was undertaken to investigate relationships amongRN staffing adequacy, work environments, and organiza-tional and patient outcomes.

The ORNA II sample consisted of RNs and patients on286 general and specialty medical-surgical nursing unitsfrom 143 randomly selected acute care hospitals through-out the United States from the Joint Commission–accredited acute care facilities with at least 99 licensedbeds. Nurse managers in each nursing unit provided dataabout nursing turnover, LOS, patient falls, and medica-tion errors. All RNs in each nursing unit who had workedon that unit for at least 3 months were invited to partici-pate in the study. Ten patients who were 18 years of ageor older, able to speak English, and hospitalized on theunit for at least 48 hours were randomly selected fromeach participating unit to complete a patient satisfactionsurvey. ORNA II data collection began in 2003 and endedin 2004. Because of missing values for the selected vari-ables, the final dataset for the current study consisted of268 nursing units from 141 hospitals.

Data Collection

Data used for this study were collected over 6 consec-utive months with data on input variables collected priorto process and outcome variables. In order to supportthe temporal ordering implied by the conceptual frame-work, we used data from selected months. The data gath-ered on nursing unit turnover in January and Febru-ary were obtained prior to collection of the workgroupprocess data in March and patient outcomes (LOS, pa-tient falls, and medication errors) data in April, May,and June. Similarly, the nursing unit turnover data fromMarch and April were collected prior to obtaining dataon workgroup cohesion in May and patient satisfactiondata in June. This data collection time sequence allowed

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Bae et al. Impact of Nursing Unit Turnover

Table 1. Descriptive Statistics for Study Variables

Variables Definition Means SD ICC(1) Rwg

Nursing unit turnover

Nursing unit turnover % (Jan–Feb) Crude turnover rates during January and February 4.29 6.47

Nursing unit turnover % (Mar–Apr) Crude turnover rates during March and April 4.58 6.43

Workgroup processes

Workgroup cohesion Nurse job satisfaction scale (Sauter et al., 1997) 4.38 0.45 0.13 0.83

Coordination with other healthcare providers Relational coordination scale (Gittell, 2002) 3.64 0.20 0.09 0.99

Coordination with physicians and pharmacists 3.70 0.22 0.13 0.97

Workgroup learning Error orientation questionnaire (Rybowiak et al., 1999) 3.79 0.32 0.08 0.84

Patient outcomes

Patient satisfaction Patient satisfaction questionnaire (Bacon & Mark, 2009) 3.43 0.22 0.07 0.93

Average length of patient stay Total number of patient days/patient discharges 4.51 1.06

Patient falls Total number of patient falls per 1,000 patient days 4.03 2.36

Medication errors Total number of medication errors per 1,000 patient days 0.77 1.31

Control variables

Work complexity Work complexity (Salyer, 1996) 3.84 0.49 0.15 0.72

Unit size Total number of nursing unit beds 33.59 11.46

Hospital size Total number of maintained beds 346.55 188.13

Technological sophistication Saidin index 4.62 1.82

Teaching status Ratio of medical and dental residents to the number 0.13 0.25

of hospital maintained beds

Education level (proportion with BSN) Proportion of nurses with a bachelor’s degree or higher 0.37 0.194

Unit tenure (mo) Average months of nurses’ tenure on the unit 74.39 32.63

RN hours (%) Percentage of nursing care hours delivered by RNs 61.87 14.37

Patient age (yr) Average age 56.76 7.53

Health status Patients’ perceived health status (five categories) 3.46 0.44

Previous hospitalization Hospitalization (yes/no) 0.53 0.21

Note. N=268. SD, standard deviation; ICC, intraclass correlation; Rwg, interrater agreement.

us to examine causality in the turnover-process-outcomerelationship.

Measures

Variable definitions are displayed in Table 1. Ingeneral, measurement of variables was straightforward.However, the approach to input, process, outcome, andsome control variables requires additional explanation.

Input variables. Crude turnover rates of RNs oneach nursing unit constituted the main input variable.The formula for calculating turnover rates is a fraction,where the numerator is the total number of RNs who lefta nursing unit during a given period and the denominatoris the average number of RNs on staff in the unit over thesame period. In order to test the hypotheses, two func-tional forms of turnover rates were used: a linear functionand a dummy variable. We used a linear turnover termto test the model except for the nonlinear relationshipbetween turnover and workgroup learning, which wastested by a turnover dummy variable. Because of a dearthof research available on the beneficial levels of turnoveron workgroup learning, we constructed the dummy vari-able using five groups based on a sufficient sample size of

nursing units for each group. The five groups were cat-egorized as follows: zero (reference group), low, moderate,high, and very high. These categories were defined by rateranges: 0.00% (119 units), greater than 0.00% to 3.30%(24 units), 3.31% to 4.50% (24 units), 4.51% to 7.50%(49 units), and greater than 7.50% (52 units).

Process variables. Workgroup cohesion was mea-sured using four items of a cohesion subscale from theNurse Job Satisfaction Scale (Sauter et al., 1997) that as-sesses perceptions about how well nurses work togetherand get along, which meets the definition of workgroupcohesion and has been used in other studies (Chang,Hughes, & Mark, 2006). Principal axis factoring yieldeda single-factor solution with all items loading greaterthan 0.50. The internal consistency reliability of the fouritems in the current study was 0.76. Relational coor-dination was measured by the Relational CoordinationScale (Gittell, 2002) encompassing four communicationdimensions (frequent, timely, accurate, and problem-solving communication) and two relationship dimensions(shared goals and shared knowledge). Two variables werecreated: one to measure nurse perceptions about coor-dination with nine healthcare provider disciplines (at-tending MDs, house staff, physical therapists, respiratory

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Impact of Nursing Unit Turnover Bae et al.

therapists, laboratory technicians, case managers/socialworkers, pharmacists, radiologists, and dietary staff) anda second to measure coordination with physicians andpharmacists only. The latter variable was created becausecoordination with physicians and pharmacists was con-sidered the critical factor in preventing medication errors.Principal axis factoring yielded a single-factor solution forboth variables. Cronbach’s alphas for relational coordi-nation with other healthcare providers and for coordi-nation with physicians and pharmacists were 0.95 and0.87, respectively. In this study, the definition of work-group learning was limited to learning from errors andfailures associated with patient safety because we wereinterested in mistakes as an outcome. Workgroup learn-ing was measured using a subscale (5 items) from the Er-ror Orientation Questionnaire (Rybowiak, Garst, Frese,& Batinic, 1999) that measures the degree to which unitmembers actively think about and diagnose the sourceof errors. Principal axis factoring confirmed that the scalehad only one factor, with all five items having factor load-ings greater than 0.50. Cronbach’s alpha for this scale was0.92.

Outcome variables. Patient satisfaction includessatisfaction with overall courtesy, friendliness of the nurs-ing staff, and promptness of nursing assistance (Bacon &Mark, 2009). Patients completed a 13-item Likert-typequestionnaire with four response options. This scale hada Cronbach’s alpha of 0.92. Average LOS was defined asthe average number of inpatient days of care for patients(the total number of patient days in each unit dividedby patient discharges) on the nursing unit. A patient fallwas defined as an unplanned descent to the floor. Patientfalls were measured by the total number of patient fallsreported for each unit divided by the number of patientdays. Because errors resulting in severe outcomes are lesslikely than other errors to go unreported, this study useda measure of medication error frequency that includedonly those errors that required increased nursing obser-vation or medical intervention for patients. The rate ofpatient falls and medication errors were measured as thenumber of incidents per 1,000 patient days.

Control variables. Measurement of control vari-ables was straightforward. However, work complexityand technological sophistication need additional expla-nation. Work complexity was measured using a 7-itemLikert-type scale developed to measure perceived envi-ronmental uncertainty (Salyer, 1996). Within a nursingworkgroup, environmental uncertainty (e.g., types andvolumes of patients) may provide more complexity inworkgroup dynamics and work process among nurses.This scale measures work complexity in terms of the ex-tent of frequent interruptions or unanticipated events.Cronbach’s alpha for the scale in the current study was

0.85. Technological sophistication was measured by us-ing the Saidin Index (Spetz & Baker, 1999), which is theweighted sum of the number of technologies and servicesavailable in the hospital. The index increases more withthe addition of technologies that are relatively rare thanwith technologies that are more common.

Data Analysis

The unit of analysis in this study was the nurs-ing unit. Workgroup cohesion, relational coordination,workgroup learning, patient satisfaction, and work com-plexity were measured at the individual level andrequired aggregation to the unit level. Intraclass cor-relation coefficients [ICC(1)] and indices of interrateragreement (Rwg) were used to justify the aggregation oflower-level data to higher units of analysis. While theRwg is used in the event that observed group variancesdiffered from some theoretically expected random vari-ance, ICC(1) assesses how within-group variance con-trasts with between-group variance. The common thresh-old for such justification is an Rwg value equal to orgreater than 0.70, and a larger ICC(1) is generally ac-cepted as indicating a greater similarity among raters.Power was assessed using Cohen’s power tables. The re-alistically observed minimum effect size is any change inR-squared that ranges from 0.03 to 0.04. According toCohen (1988), these effect sizes lie between small (0.02)and medium (0.15). The current analysis with a samplesize of 268 units had sufficient power to capture the di-rect effects of turnover as well as the mediating effects ofworkgroup processes.

The current study used both linear and count mod-els, depending on the distribution of the process andoutcome variables. Using the Breusch-Pagen and Haus-man tests (Greene, 2003), ordinary least squares (OLS)estimators were compared to random and fixed effectsestimators. The specification tests strongly suggested thatthe clustering of nursing units within hospitals did notinfluence the effect of nursing unit turnover on work-group processes. Thus, a simple OLS model with robuststandard errors was used for the workgroup processmodels. After the specification tests, OLS estimates wereused for the patient satisfaction model. To account forthe clustering of nursing units within hospitals, averageLOS was estimated by using a random effects model. Forpatient falls and medication errors, a Poisson regressionmodel with an adjustment for over dispersion was used.

Results

The means, standard deviations, and, where appropri-ate, ICC(1) and Rwg for the study variables are presented

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Bae et al. Impact of Nursing Unit Turnover

Table 2. Effects of Nursing Unit Turnover on Workgroup Processes

Relational coordination Relational coordination

Workgroup with other with physicians Workgroup

cohesion healthcare providers and pharmacists learning

Nursing unit turnover (Jan–Feb) −0.003 −0.001

(0.002) (0.002)

Nursing unit turnover (Mar–Apr) −0.008

(0.005)

Turnover Jan–Feb=0% (reference group)

0<Turnover Jan–Feb≤3.3% −0.085

(0.058)

3.3<Turnover Jan–Feb≤4.5% −0.183∗∗

(0.060)

4.5<Turnover Jan–Feb≤7.5 −0.058

(0.052)

7.5<Turnover Jan–Feb −0.062

(0.059)

Control variables

Work complexity −0.145∗ −0.106∗∗ −0.130∗∗ −0.121∗∗

(0.062) (0.030) (0.032) (0.047)

Unit size −0.000 0.001 0.000 0.001

(0.002) (0.001) (0.001) (0.002)

Hospital size 0.000 0.000 0.000 0.000∗

(0.000) (0.000) (0.000) (0.000)

Technological sophistication 0.023 −0.018 −0.008 −0.019

(0.020) (0.010) (0.010) (0.014)

Teaching status −0.122 −0.082 −0.144∗ 0.040

(0.116) (0.058) (0.068) (0.107)

Education level 0.315∗ 0.071 0.007 0.089

(0.147) (0.071) (0.075) (0.130)

Unit tenure 0.001 0.000 0.000 0.001∗∗

(0.001) (0.000) (0.000) (0.001)

RN hours 0.002 0.001 0.001 0.001

(0.002) (0.001) (0.001) (0.001)

Constant 4.562∗∗ 4.005∗∗ 4.147∗∗ 4.091∗∗

(0.309) (0.131) (0.140) (0.218)

R-squared 0.107 0.145 0.149 0.125

F value 2.96∗∗ 4.69∗∗ 4.10∗∗ 3.02∗∗

Note. N=268. ∗Significant at .05; ∗∗significant at .01. Coefficient estimates presented and standard errors in parentheses.

in Table 1. Parameter estimates for each model are pro-vided in Tables 2 and 3.

Effects of Nursing Unit Turnoveron Workgroup Processes

Table 2 presents the effects of nursing unit turnoveron workgroup processes. The relationship between theworkgroup process variables and nursing turnover wasnot significant (workgroup cohesion: β=−0.008, p=.09;relational coordination with other healthcare providers:β=−0.003, p=.08). Our analysis suggested that nursingunits with turnover rates between 3.31% and 4.50%were likely to have lower levels of workgroup learning,by 0.18 points, than nursing units with 0% turnover.

However, this study did not find any significant differ-ence in workgroup learning between the reference groupand other turnover groups. In addition, work complexityhad a negative impact on all three workgroup processes.

Effects of Workgroup Processeson Patient Outcomes

Table 3 summarizes the effects of workgroupprocesses on patient outcomes. Workgroup cohesion(β=0.091, p<.01) and relational coordination with otherhealthcare providers (β=0.159, p=.03) were significantlyassociated with patient satisfaction in the separate mod-els. When we included workgroup cohesion and rela-tional coordination in a single model, only workgroup

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Impact of Nursing Unit Turnover Bae et al.

Table 3. Effects of Workgroup Processes on Patient Outcomes

Patient satisfaction Average length of patient stay Patient falls Medication errors

Workgroup cohesion 0.079∗∗

(0.028)

Relational coordination with other healthcare providers 0.115 0.486 −0.068

(0.071) (0.320) (0.187)

Relational coordination with physicians and pharmacists 0.873∗

(0.411)

Workgroup learning −0.026 −0.581∗

(0.113) (0.295)

Control variables

Work complexity −0.068∗ −0.102 −0.048 −0.133

(0.029) (0.132) (0.069) (0.160)

Unit size 0.002 −0.010 0.004 0.008

(0.001) (0.006) (0.003) (0.006)

Hospital size −0.000 0.002∗∗ −0.000 −0.001

(0.000) (0.000) (0.000) (0.001)

Technological sophistication 0.012 −0.063 −0.009 0.045

(0.010) (0.054) (0.023) (0.055)

Teaching status −0.035 −0.340 −0.378∗ −0.336

(0.061) (0.334) (0.163) (0.416)

Education level −0.183∗ 0.241 0.155 −1.239∗∗

(0.073) (0.347) (0.170) (0.430)

Unit tenure 0.001 −0.002 0.000 0.003

(0.000) (0.002) (0.001) (0.002)

RN hours 0.001 −0.006 0.002 0.006

(0.001) (0.005) (0.002) (0.005)

Patient age 0.006∗∗ 0.001 0.005 −0.011

(0.002) (0.009) (0.005) (0.010)

Health status 0.077∗ −0.476∗∗ −0.228∗∗ 0.105

(0.031) (0.145) (0.076) (0.174)

Previous hospitalization −0.028 0.027 −0.035 0.443

(0.070) (0.309) (0.165) (0.373)

Constant 2.221∗∗ 5.130∗∗ 2.228∗∗ −1.115

(0.356) (1.610) (0.842) (1.909)

R-squared 0.237 — — —

F value/Wald chi-squared 5.94∗∗ 40.08∗∗ — —

Note. N=268. ∗Significant at .05; ∗∗significant at .01. Coefficient estimates presented and standard errors in parentheses.

cohesion was significantly related to patient satisfaction.After assessing the relationships among cohesion, coordi-nation, and patient satisfaction, we found that relationalcoordination had a positive indirect impact on patient sat-isfaction through workgroup cohesion. The random ef-fects model for LOS revealed that relational coordinationwith other healthcare providers was not associated withaverage LOS. Relational coordination with physicians andpharmacists (β=0.873, p=.03) and workgroup learning(β=−0.581, p=.03) were significantly associated withmedication errors. But these workgroup process variableswere not associated with patient falls. In the model ofmedication errors, a 1-point increase in workgroup learn-ing led to a 44% decrease in medication errors. Contraryto our expectation, the relationship between relationalcoordination with physicians and pharmacists and medi-

cation errors was positive. A possible explanation for thisunexpected finding is that, in nursing units with higherlevels of relational coordination, serious medication er-rors might be more likely to be reported compared tounits with lower levels of coordination. In terms of con-trol variables, increased work complexity was associatedwith decreased patient satisfaction. Higher levels of nurseeducation were related to lower levels of medicationerrors and decreased patient satisfaction. Involvementin teaching was associated with lower rates of patientfalls.

Mediating Effects of Workgroup Processes

This study predicted that workgroup processes wouldmediate the effects of nursing unit turnover on patient

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Bae et al. Impact of Nursing Unit Turnover

outcomes. These mediation hypotheses required the test-ing of three equations: (a) the effects of nursing unitturnover on patient outcomes, (b) the effects of nurs-ing unit turnover on workgroup processes, and (c) thecombined effects of nursing unit turnover and workgroupprocesses on patient outcomes. To show mediation, allof these effects must be significant, and the significanceof the associations between nursing turnover and pa-tient outcomes must be reduced by adding workgroupprocesses to the model (Baron & Kenny, 1986). How-ever, nursing unit turnover was only related to patientfalls (β=−0.297, p=.02). The results suggested that nurs-ing units with turnover rates between 0% and 3.3% ofturnover were likely to have lower levels of patient falls,by a 24% decrease, than nursing units with 0% turnover.Thus, it was not possible to test mediating effects of work-group processes on the turnover-outcomes relationship.

Discussion

The most important finding of this study was the ad-verse impact of turnover on workgroup learning. Com-pared to nursing units with 0% turnover, units with be-tween 3.31% and 4.5% turnover during January andFebruary experienced lower levels of workgroup learn-ing. It is important to take into consideration the con-sequences associated with decreased quality resultingfrom a lack of workgroup effectiveness and learning. Aswe mentioned, previous turnover research does not ac-count for the underlying mechanism of the turnover-outcomes relationship, focusing instead on the direct re-lationship (Alexander et al., 1994; Castle & Engberg,2005; Voluntary Hospital Association Inc., 2002). Ourfindings provide empirical evidence elucidating one pro-cess by which turnover negatively affects patient out-comes. Further turnover research needs to focus onworkgroup processes as a consequence of turnover andan underlying mechanism of the turnover-outcomesrelationship.

The study findings also support the need to increaseworkgroup cohesion and coordination to improve patientsatisfaction. This finding is consistent with findings fromprevious research and emphasizes the importance for pa-tient satisfaction of positive affective and effective coor-dination among group members (Gittell, 2002; Meterkoet al., 2004). Similarly, this study found that nursing unitsscoring higher on workgroup learning had fewer medica-tion errors. This finding provides empirical evidence forcurrent research regarding patient safety such as devel-oping and sustaining nursing unit processes where nursesare encouraged to discuss and learn from their errors.Workgroup learning should receive further attention inresearch and practice to prevent medication errors.

Our study has several limitations. First, the proposedmodel assumed a lagged impact of turnover on processesand outcomes, which implies that turnover affects rela-tional coordination a few months after turnover occurs.The 2-month period of time for collection of turnoverdata may not have been sufficient to assess true varia-tion in and levels of turnover. Also, there is no way ofknowing whether those 2 months represent typical pat-terns of nursing turnover. Alternatively, it is possible thatthe effect of turnover on workgroup processes is contem-poraneous rather than lagged, that high levels of nurs-ing turnover undermine workgroup cohesion at the sametime that turnover occurs, and this contemporaneous ef-fect was not explored in this study. Future studies shouldemploy a longitudinal design with repeated measures ofall variables. This would lead to knowledge regarding theperiod of time in which nursing turnover is likely to resultin less effective workgroup processes.

Another limitation stems from missing variables thatmight affect turnover, workgroup process, and patientoutcomes. For example, managers’ support and supervi-sion have been found to influence workgroup processesand organizational effectiveness (Kozlowski et al., 2003)and also are associated with nurse intent to stay (Kovneret al., 2006). Further research on turnover needs to ac-count for the role of managers in the model. This studyused patient age, previous hospitalization, and perceivedhealth status to control patient acuity. In future research,patient acuity needs to be controlled by a more compre-hensive method.

Additionally, future turnover researchers may considerthe use of a moderator, a concept distinct from a media-tor. The fundamental assumption of the IPO framework isthat workgroup processes are the underlying mechanismsmediating the impact of nursing unit turnover on patientoutcomes. Moderators affect the direction and strength ofthe turnover-outcomes relationship and could be used toexplore the turnover-outcomes relationship and provideinsight into the characteristics of the most at-risk nursingunits.

Conclusions

The current instability in the nursing workforce im-plies adverse impacts on the continuity and quality ofpatient care. Research to examine and to better articu-late the processes and outcomes associated with nurs-ing turnover will be crucial if healthcare organizationsare to meet these challenges under shortage conditions.The results of this study should encourage further re-search focused on how nursing unit turnover affectsworkgroup processes and patient outcomes. Future work

47

Impact of Nursing Unit Turnover Bae et al.

related to the impact of nursing turnover on various out-comes may provide frontline nurse managers with prac-tical information needed to address the challenges ofturnover.

Acknowledgments

This work was supported by grant 5R01NR003149from the National Institute of Nursing Research and waspartially funded by the Graduate School at the Universityof North Carolina at Chapel Hill through a DissertationCompletion Research Fellowship.

Clinical Resources� Nursing Shortage Fact Sheet, American Associa-

tion of Colleges of Nursing. http://www.aacn.nche.edu/Media/FactSheets/NursingShortage.htm

� State Nursing Workforce Reports, American Asso-ciation of Colleges of Nursing. http://www.aacn.nche.edu/Media/NsgWrkFrcReps.htm

References

Alexander, J.A., Bloom, J.R., & Nuchols, B.A. (1994). Nursing

turnover and hospital efficiency: An organization-level

analysis. Industrial Relations, 33(4), 505.

Argote, L., Insko, C.A., Yovetich, N., & Romero, A.A. (1995).

Group learning curves: The effects of turnover and task

complexity on group performance. Journal of Applied Social

Psychology, 25(6), 512–529.

Ayanian, J.Z., & Weissman, J.S. (2002). Teaching hospitals

and quality of care: A review of the literature. Milbank

Quarterly, 80(3), 569–593.

Bacon, C.T., & Mark, B. (2009). Organizational effects on

patient satisfaction in hospital medical-surgical units.

Journal of Nursing Administration, 39(5), 220–227.

Baron, R.M., & Kenny, D.A. (1986). The moderator-mediator

variable distinction in social psychological research:

Conceptual, strategic, and statistical considerations. Journal

of Personality and Social Psychology, 51, 1173–1182.

Bates, D.W., Spell, N., Cullen, D.J., Burdick, E., Laird, N.,

Petersen, L.A., et al. (1997). The costs of adverse drug

events in hospitalized patients. Adverse drug events

prevention study group. JAMA, 277(4), 307–311.

Brewer, C.S., Kovner, C.T., Greene, W., & Cheng, Y. (2009).

Predictors of RNs’ intent to work and work decisions 1 year

later in a U.S. national sample. International Journal of

Nursing Studies, 46(7), 940–956.

Carley, K. (1992). Organizational learning and personnel

turnover. Organization Science, 3(1), 20–46.

Castle, N., & Engberg, J. (2005). Staff turnover and quality of

care in nursing homes. Medical Care, 43(6), 616–626.

Chang, Y.K., Hughes, L.D., & Mark, B. (2006), Fitting in or

standing out: Nursing workgroup diversity and unit-level

outcomes. Nursing Research, 55(6), 373–380.

Cohen, J. (1988). Statistical power analysis for the behavioral

sciences (2nd ed.). Hillsdale, NJ: L. Erlbaum Associates.

Dunton, N., Gajewski, B., Taunton, R.L., & Moore, J. (2004).

Nurse staffing and patient falls on acute care hospital units.

Nursing Outlook, 52(1), 53–59.

Edmondson, A.C. (1999). Psychological safety and learning

behavior in work teams. Administrative Science Quarterly,

44(2), 350–383.

Ervin, N.E. (2006). Does patient satisfaction contribute to

nursing care quality? Journal of Nursing Administration,

36(3), 126–130.

Gittell, J.H. (2002). Coordinating mechanisms in care

provider groups: Relational coordination as a mediator and

input uncertainty as a moderator of performance effects.

Management Science, 48(11), 1408–1426.

Greene, W.H. (2003). Econometric analysis. Upper Saddle River,

NJ: Prentice Hall.

Halter, C.W. (2006). Influence of hospital context on cost and

length of stay following cardiac catheterization. Nursing

Economic, 24(5), 246–252.

Hedberg, B. (1981). How organizations learn and unlearn. In

P. Nystrom & W. Starbuck (Eds.), Handbook of organizational

design (pp. 3–27). Oxford, UK: Oxford University Press.

Kaissi, A. (2006). An organizational approach to

understanding patient safety and medical errors. Health

Care Manager, 25(4), 292–305.

Kane, R.L., Shamliyan, T., Mueller, C., Duval, S., & Witt, T.

(2007). Nursing staffing and quality of patient care. AHRQ

publication 07-E005. Rockville, MD: Agency for Healthcare

Research and Quality.

Kopp, B.J., Erstad, B.L., Allen, M.E., Theodorou, A.A., &

Priestley, G. (2006). Medication errors and adverse drug

events in an intensive care unit: Direct observation

approach for detection. Critical Care Medicine, 34(2),

415–425.

Kovner, C.T., Brewer, C.S., Greene, W., & Fairchild, S.

(2009). Understanding new registered nurses’ intent to

stay at their jobs. Nursing Economics, 27(2), 81–98.

Kozlowski, S.W.J., Steve, W.J., & Bell, B.S. (2003). Work

groups and teams in organization. In W.C. Borman, D.R.

Ilgen, & R.J. Klimoski (Eds.), Comprehensive handbook of

psychology: Industrial and organizational psychology. New York:

Wiley.

Kuhn, E.M., Hartz, A.J., Gottlieb, M.S., & Rimm, A.A. (1991).

The relationship of hospital characteristics and the results

of peer review in six large states. Medical Care, 29(10),

1028–1038.

Mark, B.A., Hughes, L.C., Belyear, M., Chang, Y., Hofmann,

D., Jones, C. B., et al. (2007). Does safety climate moderate

the influence of staffing adequacy and work conditions on

nursing injuries? Journal of Safety Research, 38(4), 431–

446.

48

Bae et al. Impact of Nursing Unit Turnover

Mark, B.A., Salyer, J., & Wan, T.T.H. (2003). Professional

nursing practice: Impact on organizational and patient

outcomes. Journal of Nursing Administration, 33(4), 224–234.

McGrath, J.E. (1964). Social psychology: A brief introduction.

New York: Holt, Rinehart, & Winston.

Meterko, M., Mohr, D.C., & Young, G.J. (2004). Teamwork

culture and patient satisfaction in hospitals. Medical Care,

42(5), 492–498.

Mrayyan, M.T. (2006). Jordanian nurses’ job satisfaction,

patients’ satisfaction and quality of nursing care.

International Nursing Review, 53, 224–230.

Mullen, B., & Cooper, C.L. (1994). The relationship between

group cohesiveness and performance: An integration.

Psychosocial Bulletin, 115, 210–227.

Price, J.L. (1977). The study of turnover. Ames, IA: Iowa State

University Press.

Rybowiak, V., Garst, H., Frese, M., & Batinic, B. (1999). Error

orientation questionnaire (EOQ): Reliability, validity, and

different language equivalence. Journal of Organizational

Behavior, 20(4), 527–547.

Salyer, J. (1996). Development and psychometric evaluation

of an instrument to measure staff nurses’ perception of

uncertainty in the hospital environment. Journal of Nursing

Measurement, 4(1), 33–48.

Sauter, M., Boyle, D., Wallace, D., Andrews, J., Johnson, M.,

Bates, M., et al. (1997). Psychometric evaluation of the

organizational job satisfaction scale. Journal of Nursing

Measurement, 5(1), 53–69.

Schmidt, I.K., & Svarstad, B.L. (2002). Nurse-physician

communication and quality of drug use in Swedish nursing

homes. Social Science & Medicine, 54(12), 1767–1777.

Spetz, J., & Baker, L.C. (1999). Managed care, hospital

competition, and the availability of technology: Implications for

health care costs. San Francisco, CA: Public Policy Institute of

California.

Staw, B.M. (1980). The consequences of turnover. Journal of

Occupational Behaviour, 1(4), 253–273.

Voluntary Hospital Association Inc. (2002). The business case for

work force stability. No. 2002 VHA Research Series (7).

Irving, TX. Retrieved November 9, 2009, from

http://www.studergroup.com/tools/selection andretention/

TheBusinessCaseforWorkforceStability.pdf

49

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