Burnout during nursing education predicts lower occupational preparedness and future clinical performance: A longitudinal study

International Journal of Nursing Studies 49 (2012) 988–1001

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Burnout during nursing education predicts lower occupational preparedness and future clinical performance: A longitudinal study Ann Rudman *, J. Petter Gustavsson Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden

A R T I C L E I N F O

A B S T R A C T

Article history: Received 15 June 2011 Received in revised form 17 March 2012 Accepted 29 March 2012

Background: Early-career burnout among nurses can influence health and professional development, as well as quality of care. However, the prospective occupational consequences of study burnout have not previously been investigated in a national sample using a longitudinal design. Objectives: To prospectively monitor study burnout for a national sample of nursing students during their years in higher education and at follow-up 1 year post graduation. Further, to relate the possible development of study burnout to prospective health and life outcomes, as well as student and occupational outcomes. Design: A longitudinal cohort of Swedish nursing students (within the population-based LANE (Longitudinal Analysis of Nursing Education/Entry) study) from all sites of education in Sweden was surveyed annually. Data were collected at four points in time over 4 years: three times during higher education and 1 year post graduation. Participants: : A longitudinal sample of 1702 respondents was prospectively followed from late autumn 2002 to spring 2006. Methods: Mean level changes of study burnout (as measured by the Oldenburg Burnout Inventory, i.e. the Exhaustion and Disengagement subscales) across time, as well as prospective effects of baseline study burnout and changes in study burnout levels, were estimated using Latent Growth Curve Modeling. Results: An increase in study burnout (from 30% to 41%) across 3 years in higher education was found, and levels of both Exhaustion and Disengagement increased significantly across the years in education (p < 0.001). Baseline levels, as well as development of study burnout, predicted lower levels of in-class learner engagement and occupational preparedness in the final year. At follow-up 1 year post graduation, earlier development of study burnout was related to lower mastery of occupational tasks, less research utilization in everyday clinical practice and higher turnover intentions. Conclusions: The results suggest that study burnout may have interfered with learning and psychological well-being. Aspects related to work skills and intention to leave the profession were also affected. Thus, burnout development during higher education may be an important concern, and effective preventive measures to counteract burnout development may be necessary already at the outset of nursing education. ß 2012 Elsevier Ltd. All rights reserved.

Keywords: Burnout Models Prospective studies Stress Students Nursing

What is already known about the topic? * Corresponding author at: Karolinska Institutet, Department of Clinical Neuroscience, Division of Psychology, SE-171 77 Stockholm, Sweden. Tel.: +46 8 524 83928. E-mail address: [email protected] (A. Rudman). 0020-7489/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijnurstu.2012.03.010

 Earlier studies have indicated that nursing students experience increased levels of distress during their education.

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 Prospective occupational consequences of stress or study burnout have not previously been investigated in a longitudinal design. What this paper adds  In a national sample of nursing students, a significant increase in study burnout across 3 years in higher education was found.  Study burnout development predicted poorer health at graduation and 1 year post graduation.  Study burnout development predicted lower mastery of nurse-specific tasks and lower use of evidence-based practice 1 year post graduation. 1. Introduction Given the global shortage of nurses and the ambition to ensure safe and high quality care, it is important to maintain a healthy nursing workforce (Nooney et al., 2010; Poghosyan et al., 2010; Timmins et al., 2011). Various threats to maintaining a healthy and productive nursing workforce have been documented; for instance, a dissatisfying work environment with high exposure to stress (Leiter and Spence Laschinger, 2006), patient safety issues, poor opportunities for professional development, work– family imbalance and unsatisfactory employment factors (e.g. management, wages, working hours) (Bowles and Candela, 2005; Brewer et al., 2009; Flinkman et al., 2008). Although it is well-known that nursing is an occupation that involves many stressful situations (McVicar, 2003), there is far less discussion of the fact that stress is prevalent already in undergraduate nursing education (Lo, 2002; Watson et al., 2009). During their education, students experience stress in relation to both the academic and the clinical components of the nursing program (Timmins and Kaliszer, 2002). The magnitude of stressrelated problems during education has not yet been reported in population-based samples; however, stress levels have been found to increase across nursing education (Deary et al., 2003; Edwards et al., 2010; Watson et al., 2008). 1.1. Stress and burnout symptoms It has already been shown that prolonged exposure or extreme levels of stress can lead to health consequences such as burnout (Shirom, 2011). Broadly speaking, burnout can be viewed as representing the accumulated effects of different types of stressors that a workforce is exposed to, or more specifically, as Shirom phrased it, ‘‘an affective reaction to on-going stress whose core content is the gradual depletion over time of individuals’ intrinsic energetic resources’’ p. 223 (Shirom, 2011). This definition illustrates that exhaustion constitutes one basic component in burnout. Another important component of burnout is disengagement (depersonalization or cynicism) representing an action ‘‘to distance oneself emotionally and cognitively from one’s work’’ p. 403 (Maslach et al., 2001). According to the demands–resource model of burnout (Bakker and Demerouti, 2007), the two components,

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Exhaustion and Disengagement, constitute the core dimensions of the burnout syndrome. 1.2. Stress and socialization Some hypotheses have been generated to understand the stress process that potentially leads to burnout in new graduate nurses. One such hypothesis, according to the first studies on burnout, argues that transition and socialization into the professional role itself encompasses many stressful experiences related to the struggle of mastering new skills and adapting to the new role and work environment (Cherniss, 1980; Kramer, 1974). Professional socialization is outlined as ‘‘the process whereby the attitudes, values, knowledge and skills which characterize a profession are gradually assimilated by (prospective) practitioners’’ p. 809 (Pilhammar Andersson, 1993), and at the same time this process is recognized as career-long (MacIntosh, 2003), starting during education (Messersmith, 2008). Among new graduates, problems in this socialization struggle have been referred to as ‘‘reality shock’’ (Kramer, 1974), ‘‘the crisis of competence’’ (Cherniss, 1980) and ‘‘transition shock’’ (Duchscher, 2009). This ‘‘crisis’’ or ‘‘shock’’ involves the process when a new graduate is confronted for the first time with a wide range of physical, intellectual, emotional, developmental and socio-cultural changes in relation to his/her new professional role (Duchscher, 2009). In the case of nursing students it is proposed that the duality of maintaining both student and healthcare professional roles simultaneously adds to the stressful experiences by establishing role uncertainty and role conflict (Melia, 1984; Messersmith, 2008; Startup and Wilson, 1992). There may also be a challenge here if the nursing student or new graduate fails to bridge the gap between undergraduate educational curricula and workplace expectations (Duchscher, 2009). 1.3. Stress and burnout among nursing students and new graduates In line with the first reports on stress and burnout, findings in recent studies also confirm that burnout is frequent and imminent early on in the career (Laschinger et al., 2009; Rudman and Gustavsson, 2011). The prevalence of recently qualified nurses reporting stress and burnout symptoms within their first years of practice has ranged between 20% and 60% (Cho et al., 2006; Laschinger et al., 2009; Rudman and Gustavsson, 2011). According to the notion that socialization into the professional role starts already during education (MacIntosh, 2003) and that this process is stressful in itself, it is important to study indicators of stress during education. In the few studies that have prospectively investigated prevalence and longitudinal development of nursing student stress, similar results are reported, i.e. increased levels of distress during education. For instance, Watson and colleagues found that general stress symptoms increased from the outset of education in various samples of nursing students (Deary et al., 2003; Watson et al., 2008). More specifically they found an initial increase in stress symptoms at the start (Watson et al., 2008) and an intensification of

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symptoms from the middle to the end of education (Deary et al., 2003). Correspondingly, Edwards and colleagues found increased stress levels throughout education, with the lowest levels at baseline (approximately 8 months after study entrance) in a prospective longitudinal study of students from an entire year of entry cohort at one university (Edwards et al., 2010). A Norwegian study looked at the pattern of increased stress in nursing students, as compared with students within occupational therapy and physiotherapy (Nerdrum et al., 2009). In their study, the nurses’ stress scores increased significantly over time, whereas no similar increase was found among the other student groups. Common limitations addressed in previous studies are the lack of a larger and representative sample from multiple universities, with a longer time frame and a follow-up in clinical practice (Deary et al., 2003; Edwards et al., 2010; Watson et al., 2008). In addition, some studies acknowledge the need for more modern methods to enable data to be modeled longitudinally in a more appropriate way (Burisch, 2002). Thus, in the light of previous research on the development of stress and burnout among nursing students, it was recognized that there was a need to prospectively examine study burnout development across all years in higher education for a representative sample of nursing students. The objective of the present study was therefore to prospectively monitor study burnout development across all years in higher education for a sample of nursing students in a national cohort, and relate the possible development to prospective health and life outcomes, as well as student and occupational outcomes, at follow-up in the first post-graduation year. More specifically the research questions were: 1. Can a distinct trend of study burnout development be identified during nursing education? 2. If detected, does the development of study burnout affect health and life outcomes? 3. If detected, does the development of study burnout affect student outcomes? 4. If detected, does the development of study burnout affect future professional outcomes?

1.4. Study context: Swedish nursing education In many countries, including Sweden, contemporary nursing education has shifted from being an apprenticeship system of training, with practically oriented education, to an academic bachelor’s degree program (Kapborg, 1998). In Sweden, the system of nursing education has undergone major structural changes (initiated in 1977) and has gradually led to an academic perspective on educational content and methods (Swedish National Agency for Higher Education, 1997). Today, all students undertaking nursing education at Swedish universities are enrolled for a 3-year program, and the curricula for these programs now also include classes in research methods and a move toward more self-directed learning (Fura˚ker, 2001). Over the years, the number of students participating

in these academic nursing programs has increased. For example, between 2000 and 2005, the number of places on such programs expanded from 3000 to 4500 (The National Board of Health and Welfare, 2007). 2. Methods 2.1. Design and participants Data from one prospective cohort of 1702 Swedish nursing students within the national LANE (Longitudinal Analysis of Nursing Education/Entry) (Rudman et al., 2010) study were used to investigate the primary outcome for this report – burnout during higher education – over the 3 years of higher education and at follow-up after 1 year of practice as a nurse. Students were recruited in their first year (i.e. second semester), in the autumn of 2002, from each of the 26 universities providing undergraduate nursing programs in Sweden. Their estimated time point for graduation was December 2004 and the cohort was therefore called EX2004 (EX = examination). In total, 2331 nursing students were invited to participate, and 1702 (73%) nursing students gave their informed consent (Fig. 1). At the first data collection the nursing students were on average 28 (SD 7) years old (ranging from 20 to 52 years). The majority were female (91%), of Swedish background (91%) and had previous experience in the field of healthcare (60%). To examine representativeness, the EX2004 cohort was compared with the total population of Swedish nurses who graduated in the same year. Six different demographic variables from population-based national registers were tested, namely age, gender, country of birth, residency (large city), marital status and parenthood. The only difference that was found concerned the proportion of participating females, which was 1% higher (89% vs. 88%) than among all the nurses who graduated in 2004. Longitudinal data were self-reported and collected through annual postal surveys. For the purpose of the present study, data from the first year (n = 1697, November 2002); second year (n = 1567, November 2003); third and final year (n = 1418, November 2004) of nursing education; and 1 year (n = 1401, February 2006) post graduation were used (Fig. 1). Common patterns of missing data comprised non-response in one of four data collections (n = 254) and subsequent non-response after each of the first two data collections during higher education (n = 162; 70 + 92, respectively). For more information on assumptions and possible consequences of missing data, see below. 2.2. Instruments/measurement 2.2.1. Burnout For longitudinal analyses of development of stress reactions across nursing education, the present paper utilizes the Oldenburg Burnout Inventory (Demerouti et al., 2002; Halbesleben and Demerouti, 2005). This inventory includes 16 items (8 positively and 8 negatively framed) which from the perspective of the demands– resource model of burnout (Bakker and Demerouti, 2007) assess the two core dimensions of burnout, namely

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Eligible students n=2331 Did not consent n=629

Consented to participate n=1702 Losses after consent n=2 Reasons: consented but never participated st

Participation at baseline, at 1 wave (i.e. duringfirst year of education in late autumn 2002) n=1697

nd

Participants at 2 wave (i.e. duringsecond year of education in late autumn2003) n=1567

rd

Participants at 3 wave (i.e. duringthird year of education in late autumn 2004) n=1418

th

Participants at 4 wave (i.e. after the first year of practice in early spring 2006) n=1401

Fig. 1. The LANE EX2004 cohort. Flow chart of recruitment and participation.

Exhaustion and Disengagement. The items in the Oldenburg Burnout Inventory are listed in Halbesleben and Demerouti (2005). The Swedish version was translated from both the original German version and from an approved English version. Back translation was performed and approved by the original constructor (Dahlin, 2007; Peterson, 2008). During their years in education, respondents reported their level of exhaustion by answering eight items (with four response categories, i.e. 1 ‘‘does not apply at all’’, 2 ‘‘does not apply very well’’, 3 ‘‘applies to a certain extent’’ and 4 ‘‘applies completely’’) reflecting if they felt emotionally drained or if they needed a longer time for rest. A mean value across all items was computed for each individual, with resulting scale scores ranging from 1 to 4 (i.e. 1 = not exhausted and 4 = completely exhausted). Cronbach’s alpha ranged between 0.84 and 0.86 across the study years. In a similar way, respondents reported their level of disengagement by answering eight items (with the same response categories as above) reflecting whether they devalued their education, or if they felt fed up with their course work and assignments. A mean value

across all items was computed for each individual with resulting scale scores ranging from 1 to 4 (i.e. 1 = not disengaged and 4 = completely disengaged). Regarding working life, a short-form version of the Oldenburg Burnout Inventory was used. This short-form comprises a five-item version of each scale, which was developed (using confirmatory factor analyses) as part of a PhD project (Peterson, 2008) in close collaboration with the original author (in the Oldenburg Burnout Inventory code as presented by Halbesleben and Demerouti (2005), these items correspond to D1, D2, D3, D5 and D8; E2, E3, E5, E7 and E8). The suggested two-factor structure of the Swedish translation of the Oldenburg Burnout Inventory has been supported by means of confirmatory factor analysis (Peterson et al., 2011). Moreover, it has been used in a large-scale public health study where it was found to predict future sickness absence (Peterson et al., 2011). It was also found to be sensitive to change when used as an outcome measure in a randomized controlled stress intervention trial (Peterson et al., 2008a,b). In addition, the Oldenburg Burnout Inventory has previously been

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Table 1 Descriptive statistics for both longitudinal and outcome variables. Instrument/scale

Year

% Internal dropout

M

SD

Oldenburg Burnout Inventory Exhaustion

1st (education) 2nd (education) 3rd (education) 1st (working life)

0.1 0.8 0.7 3.3

2.28 2.36 2.37 2.37

0.55 0.58 0.57 0.60

Oldenburg Burnout Inventory Disengagement short-form

1st (education) 2nd (education) 3rd (education) 1st (working life)

0.1 0.8 0.7 3.3

2.06 2.17 2.23 1.81

0.50 0.52 0.54 0.50

NSSE In-class learner engagement Occupational preparedness

3rd (education) 3rd (education)

1.0 0.8

2.16 4.65

0.79 1.43

Major Depression Inventory: depressive symptoms

3rd (education) 1st (working life)

0.5 0.6

7.91 6.90

4.52 4.23

SWLS Satisfaction with life

3rd (education) 1st (working life)

0.7 0.3

3.84 3.95

0.76 0.72

Occupational turnover intentions Questionnaire for Psychological and Social factors at work: mastery of occupational tasks Research use

1st (working life) 1st (working life)

2.4 2.7

1.36 2.94

0.68 0.55

1st (working life)

2.6

2.94

1.49

Oldenburg Burnout Inventory Exhaustion short-form Oldenburg Burnout Inventory Disengagement

applied when monitoring psychological health among medical students (Dahlin et al., 2007; Dahlin and Runeson, 2007). In the present study, Cronbach’s alpha ranged between 0.76 and 0.79 across the study years. In addition, Cronbach’s alpha was 0.78 and 0.76 for the short-form Exhaustion and Disengagement scales, respectively. Descriptive data for the Oldenburg Burnout Inventory scales across all data collections are given in Table 1. In accordance with the majority of previously published studies on burnout (Shirom, 2011), analyses were performed on each of the burnout dimensions, separately. However, in order to estimate the prevalence of burnout across nursing education, we applied cut-off values suggested by the constructors of the instrument and previously used in a Swedish study (Peterson et al., 2008a,b). Accordingly, the presence of burnout symptoms was defined as the combination of high scores on the Exhaustion scale (over 2.25) and the Disengagement scale (over 2.10). In order to evaluate the possible impact of burnout development during education, a number of health, life, student and occupational outcomes were used, measured in both the final year of education and the 1 year post graduation. Variables were chosen to reflect symptoms and outcomes commonly found or hypothesized to be consequences (or concomitants) of burnout (Schaufeli and Enzmann, 1998). Using the review by Schaufeli and Enzmann (1998), variables were chosen to reflect consequences at different levels (from the individual level to the organizational level). Thus, instruments were chosen to measure health problems (depression) and interference with private life (life satisfaction), as well as work orientation (in class-learner engagement and mastery of occupational tasks) and performance (occupational preparedness and research use in everyday clinical practice). For each multi-time scale used, a mean value across all items in a scale was computed for each individual. The

instruments are described below and descriptive data are given in Table 1. 2.2.2. Student outcomes The possible impact of burnout development during higher education on student outcomes in the final year of education was assessed by instruments measuring in-class learner engagement and overall readiness for nursing practice. The respondents rated their level of in-class learner engagement during the last year by answering items from the Active Learning scale taken from the American National Survey for Student Engagement (Kuh, 2004). Two items (with response categories ranging from 1 ‘‘does not apply at all’’ to 4 ‘‘applies completely’’), reflecting spontaneous contribution to discussions and asking questions in class, were used to form a measure of in-class learner engagement. The two items have previously been found to be highly correlated and to reflect expected longitudinal changes in activity across the years in higher education (Bruce et al., 2010). In the present study, Cronbach’s alpha was 0.75 in the final year of nursing education. In addition to the measurement of in-class learner engagement, the respondents rated their level of occupational preparedness by responding to the item ‘‘As a result of my nursing education I am well prepared to manage my future work as a nurse’’. This item has seven response categories, ranging from 1 ‘‘fully disagree’’ to 7 ‘‘fully agree’’. This single item was developed and used in a previous longitudinal study on occupational socialization among university students (Hagstro¨m and Kjellberg, 2000). The item has previously been used to compare outcome across different universities (Schu¨ldt-Ha˚a˚rd et al., 2008) and also found to be predictive of future job stress (Rudman and Gustavsson, 2011). 2.2.3. Occupational outcomes The possible impact of burnout development during higher education on outcomes in working life 1 year post

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graduation was measured by instruments assessing intention to leave the profession, individual mastery of occupational tasks and research utilization in everyday clinical practice. The respondents rated their level of mastery of occupational tasks by answering the Mastery scale from the Questionnaire for Psychological and Social factors at work (QPS Nordic), developed on behalf of the Nordic Council of Ministers and published in four Nordic languages (Dallner et al., 2000; Lindstro¨m et al., 1997). The scale comprises four items with response categories ranging from 1 ‘‘seldom/never’’ to 5 ‘‘very often/always’’. The Swedish version has been validated using confirmatory factor analysis and found both to measure a unidimensional construct and to be invariant over different occupational groups (Wa¨nnstro¨m et al., 2009). In a recent study of newly graduated teachers, the scale was found to be associated with work engagement (Hultell and Gustavsson, 2011). In the present study, Cronbach’s alpha was 0.74 1 year post graduation. Moreover, the respondents rated their level of occupational turnover intentions by answering the Turnover Intention scale (Sjo¨berg and Sverke, 2000), which consists of three items with five response categories, ranging from 1‘‘fully agree’’ to 5 ‘‘fully disagree’’. The scale has prospectively been found to predict actual turnover (Sjo¨berg and Sverke, 2000). In the present study, Cronbach’s alpha was 0.81 1 year post graduation. Finally, extent of research use in everyday clinical practice was assessed by one of the three single items comprising Estabrooks’ instrument (Estabrooks, 1999) for measuring different aspects of research utilization (called the Kinds of Research Use instrument). This instrument has recently been translated and adapted for use in Swedish healthcare settings (Forsman et al., 2009). The item tapping instrumental (or direct) research use was used in the present paper. This single item was initiated with a definition of the concept, followed by three examples of research use exemplifying the current concept. Respondents were asked to indicate how often they had used research in this way over the past 4 weeks. The item was scored on a five-point response scale, from 1 ‘‘never’’ to 5 ‘‘nearly every shift’’. According to a recent meta-analysis on measurement properties of research utilization instruments (Squires et al., 2011), the Kinds of Research Use instrument has been tested for content validity, response process validity and shown significant relationships with variables that theoretically or empirically have been shown to link to research use. 2.2.4. Health/life outcomes The possible impacts of burnout development during higher education on psychological health were measured in the final year of education and 1 year post graduation, using an instrument to assess depressive symptoms. Respondents rated their level of depressive symptoms by answering the Major Depression Inventory, which includes 12 items capturing the core symptoms of major depressive disorder (Bech et al., 2001; Olsen et al., 2003). We used the Swedish version with four response categories (1 ‘‘never’’, 2 ‘‘some of the time’’, 3 ‘‘most of

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the time’’ and 4 ‘‘all of the time’’), indicating frequency of depressive symptoms during the past 2 weeks (Dahlberg et al., 2007). The unidimensionality of the Major Depression Inventory was previously found to be acceptable (Olsen et al., 2003) and the Major Depression Inventory scale showed high correlation with clinician-rated symptom severity (r = 0.86 with Hamilton Depression Rating Scale). The Swedish version has previously been used in health services research (Dahlberg et al., 2007; Forsell, 2004) and in research on mental health among medical students (Dahlin and Runeson, 2007). Longitudinal data monitoring the development of depressive symptoms across nursing education (from the same LANE cohort as used here) have previously been reported elsewhere (Christensson et al., 2010). In the present study, Cronbach’s alpha was 0.82 both in the final year of nursing education and 1 year post graduation. In addition, the possible impact of burnout development during higher education on life outcome was measured in the final year of education and 1 year post graduation, using an instrument to assess life satisfaction. The respondents rated their level of life satisfaction by completing the Satisfaction with Life Scale (Diener et al., 1999), which consists of five items (with five response categories, ranging from 1 ‘‘fully disagree’’ to 5 ‘‘fully agree’’). The Swedish version has been validated using confirmatory factor analyses and found both to measure a unidimensional construct and to be invariant over groups of university students (Hultell and Gustavsson, 2008). In the present study, Cronbach’s alpha was 0.90 in the final year of nursing education and 0.89 1 year post graduation. 2.3. Analysis of longitudinal data The last 10–20 years have seen a dramatic change in how to analyze longitudinal data (regardless of whether it comes from clinical trials or observational studies). Data analyses based on the Analysis of Variance (ANOVA) framework have given way to methods where predictors and consequences of longitudinal change can be modeled with greater flexibility, and missingness can be handled more efficiently (Maxwell and Tiberio, 2007). In the present paper, the longitudinal analysis applied the multilevel model (also called the linear mixed model) for change, implemented as latent growth curve modeling in the structural equation modeling framework (SEM). A detailed description of both the multilevel model of change (including assumptions and equations) and its implementation in structural equation modeling are given elsewhere (Singer and Willett, 2003). A more complete and technical treatment of the structural equation modeling implementation is given elsewhere (Bollen and Curran, 2006). Growth curve modeling can be used to estimate a linear trajectory for the entire sample (i.e. to estimate an intercept and a slope describing the development of study burnout across time), while at the same time estimating the influence of a latent factor explaining individual variability in initial levels of study burnout in the first year of education (i.e. individual differences around the intercept) and a latent factor explaining individual variability in the rate of change across time (i.e. individual

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differences around the slope). In addition, the association (i.e. the covariance) between these two latent factors can be estimated. When testing longitudinal models it is recommended that model testing should progress cumulatively, beginning with the simplest growth model and building further models upon this, including parameters from previous simpler models (Preacher et al., 2008). In the present paper, model testing progressed in three steps. First, the simplest growth model was fitted to the data (Model 1, the random intercept model). This model estimates a baseline mean value for study burnout for the total group and tests if a latent intercept variable is the only needed to explain the longitudinal data pattern (i.e. if individuals’ baseline study burnout levels remain stable across time). Accordingly, two growth parameters were estimated in this model: one fixed (the baseline mean value) and one random (the individual variability around this mean value) effect. If this model does not fit the data well, a second model is tested, and a general effect of time is added to the previous model. This second model (Model 2, the random intercept/fixed slope model) tests if there is a general increase (or decrease) in study burnout, added to all individuals’ baseline values. Accordingly, this model estimates three growth parameters: two fixed (the baseline mean value and the slope) and one random (the individual variability around this mean value) effect. If this model does not fit the data, a final model, Model 3, the random intercept/random slope model, is tested. This model tests if an additional second latent variable, addressing individual differences in rate of linear change, is needed to explain the longitudinal data pattern. Thus, this model estimates four growth parameters: two fixed (the baseline mean value and the slope) and two random (the individual variability around this mean value and the individual variability around the slope) effects. Although it is in many ways similar to hierarchical linear modeling or multilevel regression implementations of the multilevel model for change, the latent growth curve model has a few interesting advantages (Preacher et al., 2008). Perhaps the most important of these is that the growth parameters can be used as predictors of other variables and that model fit can be tested more extensively. In the present paper, parameters of the final growth model were tested against health, life and student outcomes in the final year of higher education (see path diagram A in Fig. 2, illustrating the final growth model (Model 3) with growth parameters as predictors of outcomes). These variables were included as outcome variables in the growth model, explained by the latent growth factors. In order to estimate the possible influence of burnout development during higher education on outcomes in working life, the growth model was rearranged so that the growth factors reflecting the burnout levels reached in the final year of education predicted outcome variables 1 year post graduation (see path diagram B in Fig. 2, illustrating the final growth model (Model 3) with growth parameters as predictors of outcomes). The associations between growth factors and outcomes in higher education and 1 year post graduation were presented as standardized regression weights.

Analyses were performed separately for each block of outcome variables. Implementation of the structural equation modeling also has an important advantage in that it provides different possibilities for evaluating model fit. Not only can significance testing of each estimated parameter be performed, but the statistical model as a whole can be evaluated in different ways (reflecting absolute fit, incremental fit and parsimony fit). Thus, the adequacy of imposing these growth models on the present longitudinal data was evaluated using three different types of model indices. These indices and proposed cut-off points were chosen on the basis of their performance in Monte Carlo simulations of Confirmatory Factor Analyses and recommendations based on these simulations (Brown, 2006). Specifically, good model fit was indicated by a standardized root mean square residual (SRMR) below 0.08, a root mean square error of approximation (RMSEA) of around 0.05, a non-significant close fit test (Cfit), and a comparative fit index (CFI) of around 0.95. Before estimation, an evaluation was made of whether the clustered nature of the data needed to be taken into account (Heck and Thomas, 2009). Intraclass correlations were computed and found to range between 0.06 and 0.08 for the Exhaustion scale, and between 0.02 and 0.04 for the Disengagement scale. Thus, at most, 8% of all individual variation may reflect between-universities variation. In preliminary analyses, the extent of individual differences in baseline values and rate of change for Exhaustion became somewhat smaller when applying a two-level growth model (controlling for possible variation in mean levels and longitudinal change across universities). Therefore, in order not to overestimate the extent (and sources) of individual differences in intercepts and slopes, the twolevel growth model was chosen as the longitudinal growth model for all subsequent analyses (controlling for possible differences among universities). The linear growth curve model was estimated using the full information maximum likelihood (FIML) estimation in the Mplus 6.1 software program (Muthe´n and Muthe´n, 1998–2010). This method makes use of all available responses in the longitudinal data (i.e. it includes incomplete cases) and is currently recommended as it provides the most efficient and least biased estimates (Endlers, 2010). However, this assumes that data are completely missing at random (MCAR) or missing at random (MAR). In the present study, the possible influence of missingness on the estimated growth parameters was evaluated in a two-step procedure, where predictors of missingness were identified and then related to levels of burnout. First, we scrutinized differences between respondents with complete (n = 1372) and non-complete data (n = 330) on age, gender, non-Swedish origin, social class, previous experience (of university studies, work in the healthcare system, or clinical training), marital status, parenthood and self-rated health. Secondly, variables that were significantly associated with missingness were related to levels of Exhaustion and Disengagement. In order to present the magnitude of differences in comparable metrics, associations were computed as correlations coefficients.

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A First year

Second year

Third year

1 1 1 Intercept

0

1

Outcomes at the end of educaon

2

Ra te of change

B First year

Second year

Third year

1 1 1 Intercept

-2

-1

0

Outcomes at 1 year post graduaon

Ra te of change

Fig. 2. Path diagram illustrating linear growth models for development and outcomes of study burnout. Note: By structural equation modeling framework convention (Preacher et al., 2008), the circles represent the latent growth factors predicting development across time in the repeated measures (represented as squares) and the consequential outcomes (also represented as squares). Numbers illustrate the scaling of time.

2.4. Ethical considerations The Research Ethics Committee at Karolinska Institutet, Sweden approved the study (KI01-045, 2001-05-14 and 2003-12-29). At the outset, informed consent was provided by all respondents. They received information about the study, guaranteeing confidentiality and underlining the fact that participation was voluntary and could be terminated at any time. 3. Results 3.1. Do burnout levels increase across nursing education? Of the three different longitudinal models tested subsequently, only the model that estimated an intercept and slope, and also allowed for individual differences both in baseline values and rate of change, fitted the longitudinal data well (i.e. Model 3, Table 2). This longitudinal model revealed that both Exhaustion and Disengagement were found to increase across the years in education (see

Table 2 and the model-estimated change trajectories in Fig. 3). As illustrated in Fig. 3, the increase was somewhat more pronounced for disengagement than for exhaustion. An inspection of model fit suggested that the development of disengagement fitted a linear growth model somewhat better than the development of exhaustion. The prevalence of study burnout across nursing education was also estimated using defined cut-off values. The (cross-sectional) prevalence was found to increase from 29.7% during the first year of higher education to 36.9% in the second year. In the third and final year of nursing education the prevalence increased further to 41.0%. 3.2. Do increasing levels of burnout across education affect student outcomes in the final year? In order to evaluate the possible impact of these longitudinal changes, variables hypothetically related to burnout (classified as health/life or student outcomes) were included as outcome variables in the latent growth

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 49 (2012) 988–1001

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Table 2 Development of Exhaustion and Disengagement across higher education. Estimates and model fit from the three postulated latent growth models (n = 1700). Model fit

x Model 1 (df = 11) Exhaustion Disengagement Model 2 (df = 10) Exhaustion Disengagement Model 3 (df = 6) Exhaustion Disengagement

2

Longitudinal main effects

Test of individual differences in growth factors

CFI

RMSEA

SRMR

Intercept

Sig.

Slope

Sig.

Var(I)

Sig.

Var(S)

Sig.

Cov(I * S)

Sig.

160.9 276.8

.891 .754

.090 .119

.034 .053

2.342 2.153

.001 .001

– –

– –

.182 .155

.001 .001

– –

– –

– –

– –

131.2 119.4

.912 .899

.084 .080

.032 .049

2.301 2.072

.001 .001

.046 .091

.001 .001

.183 .159

.001 .001

– –

– –

– –

– –

78.1 39.2

.948 .969

.084 .057

.014 .017

2.300 2.073

.001 .001

.048 .091

.001 .001

.187 .161

.001 .001

.012 .020

.001 .001

.005 .007

.386 .101

Note: Model 1 = random intercept model; Model 2 = random intercept and fixed slope model; Model 3 = random intercept and random slope. Abbreviations: df, degrees of freedom; CFI, comparative fit index; RMSEA, root mean square error of approximation; SRMR, standardized root mean square residual. Sig., level of statistical significance (p-value); Var(I), extent of individual differences around the intercept; Var(S), extent of individual differences around the slope; Cov(I * S), covariance between intercept and slope.

model (depicted as Model A in Fig. 2). Regression coefficients are presented in Table 3. Individual differences in baseline and rate of change values in exhaustion during education consistently predicted both health problems as well as lower levels of satisfaction with life at the end of education. Regression coefficients were somewhat higher when predicting health and life outcome variables from growth factors related to development of exhaustion in comparison with disengagement. In addition, initial and increasing levels of disengagement across education predicted lower levels of in-class learner engagement as well as lower levels of occupational preparedness. Regression coefficients were somewhat higher when predicting student outcome variables from growth factors related to change in disengagement in comparison with exhaustion.

replicated associations found for health/life outcomes at the end of higher education, although the estimates were now somewhat lower. In addition, higher disengagement and exhaustion levels at the end of nursing education predicted lower levels of mastery of occupational tasks, lower levels of research use in everyday clinical practice, and higher levels of intention to leave the profession 1 year post graduation. Again, regression coefficients were somewhat higher when predicting health/life outcomes variables from growth factors related to exhaustion in comparison with disengagement. Moreover, regression coefficients were somewhat higher when predicting occupational outcome variables from growth factors related to disengagement in comparison with exhaustion.

3.3. Do increasing levels of burnout across education affect work-related factors 1 year post graduation?

The potential impact of missingness on the longitudinal model was evaluated by examining differences between respondents with complete (across all data collections, n = 1252) and non-complete data on age, gender, nonSwedish origin, social class, previous experience (of university studies, work in the healthcare system, or clinical training), marital status, parenthood and self-rated

The potential impact of Exhaustion and Disengagement levels reached during education was also evaluated against outcomes 1 year post graduation (depicted as Model B in Fig. 2). Results are presented in Table 4. The analysis

3.4. Impact of missingness

Burnout development during educaon 2.75

Exhauson

2.65

Disengagement

2.55 2.45 2.35 2.25 2.15 2.05 1.95 1.85 1.75

Year 1

Year 2

Year 3

Fig. 3. Development of Exhaustion and Disengagement across higher education. Estimates from a latent growth model.

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 49 (2012) 988–1001

997

Table 3 Consequences of burnout development across higher education. Prediction of health, life and student outcomes in the third and final year. Associations given as standardized regression coefficients (b) taken from latent growth models (n = 1700; df = 8). Exhaustiona

Disengagementb

Baseline

b Outcomes in final year of education Health and life outcomes Depressive symptoms Satisfaction with life Student outcomes In-class learner engagement Occupational preparedness

Rate of change

b

Sig.

Baseline Sig.

b

Rate of change Sig.

b

Sig.

.526 .349

.001 .001

.597 .343

.001 .001

.382 .287

.001 .001

.300 .288

.001 .001

.101 .320

.012 .001

.118 .283

.022 .001

.106 .377

.010 .001

.191 .438

.001 .001

a Model fit: Health and life outcomes: x2 = 103.7, RMSEA = 0.084, CFI = 0.954, SRMR = 0.018. Student outcomes: x2 = 99.6, RMSEA = 0.082, CFI = 0.946, SRMR = 0.015. b Model fit: Health and life outcomes: x2 = 52.7, RMSEA = 0.057, CFI = 0.975, SRMR = 0.014. Student outcomes: x2 = 49.9 RMSEA = 0.056, CFI = 0.971, SRMR = 0.013.

health. The comparisons showed that non-complete data was more frequent among younger respondents (r = 0.06; p < 0.05) and among respondents with non-Swedish origin (r = 0.11; p < 0.001). Furthermore, these two variables were scrutinized with the aim of finding whether they were also related to levels of study burnout. Younger age was found to be associated with disengagement (r ranged between 0.14 and 0.20; p < 0.001) and non-Swedish origin was found to be associated with higher levels of exhaustion (r ranged between 0.08 and 0.12; p < 0.001) Thus, in combination, these results indicate that levels of burnout may be associated with missingness, with the possible effect of underestimating the general increase in study burnout.

health and subjective well-being, as well as important student and occupational outcomes. Looking at the two burnout dimensions of Exhaustion and Disengagement separately, the strength of association was found to differ slightly, depending on the character of the outcome. Becoming more exhausted during studies was particularly associated with a higher degree of depressive mood and less life satisfaction, both at graduation and 1 year post graduation. High and increased disengagement over time was mainly related to being less prepared for a nursing job at the end of nursing education, lower mastery of nursespecific tasks and lower research utilization in everyday clinical practice 1 year post graduation. Moreover, increases in disengagement predicted higher levels of intention to leave the profession 1 year later.

4. Discussion 4.1. Stress and burnout during education In a national sample of nursing students, an annual increase in study burnout (from 30% to 41%) across 3 years in higher education was found. The unique finding in the present study was, however, that study burnout has important prospective consequences. Thus, high baseline levels and development of study burnout predicted poorer

In our national sample of nursing students, the prevalence of study burnout symptoms (i.e. the combination of Exhaustion and Disengagement) showed a 38% increase in prevalence across 3 years in higher education. This result and previous longitudinal findings (Deary et al.,

Table 4 Consequences of burnout development across higher education. Predictions of health, life and occupational outcomes at 1 year post graduation. Associations given as standardized regression coefficients (b) taken from latent growth models (n = 1700; df = 8 except where stated). Study burnout Exhaustiona

b Outcomes in working life Job burnout Exhaustion Disengagement Health and life outcomes Depressive symptoms Satisfaction with life Occupational outcomes Occupational turnover intentions Mastery of occupational tasks Research use

Disengagementb p

b

p

.440 .243

.001 .001

.341 .430

.001 .001

.420 .331

.001 .001

.279 .293

.001 .001

.142 .176 .133

.001 .001 .001

.374 .169 .167

.001 .001 .001

a Model fit: Job burnout: x2 = 123.6, RMSEA = 0.065, CFI = 0.959, SRMR = 0.009. Health and life outcomes: x2 = 78.4, RMSEA = 0.072, CFI = 0.960, SRMR = 0.011. Occupational outcomes: x2 = 76.8, df = 9, RMSEA = 0.067, CFI = 0.955, SRMR = 0.011. b Model fit: Job burnout: x2 = 45.9, RMSEA = 0.053, CFI = 0.979, SRMR = 0.012. Health and life outcomes: x2 = 44.2, RMSEA = 0.052, CFI = 0.978, SRMR = 0.014. Occupational outcomes: x2 = 47.8, df = 9, RMSEA = 0.050, CFI = 0.973, SRMR = 0.012.

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2003; Edwards et al., 2010; Nerdrum et al., 2009; Watson et al., 2009) thus point to the fact that problems of student stress in nursing education increase across the years in education. Recently, studies have addressed the origin of student stress and shown that nursing students perceived stress related to both their academic and clinical education (Burnard et al., 2008; Jimenez et al., 2010; Timmins et al., 2011). University students in general are stressed by assignments and workload during academic training, but for nursing students, training in a clinical setting added significantly to that stress (Burnard et al., 2008; Lindop, 1999; Sheu et al., 2002). Typical sources of stress that have previously been documented are: lack of professional knowledge and skills, taking care of patients (e.g. handling emergencies, dealing with children or death of a patient), relationships with clinical staff and fear of making mistakes (Burnard et al., 2007; Chan et al., 2009; Kim, 2003). Clinical education naturally involves different practical demands and requirements to integrate theory and practice; nonetheless, this is problematic if there is a pronounced dichotomy between the school and the ward, and if students have divergent influences and interests (Startup and Wilson, 1992). The findings of Nerdrum and co-workers even suggested that nursing students’ clinical and academic study environments may be more stressful, compared with environments experienced by occupational therapy and physiotherapy students (Nerdrum et al., 2009). Based on their findings that nursing students experienced excessive workload, least clarity of program structure, lowest quality of student climate and more patient-oriented practice than the other healthcare programs (42% vs. 25%, respectively), they discuss that student stress may reflect back on the design of nursing education programs. Since the nursing programs often present students with demands that pull them in several directions simultaneously (Startup and Wilson, 1992), there is a risk that the findings of Melia from 1980 still apply today (Melia, 1984). She found that constantly moving between nursing schools, clinical placement, and also from one clinical experience to another, resulted in students largely focusing on ‘‘fitting in’’ and ‘‘getting through’’, rather than preparing for work as qualified nurses and autonomously managing patient care. Taken together, as the present study confirms, the consequences of the above-mentioned stressors and coping strategies give rise to higher prevalence of stress symptoms across time. 4.2. Consequences of burnout The prospective findings showed that increased study burnout had consequences on health, life, student and occupational outcomes which have not been shown previously in a national sample of nursing students. However, consequences of job stress and burnout have been reported both in relation to health risks and job dissatisfaction for the individual worker, but also in relation to the associated difficulty in providing safe and high quality care (Kanai-Pak et al., 2008; Poghosyan et al., 2010; Vahey et al., 2004). The premise is that quality of care is compromised due to a process of prolonged stress, and pressure, in which staff accomplish less, becoming

more exhausted and disengaged, and less responsive to the needs of patients (Schaufeli and Buunk, 2003). In the present study, one quality aspect of care – namely, degree of research or knowledge utilization in everyday clinical practice – was found to be a consequence of the development of study burnout. A similar connection was demonstrated among clinical nurses by Estabrooks and colleagues, who found that lower levels of job burnout predicted more research utilization at the individual nurse level (Estabrooks et al., 2007), and conversely that nurses’ emotional exhaustion had a negative effect on research utilization (Cummings et al., 2007). Also, nurses’ commitment to change in clinical practice was linked to job burnout; i.e. the higher the levels of burnout, the less the commitment to change (Wallin et al., 2006). Our results showed that study burnout predicted less inclass learner engagement and lower occupational preparedness at graduation, which could be interpreted as a sign that transition or socialization into the professional role was not ideal for individuals with increased disengagement and exhaustion. Furthermore, study burnout was linked to poorer mastery of professional skills 1 year after graduation. These results strengthen the understanding that especially being more disengaged creates performance problems, both with regard to the individual’s own competence and the ability to base care on research and evidence-based knowledge. Again, this is in line with studies showing that stressful and unproductive study situations may lead to less successful professional socialization, which in turn may have negative consequences in terms of lack of critical thinking, and the tendency to work according to routine rather than patient needs (Mackintosh, 2006; Wilson and Startup, 1991). In addition, disengagement predicted higher levels of intention to leave the profession 1 year later. It is crucial that inexperienced students and new graduates are given supervision, guidance and support in order to uphold a vigorous, sustainable and healthy workforce (Beecroft et al., 2008; Cho et al., 2006; Rudman and Gustavsson, 2011) that remains in the healthcare profession (Hayes et al., 2011). The present study suggests that increased exhaustion during education may have a sustaining consequence on a person’s health after completion of education, and also after entering professional working life. In the present study, initial high levels and subsequent progression of exhaustion during studies resulted in significant costs in terms of health and well-being, both at graduation and 1 year later. In line with this reasoning, if studies demand considerable effort, both academically and in clinical training (e.g. high work pressure, adverse physical setting, and/or emotionally demanding contacts with clients), it is most likely that they will also require considerable resources so that students can cope with the situation adequately and recover from energy loss. Preventive measures to be taken could for instance involve thoroughly considering students’ and new graduates’ ways of coping with stressful situations, and skills for coping with maladaptive cognitions. 4.3. Limitations A limitation of the study was that all data were selfreported. It would have been ideal to have educational

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outcome data on exam scores or views from educators or supervisors; however, in Swedish nursing education only two grades are given: pass and fail (at most, a third grade, pass with merit or distinction, may be given). Most students pass their exams sooner or later, and in our study where we follow this group of students after they enter working life (i.e. after becoming registered nurses), they have all passed their exams. On the other hand, our measures of student outcomes have previously been found to prospectively predict future outcomes. For example, we have data on their own assessment of how confident they are that they have incorporated sufficient knowledge and skills to work as clinical nurses, and we have evidence from another cohort of students entering working life that their own assessments of preparedness predict future burnout development (Rudman and Gustavsson, 2011). The longitudinal analysis across the years in education was performed using the Oldenburg Burnout Inventory. However, the follow-up data in working life was based on a short-form version (10 items instead of 16) of the Oldenburg Burnout Inventory. A possible consequence of using a short-form version might be that we have underestimated the association between study burnout development and future burnout due to somewhat lower reliability for these shorter scales. However, the estimates of internal consistency for the short-form scale are close to estimates found for the full scales. In addition, when extracting the short-form scales from the full version, using the baseline data collection, and correlating full versions with short-form versions of the scales, the resulting correlations were above 0.90 (0.96 for exhaustion and 0.91 for disengagement). Thus, although the use of different versions of the same instrument at different data collections is not recommended, in this particular case the potential problems are negligible. Mean level increase in Exhaustion and Disengagement was distinct and statistically significant, with confirmed consequences at the end of education and in working life; however, it was not large. Nevertheless, the cross-sectional estimates of study burnout prevalence, combining results on both burnout dimensions, indicated a substantial increase across time. To what extent can the successive loss of respondents over the years influence the growth curve estimates, and if so, in what direction? Firstly, in the dropout analyses, one variable that could be related to burnout, i.e. self-rated health, was not associated with successive dropout. This may indicate that there is no large selection bias influencing the results, because unhealthy or stressed individuals do not leave the cohort to a greater degree than healthy ones. However, among the sociodemographic predictors of non-response, for example, young age was related to future non-response. This is a potential problem, since it has previously been found that young age is related to higher levels of burnout (Maslach et al., 2001; Rudman and Gustavsson, 2011). Thus, if there were to be a selection bias, resulting in young respondents with high levels of study burnout leaving the cohort, the probable effect on the estimated linear trend would be an underestimation of the increase. However, the practical significance of this study should not be judged against this rather small (and perhaps underestimated) general

999

increase in study burnout. Instead, the focus should be on individual differences in change and the consequences of these on future outcomes. In our opinion, the magnitude of these associations is not negligible. Finally, the current study has some obvious strengths. Data covers three consecutive years during nursing education and a follow-up early in the professional career. Also, the study was based on a national sample with relatively good response rates throughout the four data collection waves, and this sample has been found to be representative of the population of newly graduated nurses (Rudman et al., 2010). Moreover, the Swedish translations of most instruments used in the present study have previously undergone extensive psychometric testing in a Swedish healthcare setting. 5. Conclusions The findings in the present study strengthen the notion that contemporary nursing education programs involve a number of challenges, and for some students result in burnout symptoms (exhaustion and/or disengagement). Elevated levels of study burnout in nursing education may interfere with learning and psychological well-being. The main objective for pursuing this study was to ascertain whether the development of study burnout has any consequences on outcomes of education, and investigate how new graduate nurses function in clinical practice. Using this longitudinal design with data collected at four points in time over 4 years, three during education and one post examination, prospective associations were detected. A spectrum of outcomes was related, indicating that burnout development during higher education is an important concern. Ultimately, if a high-demand situation is prolonged, compensatory strategies may involve redefinition of task requirements, which may lead to reduced engagement and ambition to deliver high quality care, and at worst risky behavior involving high levels of tiredness and unsafe provision of care. In addition, increased disengagement was linked to intention to leave the profession all along, which highlights the importance of counteracting disengagement early on. Conflict of interest: No conflicting interest.Funding: The authors received Grants from AFA Insurance.Ethical approval: The Research Ethics Committee at Karolinska Institutet, Sweden approved the study (KI01-045, 200105-14 and 2003-12-29). At the outset, informed consent was provided by all respondents. They received information about the study, guaranteeing confidentiality and underlining the fact that participation was voluntary and could be terminated at any time. References Bakker, A.B., Demerouti, E., 2007. The job demands-resources model: state of the art. Journal of Managerial Psychology 22 (3), 309–328. Bech, P., Rasmussen, N.A., Olsen, L.R., Noerholm, V., Abildgaard, W., 2001. The sensitivity and specificity of the Major Depression Inventory, using the Present State Examination as the index of diagnostic validity. Journal of Affective Disorders 66 (2-3), 159–164. Beecroft, P.C., Dorey, F., Wenten, M., 2008. Turnover intention in new graduate nurses: a multivariate analysis. Journal of Advanced Nursing 62 (1), 41–52.

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