Early-career burnout among new graduate nurses: A prospective observational study of intra-individual change trajectories

International Journal of Nursing Studies 48 (2011) 292–306

Contents lists available at ScienceDirect

International Journal of Nursing Studies journal homepage: www.elsevier.com/ijns

Early-career burnout among new graduate nurses: A prospective observational study of intra-individual change trajectories Ann Rudman *, J. Petter Gustavsson Division of Psychology, Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden

A R T I C L E I N F O

A B S T R A C T

Article history: Received 8 March 2010 Received in revised form 12 July 2010 Accepted 15 July 2010

Background: Newly qualified and inexperienced nurses are at particular risk of suffering emotional exhaustion and burnout in unsupportive practice environments. Despite new nurses’ potential vulnerability, development of burnout after graduation has rarely been studied longitudinally and in relation to demographic and educational characteristics prior to working life entry, i.e. during education. Objectives: To identify and compare typical change trajectories (i.e. common patterns of intra-individual development) in burnout symptoms for new graduate nurses annually over a three-year period, during which there was reason to believe that this group was especially vulnerable. Design: A prospective longitudinal and national cohort of 1153 nurses within the population-based LANE study (Longitudinal Analyses of Nursing Education), where new graduate nurses were assessed four times annually, i.e. in their final year of nursing education and three times post graduation (after 1, 2 and 3 years). Participants: A longitudinal sample of 997 respondents was prospectively followed. Methods: Within-group changes in burnout levels were analysed using a repeatedmeasures analysis of variance, and cluster analytic techniques were used to identify typical trajectories of burnout. Results: At group level, mean levels of burnout were rather stable across time. However, underlying these levels we identified eight change trajectories, explaining 74% of all individual variation; seven of them reflected significant changes across time. Almost every fifth nurse reported extremely high levels of burnout at some point during their first three years after graduation. Changes in burnout levels were accompanied by concurrent changes in depressive symptoms and intention to leave the profession. This study also showed that negative development of burnout was predicted by not feeling well prepared for a nursing job, lacking study interest, high levels of performance-based self-esteem and depressive mood in the final year of education. Conclusions: An investigation of burnout symptoms over time disclosed numerous development patterns, some of which were stable while others changed significantly. Hence, this study gave a more nuanced picture of burnout development among new graduate nurses, highlighted by eight different trajectories. Regarding the time frame, nearly every second new graduate showed a significant increase in levels of burnout during their second year post graduation. ß 2010 Elsevier Ltd. All rights reserved.

Keywords: Burnout Development Longitudinal Cohort Cluster analysis New graduate nurse

What is already known about the topic?

* Corresponding author. Tel.: +46 8 524 83928. E-mail address: [email protected] (A. Rudman). 0020-7489/$ – see front matter ß 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijnurstu.2010.07.012

 Newly qualified and inexperienced nurses are at particular risk of suffering emotional exhaustion and burnout in unsupportive practice environments.

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

 Theories of burnout describe both the antecedents and the process of burning out; however, empirical evidence points to stable individual differences in burnout across time.  Despite new nurses’ potential vulnerability, development of burnout post graduation has rarely been studied longitudinally and in relation to demographic and educational characteristics prior to working life entry, i.e. during education. What this paper adds  Beyond correlational and mean-level stability at group level, diverse growth trajectories of professional burnout can be identified.  During the first three years of practice, every fifth nurse is at some point ‘‘burned out’’, and for the majority of novice nurses, the second year of practice seems especially stressful.  Those of younger age seem especially vulnerable to early-career burnout.

1. Introduction Research into stress and professional burnout has shown that different aspects of working environments influence burnout (Aiken et al., 2009; Leiter and Spence Laschinger, 2006). Stressful work conditions such as work overload, lack of control, insufficient reward, breakdown of community, absence of fairness, conflicting values and bad administrative support have been linked to burnout (Maslach and Leiter, 1997). In the case of nurses, empowering work conditions, i.e. access to opportunity for development, information, support, resources necessary to accomplish work as well as formal and informal power, were shown to affect various areas of working life, which in turn influence nurses’ health (Laschinger and Finegan, 2005). Conversely, when work conditions do not ensure that employees have access to these factors (i.e. when social structures in the workplace are not empowering), this may result in reduced autonomy and impact in the organization and a sense that the job is meaningless, which may lead to burnout (Laschinger et al., 2010). Newly qualified and inexperienced nurses are at particular risk of suffering emotional exhaustion and burnout in unsupportive practice environments (Cho et al., 2006; Kanai-Pak et al., 2008; Laschinger et al., 2009). Despite new nurses’ potential vulnerability, development of burnout post graduation has rarely been studied in relation to preceding factors during education (Crow et al., 2005; Duchscher, 2009; Watson et al., 2009). Also, despite the growing number of studies on antecedents of burnout, relatively few have explored demographic (Cimiotti et al., 2008) and educational characteristics (Duchscher, 2009; Watson et al., 2009). Burnout has been defined as a syndrome comprising two core dimensions: exhaustion and cynicism or disengagement. It develops due to taxing work situations, initially as a consequence of additional efforts to handle the situation and further due to the application of

293

defensive behaviour and emotional detachment (Schaufeli and Buunk, 2003; Schaufeli and Enzmann, 1998). Although it is seen as an individual reaction, work factors such as excessive job demands are hypothesized to drive people into a downward spiral of expending valuable personal resources (such as effort, energy, time) (Maslach et al., 1996; Shirom, 2003). Research has consistently found that work situations characterized by high workload and time pressure (quantitative demands), as well as role conflicts and role ambiguities (qualitative demands) are associated with burnout (Lee and Ashforth, 1996; Maslach et al., 2001; Schaufeli and Buunk, 2003; Schaufeli and Enzmann, 1998). Although such stressors may apply, both in meaning and measurement, across occupations (Wa¨nnstro¨m et al., 2009), the importance of unique demands (and resources) for different occupations has recently been stressed to gain a more complete understanding of environmental sources of burnout development (Bakker and Demerouti, 2007). For example, the emotional labour characterizing the everyday practice of human service professionals (including nurses), may be seen as a unique factor predicting burnout among nurses, over and above common job stressors (Zapf et al., 2001). As younger age has been associated with higher burnout levels, novice professionals may be especially vulnerable to burnout (Maslach et al., 2001; Poncet et al., 2007; Schaufeli and Enzmann, 1998). For example, new graduate nurse professionals may initially feel inadequately prepared for their occupational role (Cherniss, 1980; Duchscher, 2009) and their expectations may clash with the harsh everyday reality at work (Maben et al., 2006; Mackintosh, 2006). Thus, in their new work environment, work overload and role stress will inevitably put the newcomer in a vulnerable position; the new graduate nurse may be overwhelmed by feelings of failure and frustration (Duchscher, 2009). In cases where inadequate socialization to the nursing role develops into earlycareer burnout, energy turns into exhaustion, and involvement into cynicism (Cherniss, 1980; Kramer, 1974; Mackintosh, 2006). Although theories of burnout outline how critical sources in the work environment have an impact on individual burnout and how the process develops within the individual, longitudinal studies find rather high test– retest correlations across time (Schaufeli and Enzmann, 1998; Shirom, 2005). Surprisingly, these stability correlations are close in magnitude to those found for stable individual traits (Roberts et al., 2000). Moreover, individual changes in levels of burnout are commonly predicted, not by job stressors, but by former levels of burnout (Schaufeli and Enzmann, 1998). However, when studying individual development, mean-level stability as well as stability correlations at group level may mask substantial and perhaps diverging patterns of intra-individual change. Such patterns may be uncovered by studying individual change trajectories across several time points targeted for the phenomena under investigation. Our aim was to identify and compare typical change trajectories, i.e. common patterns of intra-individual development in burnout symptoms for new graduate nurses, i.e. registered nurses (RNs), annually for the first

294

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

three years post graduation – a period during which there was reason to believe that this group was especially vulnerable (Cherniss, 1980). The potential development of burnout symptoms during the first years in a healthcare profession is expected to differ because every individual may react and interact in an infinite number of ways during this process. Also, the change process may be triggered by different complex mechanisms within the individual. Although the process is unique for every person, it is likely that a limited number of typical trajectories can be identified and studied (Bergman et al., 2003). One way of addressing this complexity would be to take several measures of burnout over a period of time and then classify individuals with respect to differences and similarities (Baltes and Nesselroade, 1979). Exploring individual response patterns in this way should reveal how, and in how many ways, burnout symptoms are developed within the individual over time. Change trajectories were therefore studied in two steps. First, individual change trajectories were formed, i.e. a change trajectory was calculated for every individual separately with respect to rate, form and timing. Secondly, differences and similarities between respondents were examined to establish a classification of typical change trajectories representing inter-individual differences (similarities) among individuals. Thus, the aim of the present study was to establish whether typical development patterns of burnout among new graduates can be identified over the first three years of practice in the healthcare sector, and if so, to determine individual characteristics of nurses with different trajectories in relation to socio-demographic background, previous health problems, educational outcome, induction at first employment as a nurse, as well as concurrent health and work attitudes. 1.1. Research questions Beyond correlational and mean-level stability at group level, what common growth trajectories of professional burnout can be identified over the first years after graduation (i.e. one, two and three years after graduation)? What do these patterns indicate regarding prevalence and incidence of early-career burnout? And finally, how are these growth trajectories (a) predicted by educational and individual factors and (b) related to concurrent development of psychological health and turnover intentions? 2. Methods 2.1. Study sample Data from a national cohort of 1155 nurses within the population-based LANE study (Longitudinal Analyses of Nursing Education) (Rudman et al., 2010) were used to investigate development of burnout trajectories in the first three years of practice. Altogether, 1700 students from the final semester of nursing education, from any one of the 26 universities providing undergraduate nursing education in Sweden, were invited to participate in the study. In total, 1153 (68%) students gave informed consent and answered

the first LANE questionnaire. Nurses in the study sample graduated from Swedish universities in 2002 and the cohort was therefore named EX2002. Background characteristics as well as educational outcomes were assessed during the last term of education, and subsequently burnout and numerous individual and work-related factors related to ill health were assessed annually up to three years after graduation (Rudman et al., 2010). Description of the four data collections, the longitudinal sample selection and timing of follow-ups are presented in Fig. 1. At the first data collection, during education, the 1155 study participants were on average 30.5 years old (ranging from 21 to 52 years), a majority were female (89%), of Swedish background (93%) and had previous experience in the field of healthcare (54%) (Rudman et al., 2010). Regarding representativeness, the EX2002 cohort was compared with six different demographic variables from population-based national registers (i.e. age, gender, country of birth, residency (large city), marital status, and parenthood), and was representative on all tested variables except for the higher prevalence of Swedish-born students in the cohort (94% vs. 91%) (Rudman et al., 2010). Response rates varied between 81% and 92% and declined somewhat over time. However, no more than 38 participants dropped out entirely after the first assessment. For a further detailed description of the cohort sample and comprehensive analyses of non-response, see Rudman et al. (2010). 2.2. Data collection All data were self-reported and collected through annual postal surveys (Rudman et al., 2010). For the purpose of the present study, data from education (E, n = 1155, November 2002); one year (Y1, n = 1059, February 2004); two years (Y2, n = 1037, February 2005); and three years (Y3, n = 933, February 2006) post graduation were used (Fig. 1). Altogether, 156 participants (for losses to follow-ups in this study, see Fig. 1) did not respond at two or more of the three time points after graduation and they were compared with those included in the longitudinal analyses regarding age, gender, country of birth, earlier experience of healthcare work and self-rated health. These nonrespondents did not differ in age, gender, sex, country of birth, earlier experience of healthcare work; however, they reported lower levels of general health (13 vs. 7%). In total, 687 nurses (60% of the nurses included in the cohort) completed the questions regarding job burnout at all three time points after entering working life (year 1–3) (Fig. 1). In the longitudinal analyses, an additional 310 nurses (27%) who had a missing value in one of the three data collections during working life were given an imputed value according to the twin imputation method in SLEIPNER (Bergman and El-Khouri, 2002). In this method, one respondent’s missing value in a certain variable is replaced with the value for the most similar case with complete data. The procedure follows the rationale of the pattern-oriented approach to the study of individual development across time (Bergman and El-Khouri,

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

295

Fig. 1. Description of the four data collections and the longitudinal sample (i.e. sample selection, participant recruitment, consent, timing of follow-ups and the wave response). Education: last year of nursing education (in 2002), year 1: one year after graduation (in 2004), year 2: two years after graduation (in 2005), year 3: three years after graduation (in 2006).

296

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

1986). After imputation, 997 participants were included in the longitudinal analysis (Fig. 1). The imputed sample consisted of 162 participants who did not respond to one of three data collections, and 148 who did not respond to the burnout inventory. The imputed sample (n = 310) were compared with the 687 respondents with complete values, regarding age, sex, country of birth, earlier experience of healthcare work and self-rated health. The only difference found between the groups was regarding age, i.e. respondents with complete values were more often >35 (34 vs. 23%) and imputed group members were more often 25–35 years of age (47 vs. 38%). 2.3. Measurement Although measurement models of burnout often stress the multidimensional nature of the concept, theoretically the various dimensions can be ordered sequentially, reflecting different phases. In a recent psychometric study (using an item response approach based on data from the LANE cohort, three years after graduation), the result indicated that items reflecting varying levels of exhaustion reactions and negative attitudes towards work were found to adhere to a one-dimensional measurement model (Gustavsson et al., 2010). This supported that early-career burnout can be measured by a one-dimensional summative scale and that different levels of burnout on this scale may reflect different phases in the burnout process, i.e. where there is a progression from initially increasing levels of exhaustion into burnout, due to dysfunctional coping (cynicism and disengagement). In line with these psychometric results, burnout was operationalized in the present study as a summative scale on the basis of responses to six items from a previously used burnout inventory (Demerouti et al., 2001). The original response scale with four response categories was used, i.e. 1 ‘‘totally disagree’’ to 4 ‘‘totally agree’’. The use of seven burnout items was recommended in the psychometric paper (Gustavsson et al., 2010). Unfortunately, one of these items was not included in the assessment two years after graduation; it was therefore decided to exclude this item from the other two assessments as well. The possible implication of this revision was tested by correlating the full and reduced scale. The correlation between the full seven-item version and the shortened sixitem version was close to unity (r = 0.990). In addition, the psychometric analyses presented in the previous paper were performed on data from all occasions, and the psychometric property for the six-item version was found to be equivalent to the seven-item version. Thus, the scale comprised the six items included on all occasions. This summated scale, used in the longitudinal analyses when computing individual change trajectories of burnout, ranged between 6 and 24 (i.e. 6 = no burnout and 24 = highest level of burnout). In line with previous psychometrically derived cut-off values (Gustavsson et al., 2010); prevalence of burnout symptoms was classified as follows: 6–11.9 low, 12–18 moderate, 18.1– 24 high burnout. The estimated internal consistencies were 0.81, 0.78, and 0.81 across the first three years after graduation. Short descriptions of the variables that may

predict, contribute to, or modify the development of typical burnout trajectories are given in Table 1. 2.4. Statistical analysis A univariate repeated-measures analysis of variance (rANOVA) was conducted to assess within-group changes in burnout levels for the new graduate nurses across the first three years of practice. This analysis was followed by pair-wise comparisons of the mean burnout changes between the years of practice. Partial eta and Cohen’s d were calculated as effect size indices. The rank-order stabilities of burnout over the first three years of practice were computed by test–retest (Pearson) correlations for the total sample. An alpha level of p < 0.05 was generally applied. Cluster analytic techniques were used to identify typical trajectories of burnout (Bergman et al., 2003). First, Ward’s hierarchical method was performed on the data from the 687 nurses with complete responses at all three time points. Similarities between all individual change trajectories with respect to rate, form and timing were calculated by the squared Euclidean distance. The Ward analysis followed an established rationale and was conducted using procedures in SLEIPNER 2.0 (Bergman and El-Khouri, 2002). Preparatory analysis was performed in order to identify outliers, given that outliers can affect the analysis by means of undesirable combinations of clusters that last throughout the analysis. This procedure was carried out using the module RESIDUE (SLEIPNER 2.0 (Bergman and El-Khouri, 2002)) and no outliers with diverging patterns were identified or excluded from further analysis. Three characteristics were considered in order to establish and ensure a trustworthy classification. A solution with an acceptable level of Explained Error Sum of Squares (EESS) is required (i.e. recommended minimum level of the percentage ESS = 67%). Next, homogeneity coefficients were examined to verify no unwarranted increases when two clusters with moderate levels were merged, and recommended values should preferably not exceed 1 (Bergman et al., 2003). Finally, besides a high explanatory power and homogeneity within clusters, a theoretically meaningful, comprehensible and reliable solution was required, to reject the next step in the analysis where two clusters with fundamentally different trajectories come together (Bergman et al., 2003; Milligan, 1996). A validation procedure (i.e. Simulate) was used to test the results of the chosen solution in the cluster analysis against a null hypothesis of no relationships in the data. This was done by simulation of 20 datasets, obtained by adapting the original dataset randomly, while maintaining constant marginal frequencies for the included variables. Secondly, a K-means cluster analysis was performed involving assignment of individuals in the total sample (n = 997, including both the respondents with complete and imputed data) to the cluster centroids (means) produced by the solution chosen from the hierarchical clustering method. Before the K-means cluster analysis, the twin method of imputation was applied for the 310 participants with non-response on one of the three

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

297

Table 1 Description of concurrent and predictive variables used to characterize the burnout trajectories. 1. Concurrent variables measured year 1, 2 and 3 after education Depressive mooda: no vs. yes Intention to quit: often think about leaving the profession or healthcare: yes vs. no One item measuring intention to quit, with five response categories ranging from 1 ‘‘Fully agree’’ to 5 ‘‘Fully disagree’’, where 1 and 2 = yes and 3,4 and 5 = no 2. Predictor variables measured during last term of education (except length and content of induction at first employment as a nurse, which was measured one year after graduation) a. Demographics and individual factors Age in years <25, 25-35, >35 Sex: male vs. female Own children: no vs. yes Country of birth: Swedish vs. not Swedish Previous training as a nursing assistant (before studies): no vs. yes Previous work experience in the healthcare sector: no vs. yes Performance-based self-esteem: no vs. yesb Scale for performance-based self-esteem (PBSE) (Hallsten et al., 2005) included four items related to general performance-based self-esteem with a response format from 1 ‘‘fully agree’’ to 5 ‘‘fully disagree’’. The mean value of the four PBSE items were calculated and a cut-off of 2.5 vas used i.e. <2.50 = yes vs. >2.51 = no High scores in (or high levels of) Negative affectivity: no vs. yes Negative affectivity scale taken from a personality inventory (Gustavsson et al., 2003) assessing the trait from the five factor model of personality that previously have been most consistently associated with burnout (Swider, 2010). The scale includes four items related to stress susceptibility and emotional reactivity with a response scale from 1 ‘‘applies completely’’ to 4 ‘‘does not apply at all’’. The mean value of the four items was calculated and a cut-off of 2.5 was used (i.e. <2.50 = yes vs. >2.51 = no) b. Lifestyle, health and stress Self-rated health: good (good/pretty good) vs. poor (neither good nor bad/pretty bad/bad) Nourishing eating habits: no vs. yes Smoking: no vs. yes Harmful alcohol consumption: no vs. yes Harmful alcohol consumption was measured by one item from the Alcohol Use Disorders Identification Test (AUDIT) (i.e. the ‘binge drinking’ question which proved to be the best item indicator of hazardous or harmful alcohol use in the test) (Bergman and Kallmen, 2002) Depressive moodc: no vs. yes Previous depressive episode (lifespan): no vs. yes This single item was in the questionnaire added to the Major Depression Inventory (MDI) and was used to assess the life-time prevalence of a depressive episode as described by items in the MDI Musculoskeletal tension and paind: no vs. yes Back paind: no vs. yes Neck and shoulder paind: no vs. yes Burnout symptomse during studies/exhaustion: no vs. yes The exhaustion scale consisted of six items with four response categories i.e. 1 ‘‘does not apply at all’’, 2 ‘‘does not apply very well’’, 3 ‘‘applies pretty much’’ and 4 ‘‘applies completely’’. A mean value where computed that ranged from 1 to 4 (i.e. 1 = not exhausted and 4 = completely exhausted) and a cut-off of 2.5 was used i.e. <2.49 = no vs. 2.50 = yes Burnout symptomse during studies/Disengagement: The disengagement scale consisted of six items with four response categories i.e. 1 ‘‘does not apply at all’’, 2 ‘‘does not apply very well’’, 3 ‘‘applies pretty much’’ and 4 ‘‘applies completely’’. A mean value where computed that ranged from 1 to 4 (i.e. 1 = not disengaged and 4 = completely disengaged) and a cut-off of 3.0 was used (i.e. <2.9 = no vs. 3.0 = yes) Present stressorsf studies: yes (very stressed/stressed) vs. no (not stressed/not at all stressed) Present stressorsf choice of profession: yes (very stressed/stressed) vs. no (not stressed/not at all stressed) c. Educational outcome Importance of nursing studies: no vs. yes Significance of studies was measured by one item: ‘‘How important are your studies in your life?’’ with a response format from 1 (‘‘one of the least important things in my life’’) to 7 (‘‘one of the most important things in my life’’) Overall educational outcome: Preparedness: no vs. yes Preparation overall outcome was measured by one item: ‘‘As a result of my education I am well prepared to manage my work as a nurse’’ with seven response categories ranging from 1 ‘‘fully disagree’’ to 7 ‘‘fully agree’’ (1, 2, 3, 4 = no vs. 5, 6, 7 = yes) Overall educational outcome: Comprehensive evaluation of nursing education: good vs. bad Evaluation of nursing education was measured by a single question phrased ‘‘What overall grade would you give the study programme you are following at this university? with the following response format: 1 ‘‘Very good’’, 2 ‘‘Good’’, 3 ‘‘Bad’’, or 4’’Very bad’’’’ (1, 2 = good vs. 3, 4 = poor) Satisfaction with length and content of induction at first employment as a nurse (measured one year after graduation) Evaluation of induction at first employment was measured by a single question phrased ‘‘Are you satisfied with the length and content of the induction you were given at your first employment?’’ with the following response format: 1 ‘‘Very satisfied’’, 2 ‘‘Satisfied’’, 3 ‘‘Not so satisfied’’, or 4’’Not at all satisfied’’ (1, 2 = satisfied vs. 3, 4 = not satisfied) a Depressive symptoms were assessed by the Major Depression Inventory (Bech et al., 2001), and a scale consisting of items reflecting the three core symptoms according to ICD 10 was used. b Self-esteem is a heterogeneous characteristic and one aspect of self-esteem concerns its contingency, how self-esteem is based and construed (Crocker and Park, 2003). The PBSE scale measures an orientation to gain or maintain self-esteem through good performance in roles or arenas of importance for selfesteem. The concept of PBSE was developed to understand burnout processes (Hallsten et al., 2005). c Depressive symptoms were assessed by the Major Depression Inventory (Bech et al., 2001) and here calculated according to ICD-10 (three core symptoms should have been present nearly every day during the past two weeks). d Frequency of symptoms was measured during the last four weeks. Prevalence of symptoms was classified as a positive response to either of the three response categories: a little, moderately or a lot. e Symptoms of burnout were measured by a modified version of the Oldenburg Burnout Inventory (Demerouti et al., 2001) were statements relating to working life were reworded to address study life instead. f Deriving from a checklist of current stressors that are dichotomized according to intensity of strain.

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

298

variables (see above). Thus, the final classification was established using K-means analysis on the 997 sample, conducted in SPSS statistics 17.0 (SPSS Inc, 2009). Mean values and standard deviations in burnout across time for each change trajectory were computed and interpreted (and these levels were not statistically different when comparing classifications with or without the imputed data). The statistical significance and magnitude of differential development in burnout was tested as an interaction effect in a two-way repeated-measures ANOVA (with the classification as independent variable, and the three burnout measurements across time as dependent variables). Post-hoc tests were also performed to examine the within-subject changes over time for each change trajectory, separately. To illustrate the magnitude of differences in levels and changes of burnout, Z-scores were calculated and calibrated against the measurements one year after graduation and presented in figures. Thirdly, analyses were performed in order to compare the classification of trajectories with external or explanatory variables (i.e. concurrent and predictive variables not used in the primary classification analysis, Table 1). The purpose of this analysis was to ‘‘validate’’ or find out whether individuals who had similar trajectories of burnout symptoms also had other common characteristics with respect to the selected external variables. This further test of the internal validity of the trajectory classification is regarded as crucial in examining the trustworthiness of the chosen solution (Aldenderfer and Blashfield, 1984; Bailey, 1994) and concerns whether the different development patterns can be observed as concurrent changes in external variables (also whether these changes can be predicted, see below). Explanatory variables used to characterize the development patterns were chosen on the grounds that they were relevant in relation to development of burnout (Schaufeli and Enzmann, 1998). A two-way repeatedmeasures ANOVA was performed to test whether the differential development patterns in burnout also reflect concomitant changes in depressive mood and intention to leave the profession across the three time points (the trajectory by time interaction effect). A post-hoc test was performed to test if each of the eight change trajectories was also associated with significant changes in levels of depressive mood and intention to leave the profession across time (the within-subject test of the time effect). Again, for illustrative purposes, Z-scores for depressive symptoms and intention levels were calculated and

calibrated against the measurements one year after graduation, and presented together with burnout trajectories in figures. Moreover, the classification of trajectories was also validated by examining whether individuals belonging to the different typical burnout trajectories were different with respect to demographic, educational outcome, and other related variables. Descriptive analyses were performed using x2 tests (level of statistical significance was set at p < 0.05) (Glass and Hopkins, 1996). Post-hoc tests were performed in order to find out which trajectories and categories of an explanatory variable added most to a significant x2 test (as reflected by adjusted standardized residuals with a value >1.96 or < 1.96). These validation analyses were computed on data with and without the imputed cases. As these two datasets yielded almost identical outcomes, the results presented come from the analyses including imputed data. Analyses were conducted using SPSS statistics 17.0 (SPSS Inc, 2009). Oral and written information about the study, including details about secured confidentiality for potential participants, was given to all nursing students. Participants were also assured that they could terminate their participation at any time if they wished. Voluntary written informed consent was obtained from each participant. The Research Ethics Committee at Karolinska Institutet, Sweden gave approval for the study, no. KI01-045 [2001-05-14; 200312-29]. 3. Results 3.1. Mean-level changes and rank-order stability across the first three years of practice The mean burnout levels from the first, second and third years after graduation for the total sample are presented in Table 2. The highest mean level of burnout was obtained two years post graduation. Although statistically significant (F = 3.19; df = 2; p = 0.041), the changes over time were small (partial eta2 = 0.003) and the highest levels present two years post graduation differed by only 0.06 (Cohen’s d) from the assessments one and three years post graduation. Rank-order stability was calculated as test–retest (Pearson) correlations. Both one-year stability coefficients were about 0.60 (r12 = 0.59 and r23 = 0.61), while two-year stability (r13 = 0.52) was lower.

Table 2 Mean values (centroid) and standard deviations of the eight trajectories of burnout. Trajectory

N (%)

Description

All A B C D E F G H

997 151 132 191 161 74 104 94 90

Unaffected Changing from moderate to low levels Developing from low to moderate levels of burnout Moderate and rather stable levels of burnout Increasing to moderate levels of burnout, followed by recovery Moderate levels, becoming higher across time Initially high values of burnout, recovering across time High and increasing levels of burnout

(15) (13) (19) (16) (7) (11) (10) (9)

Year 1 Mean (SD)

Year 2 Mean (SD)

Year 3 Mean (SD)

12.67 (3.6) 8.28 (1.4) 12.74 (1.6) 9.5 (1.4) 14.66 (1.5) 13.24 (2.0) 12.49 (1.7) 18.47 (1.8) 16.82 (2.3)

12.91 (3.6) 8.3 (1.4) 10.25 (1.8) 11.79 (1.7) 12.39 (1.2) 15.73 (2.1) 16.14 (1.7) 16.74 (2.1) 17.82 (2.4)

12.69 (3.6) 8.4 (1.7) 9.84 (1.5) 12.35 (1.8) 14.19 (1.8) 9.96 (1.7) 15.22 (1.6) 14.50 (1.7) 19.59 (1.6)

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

3.2. Classification of individual change trajectories Eight change trajectories were identified, explaining 74% of all individual variation. The validation procedure (i.e. Simulate) confirmed that the chosen solution explained significantly more of the variance than expected by chance. The form and level for each trajectory along

299

with the number of individuals constituting each trajectory are presented in Table 2 and Fig. 2. Each trajectory was homogenous, as reflected in low homogeneity coefficients ranging between 0.35 and 0.80. The eight trajectories reflected significantly different development patterns across all three time points (also between the 1st to 2nd, and 2nd to 3rd time points) as

Fig. 2. The 8-cluster solution for burnout (unbroken line). Each mean value is illustrated by a profile linking the z-transformed means of the corresponding curve at all three time points after graduation year 1 (Y1), year 2 (Y2) and year 3 (Y3). A unit on the y-axis represents one standard deviation. Concurrent values for depressive mood (dotted line) and intention to quit (broken line) are shown in a similar way. Positive values indicate high levels of burnout, depressive mood and intention to quit, and negative values indicate low levels.

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

300

Table 3 Test of differential development across time (group by time interaction) in a repeated-measures ANOVA (df = 2, 997). Post-hoc tests concern effect of time within each development group separately. Across all 3 waves

a

Burnout (time) Burnout (GC  time)b Post-hoc (time)c A B C D E F G H Depressive mood (GC  time)d Post-hoc (time)e A B C D E F G H Intention to quitd (GC  time) Post-hoc (time)e A B C D E F G H a b c d e

From 1st to 2nd wave 2

From 2nd to 3rd wave 2

F

p

eta

F

p

eta

F

p

eta2

3.2 124.0

.041 0.001

0.003 0.468

5.5 127.5

0.019 0.001

0.005 0.474

4.6 86.3

0.032 0.001

0.005 0.379

0.3 114.7 145.3 100.1 161.9 124.5 102.0 44.5 8.0

0.616 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

0.001 0.467 0.433 0.385 0.689 0.547 0.523 0.333 0.065

0.1 135.8 211.3 234.4 52.6 248.5 37.2 10.8 8.1

0.885 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001

0.001 0.509 0.527 0.594 0.419 0.707 0.285 0.108 0.065

1.5 3.8 8.0 128.3 298.2 13.3 62.3 31.2 5.3

0.574 0.051 0.005 0.001 0.001 0.001 0.001 0.001 0.001

0.002 0.029 0.040 0.445 0.803 0.114 0.401 0.259 0.044

0.8 1.9 7.9 3.0 10.2 8.3 20.1 0.8 7.9

0.451 0.145 0.001 0.052 0.001 0.001 0.001 0.462 0.001

0.006 0.018 0.050 0.024 0.140 0.097 0.209 0.011 0.075

1.7 0.1 11.1 5.6 7.6 15.0 11.4 1.9 7.5

0.198 0.917 0.001 0.019 0.008 0.001 0.001 0.171 0.001

0.012 0.001 0.069 0.043 0.107 0.163 0.130 0.027 0.067

0.7 3.6 0.1 4.1 18.7 1.0 10.5 0.3 8.1

0.403 0.060 0.591 0.045 0.001 0.323 0.002 0.578 0.001

0.005 0.032 0.002 0.032 0.228 0.013 0.121 0.004 0.077

1.8 2.6 10.7 1.5 22.9 8.6 0.1 18.7

0.161 0.078 0.001 0.226 0.001 0.001 0.944 0.001

0.014 0.027 0.078 0.014 0.313 0.112 0.001 0.272

1.2 3.7 14.4 1.1 23.3 10.1 0.1 19.2

0.269 0.057 0.001 0.302 0.001 0.002 0.814 0.001

0.010 0.038 0.102 0.010 0.318 0.130 0.001 0.277

0.7 3.2 0.6 4.0 30.4 0.2 0.2 3.7

0.407 0.077 0.437 0.049 0.001 0.689 0.698 0.060

0.005 0.033 0.005 0.036 0.378 0.002 0.003 0.069

One-way repeated-measures ANOVA on burnout scores across time. Test of the interaction effect (trajectory by time) on burnout scores in a two-way repeated-measures ANOVA. A post-hoc test on each trajectory separately, addressing mean-level changes in burnout scores across time. Test of the interaction effect (trajectory by time) on concurrent variables in a two-way repeated-measures ANOVA. A post-hoc test on each trajectory separately, addressing mean-level changes in concurrent variables across time.

confirmed by a significant and substantial interaction (trajectory by time) effect in a repeated-measure ANOVA (Table 3). In a post-hoc test (Table 3), seven out of eight trajectories were found to reflect significant changes across time (again, both across all three time points and between the 1st to 2nd, and 2nd to 3rd time points). 3.3. A description of change trajectories The eight change trajectories of burnout are described by mean values and standard deviations in Table 2, and graphically illustrated in Fig. 2. In addition, statistical tests of changes in concurrent levels of depressive symptoms and intention to leave the profession are given in Table 3 and levels are displayed in Fig. 2. Furthermore, the eight trajectories were compared using factors taken from the final year of education that presumably predict, contribute to or modify the development of burnout (Table 4). Below, based on all statistical tests presented in Tables 2–4, characterizations of each change trajectory are given. 3.3.1. Trajectory A: unaffected individuals The consistently low levels of burnout characterizing this trajectory (mean levels presented in Table 2 and illustrated as Z-scores in Fig. 2) were also reflected in

concurrent levels of low and non-significantly changing levels of both depressive mood and intention to leave the profession (Table 3, Fig. 2). Data from the baseline assessments in the final semester of higher education (Table 4) also indicated that nurses defining this trajectory were often older (>35), were parents and had former experience of work and education in the field of healthcare. They also differed from the other trajectories with regard to numerous individual, lifestyle, health and stress-related factors. For example, they reported low levels of performance-based self-esteem and negative affectivity, good self-rated health, sleep quality and eating habits, no risk consumption of alcohol, absence of depressive symptoms (both at present and earlier in life), musculoskeletal tension or pain, study exhaustion, disengagement and stress. Also, they felt good about their choice of occupation and that their education had prepared them well for working life. In addition, they had experienced that their introduction to their first employment had been sufficient. 3.3.2. Trajectory B: individuals changing from moderate to low levels of burnout Across time this trajectory reflects moderate levels of burnout that become low across time. These low levels were also reflected in concurrent levels of low and non-

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

301

Table 4 Burnout process trajectories to: (1) demographics and individual factors during education, (2) lifestyle, health and stress during education, (3) educational outcome during education, with the exception of satisfaction with length and content of induction at first employment as a nurse which was assessed one year after graduation (df 7, total n = 997). Bold numbers indicate which cells in the contingency table contribute significantly to the Chi-square test (i.e. adjusted standardized residuals with values >1.96 or < 1.96).

x2

%

1. Demographics and individual factors Age: years <25 25–35 >35 Sex Male Own children Yes Swedish-born Yes Lic Nurse pract Yes Experience of previous healthcare Yes work PBS high Yes Negative affectivity Yes 2. Lifestyle, health and stress Self-rated health Poor Nourishing eating habits No Smoking No Harmful alcohol consumption Yes Depressive mood Yes Previous depressive episode Yes Musculoskeletal tension and pain Yes Back pain Yes Neck and shoulder pain Yes OLBI exhaustion Yes OLBI disengagement Yes Stressors – studies Yes Stressors – choice of occupation Yes 3. Educational outcome Importance of studies Yes Preparedness for nursing No Overall evaluation of school Bad No satisf. Satisfaction with length and content of induction at first employment as a nurse

p

Total

1:A

2:B

3:C

4:D

5:E

6:F

7:G

8:H

28.0 41.2 30.8 10.7 44.0 5.3 42.9 54.9

14.6 36.4 49.0 7.9 67.1 7.4 56.4 66.4

19.7 40.9 39.4 11.4 54.6 6.8 50.8 61.4

36.6 36.1 27.2 10.5 40.6 4.2 39.5 54.3

28.0 42.9 29.2 14.3 36.9 6.3 42.1 52.2

31.1 39.2 29.7 13.5 47.3 5.5 37.8 52.7

25.0 48.1 26.9 6.7 37.3 2.9 46.6 56.7

37.2 44.7 18.1 14.9 30.4 1.1 34.4 53.3

35.6 47.8 16.7 6.7 27.1 6.8 26.1 33.3

59.66

0.001

9.00 61.28 7.64 29.50 27.48

0.253 0.001 0.365 0.001 0.001

42.6 32.0

24.0 8.1

34.8 19.8

33.0 24.1

56.0 41.1

41.9 33.8

45.2 39.4

59.1 57.4

62.9 53.9

69.04 110.07

0.001 0.001

7.0 17.9 75.9 63.7 16.1 44.0 51.1 43.6 50.3 58.5 11.1 58.4 29.8

2.0 9.3 79.3 47.8 3.3 21.5 39.1 25.2 40.4 39.2 3.4 46.7 12.1

4.6 12.9 83.2 59.1 8.3 34.1 56.1 46.2 56.1 57.6 9.8 59.1 19.7

7.4 14.7 79.6 66.1 12.6 37.4 44.5 45.5 44.0 46.1 7.9 55.0 23.0

7.5 22.0 73.1 65.7 16.3 46.8 56.5 45.3 49.7 64.3 10.8 59.7 32.7

4.1 20.3 77.0 70.4 20.3 51.4 51.4 41.9 48.6 62.2 10.8 58.9 30.1

9.6 19.2 67.3 59.2 23.1 53.8 52.9 51.0 63.5 68.0 12.6 54.4 37.9

10.9 26.6 71.3 77.4 25.8 62.4 57.4 47.9 53.2 75.5 23.4 70.2 47.9

12.6 27.0 70.5 72.3 34.8 68.5 58.9 52.2 55.6 78.4 19.3 73.9 55.1

15.32 23.15 13.66 29.24 60.03 80.05 19.10 27.52 19.35 66.23 31.82 24.32 78.64

0.032 0.002 0.058 0.001 0.001 0.001 0.008 0.001 0.007 0.001 0.001 0.001 0.001

77.0 31.1 18.8 24.5

77.3 17.3 9.5 13.9

75.8 24.2 18.5 25.0

82.6 28.3 13.6 20.0

79.9 30.2 21.0 24.2

79.6 27.4 12.2 31.9

78.6 32.4 19.4 25.5

63.8 50.0 30.9 34.5

70.8 53.9 34.1 33.7

15.81 54.79 36.95 21.46

0.027 0.001 0.001 0.003

significantly changing levels of both depressive mood and intention to leave profession. Similarly to Trajectory A, individuals comprising the present trajectory were generally over 35 years of age; were already parents during their studies and had previous training as a nursing assistant. In addition, they had no depressive symptoms during their studies or earlier in life. Nurses comprising Trajectory B were typically not stressed by their choice of profession. 3.3.3. Trajectory C: individuals developing from low to moderate levels of burnout The development from low to moderate levels of burnout characterizing this trajectory was also reflected in concurrent levels of low but significantly increasing levels of both depressive mood and intention to leave the profession. In contrast to A and B, nurses comprising Trajectory C were young (<25). They resembled the A development group in that they had low levels of performance-based self-esteem, no earlier episodes of depressive symptoms and low levels of exhaustion during their studies. Similarly, C neither experienced musculoskeletal tension and pain nor stress in connection with their choice of occupation. They valued their

studies a great deal and assessed their nursing education positively. 3.3.4. Trajectory D: individuals with moderate and rather stable levels of burnout The moderate (but significantly varying) levels of burnout characterizing this trajectory across time were also reflected in concurrent levels of non-significantly changing levels of both depressive mood and intention to leave the profession. During education, individuals in this group differed from the other groups in that they were characterized by high performance-based self-esteem and negative affectivity. 3.3.5. Trajectory E: individuals with increasing burnout, followed by recovery In absolute terms, this group reflected the highest degree of change across the first three years in working life. Individuals constituting Trajectory E had moderate levels of burnout initially that substantially increased in year 2. This was followed by a dramatic decrease, approaching low burnout levels, three years after graduation. These dramatic changes were mirrored by concurrent changes in depressive mood and intention to leave the profession.

302

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

This trajectory did not differ for any of the parameters measured during the last year of nursing education. 3.3.6. Trajectory F: individuals with moderate levels, becoming higher across time The levels of burnout characterizing this trajectory increased from moderate levels between the first and second years in working life and then stabilized at rather high levels of burnout. This change between the first and second years was similar to what was found for Trajectory E and was also reflected in concurrent changes in levels of both depressive mood and intention to leave the profession. In the final semester of education, this development group was characterized by frequent problems with neck and shoulder pain, as well as with exhaustion. Also, they were not so sure about their choice of occupation and this was a source of stress for them. Earlier episodes of depressive symptoms were common among nurses comprising Trajectory F. 3.3.7. Trajectory G: individuals with initially high values of burnout, recovering across time Nurses who started out with the highest levels of burnout in year 1 reported substantial decreases at both subsequent time points. This pattern of high but substantially decreasing levels of burnout was reflected in initially rather high but also decreasing levels of depressive symptoms. However, the rather high levels of intention to leave the profession did not change across time. In the final year of nursing studies this development group differed from other groups, being typically younger and without children of their own. At the same time, they were characterized by numerous problems such as: high levels of performance-based self-esteem and negative affectivity, high levels of depression, exhaustion, disengagement, and stress from studies and choice of occupation. They also suffered from poor sleep quality, bad eating habits and risk consumption of alcohol. Regarding educational outcomes, respondents comprising Trajectory G did not attribute much value to studies or feel that education had prepared them for work as a nurse. Finally, their induction at first employment as a nurse was inefficient and compressed, as reflected by their dissatisfaction with the content and length of induction. 3.3.8. Trajectory H: individuals with high and increasing levels of burnout The development from initial high to even higher levels of burnout characterizing this trajectory was also reflected in concurrent significantly increasing levels of intention to leave the profession. Concurrent levels of depression were high and did not change significantly across time. In the final semester of nursing education, individuals comprising this development group had no children of their own and no former experience of healthcare education and work. In general, older students were not part of this group. Like Trajectory G, they experienced multiple health and lifestyle problems during their studies, i.e. high levels of performance-based self-esteem, negative affectivity, depression, exhaustion, disengagement, and stress from studies as well as of their

choice of occupation. They also reported low sleep quality and poor eating habits, with the addition of poor self-rated health. This development group did not consider their education positively, lacked a sense of preparation for working life and felt dissatisfied with the induction at first employment as a nurse they received at their first employment as a nurse. 4. Discussion In this cohort of newly graduated nurses, the mean burnout levels were rather stable across time. However, we found diverging developmental patterns underlying this mean-level stability at group level. The results indicate that during the first three years of working life, almost every fifth nurse will at some point have extremely high levels of burnout. Interestingly, some have these high levels after the first year and then recover, while others develop them across the first three years. Moreover, for a majority of the novice nurses, the second year of practice seems especially stressful, as substantial increases in burnout levels were found for four out of eight development patterns during this period (and the total mean value reached its highest point here). Importantly, most changes in burnout levels were accompanied by concurrent changes in depressive symptoms and intention to leave the profession. Thus, the different burnout trajectories do not reflect arbitrary patterns of change, but are mirrored by changes as shown in a crucial concomitant symptom, i.e. depression and turnover intentions. Perhaps even more convincing, meaningful predictions were made by variables assessed during the final semester of nursing education, before the measurement of burnout in working life. Previous research has shown how novice professionals’ entry into working life can be experienced as stressful. Even with several years of formal training they may initially feel inadequately prepared for their occupational role (Duchscher, 2009; Laschinger et al., 2009) and their ideals or values may clash with the harsh everyday reality at work (Maben et al., 2006; Mackintosh, 2006). Such a stressful entry has been described as constituting a reality shock (Kramer, 1974), a crisis of competence (i.e. questioning whether one’s own ability is sufficient for the demands of work)(Cherniss, 1980) and a transition shock (Duchscher, 2009). In these studies, the time frame of investigation ranged from the first four to twenty-four months of practice (Cherniss, 1980; Duchscher, 2009; Kramer, 1974; Laschinger et al., 2009; Maben et al., 2006). Contrary to what would be expected from these studies, in the present study no main effect of time was found, as the mean levels were stable across the first three years post graduation. However, the different burnout trajectories found in this study may reflect that such initial adjustment problems may come with different scope and timing. In addition, the prevalence estimate (i.e. about 20% across three years) is much lower than was found in two Canadian studies (i.e. over 60%) (Cho et al., 2006; Laschinger et al., 2009). Even though these differences may reflect actual differences in burnout levels between these countries, it is more possible that they mirror differences due to

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

sampling, measurement and (most likely) the chosen cutoff criteria. Few previous studies address the relationship between demographic characteristics and burnout (Cimiotti et al., 2008; Thomas, 2004), as compared with the many studies on how working environments influence nurses (Aiken et al., 2009; Laschinger and Finegan, 2005; Leiter and Spence Laschinger, 2006) and new graduate burnout (Cho et al., 2006; Kanai-Pak et al., 2008; Laschinger et al., 2009). However, in the present study a significant relationship between age and five of the eight trajectories was found. The two trajectories with the lowest burnout levels throughout the years (A, B) were older (>35) and characterized by being healthy, and having a family life underway already during their studies. This result confirms some earlier findings of a lower risk for burnout among older employees (Maslach et al., 2001; Schaufeli and Enzmann, 1998). However, in earlier studies the relation between age and burnout has been confounded by work experience, resulting in the assumption that burnout tends to be greater earlier on in an individual’s career. Thus, studies have found a higher prevalence of severe burnout among new graduate nurses (<2 years of practice) (Cho et al., 2006; Laschinger et al., 2009), as compared with the prevalence among experienced staff nurses (with an average of 8–18 years of work practice) (Laschinger et al., 2009; Laschinger et al., 2006). On the basis of the results in the present study, where age was still a significant predictor of burnout when the entire sample consisted of new graduate nurses, it is necessary to refine this assumption and further stress the interaction between young age and little work experience. This interaction may have been uncovered here because of the relatively high proportion of young subjects. In several earlier studies young age was defined as being below 30 or sometimes even 40 (Kanai-Pak et al., 2008; Maslach et al., 2001; Schaufeli and Enzmann, 1998). However, in this study the 279 nurses classified as young were under 25 years of age. Although age seems to be an important predictor of future burnout development, it is important to notice the heterogeneity among these youngest nurses. Thus, with respect to the three trajectories with significantly younger nurses, this study indicates that nearly 20% (G, H) of novice nurses experience high levels of burnout, but also that another 20% (C) manage their three first years without severe effects on their health. Already during education these subgroups of young nurses differed on variables reflecting occupational preparedness and educational outcomes. Thus, trajectories G and H comprised young nurses whom more often (comparing to the young nurses comprising the C trajectory) felt stressed due to studies, evaluated their nursing education as deficient, assessed themselves as ill-prepared to work as nurses, and felt pressured by their occupational choice. Both individual (student) and structural (educational) variables may influence the heterogeneity among the youngest students, resulting in different levels of stress over occupational choice and motivation to reach educational goals. Moreover, the heterogeneity among the youngest nurses may also reflect different incentives for choosing a nursing career. The desire to care for and help other people greatly

303

influenced many nurses’ occupational choice (Duffield et al., 2004; Mackintosh, 2006; Price, 2009) but for some it may rather be a ‘‘default choice’’ (e.g. they did not know what else to do, entry qualifications were low, someone else was choosing a nursing career) or a ‘‘stepping stone’’ (e.g. first step to another career, opportunity to travel, good career to fall back on) (Duffield et al., 2004). Here we hypothesize that these different incentives may influence both study engagement and educational outcomes, and prospectively influence future burnout development. But there is a need to further investigate how motivation for becoming a nurse, study engagement, as well as quality and appropriateness of theoretical and clinical training contribute as interacting factors with consequences for future occupational health. This is important since the consequences of unsuccessful adjustment and professional socialization can be costly, for the individual nurse as well as for patient care (Aiken et al., 2002; Estabrooks et al., 2007; Levett-Jones and Lathlean, 2009; Mackintosh, 2006; Vahey et al., 2004). As recommended by other researchers in this field, individual differences need to be taken into account (Deary et al., 2003; Watson et al., 2008). In the present study, various factors that have already been assessed in other studies, such as striving for self-esteem (performancebased self-esteem), negative affectivity, current and lifetime episodes of depression, characterized those with the highest burnout levels one year post graduation (Trajectory G) and those who developed high burnout levels across the first three years (Trajectory H). In contrast, new graduates with the lowest initial levels of burnout (Trajectories A and C) also reported low prevalence of performance-based self-esteem, negative affectivity, and depressive symptoms at the end of their education. As these predictors are probably interrelated and interact with the training and learning environment of higher education, further research on their associations needs to draw upon frameworks that can disentangle their covariation. One such possible framework relates socialization pressures and social influences on individual striving for self-esteem, and consequential problems with professional learning, stress and health (Crocker and Park, 2003; Hallsten et al., 2005). Such an approach is in line with research describing challenging socialization processes present throughout nursing education (Bradby, 1990; Mackintosh, 2006; Wilson and Startup, 1991), indicating that crises and shocks may be present already before entering working life. The possible contribution of study demands, individual factors, educational outcomes and variables related to age on future burnout development also needs to be tested against factors in working life. The dramatic changes after the first year are certainly better explained by additional strains at the workplace as well as private life events (and their interaction). In line with this reasoning, at least two significant types of change, expressed as increases in burnout symptoms during the second year (Trajectories C, E, F and H) and as substantial improvement or recovery after year 2 (Trajectory E and G), warrant further studies. The consistent improvement over time of these individuals who had the highest burnout levels one year after

304

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

graduation may be particularly interesting. The analyses in this paper show that their psychological health increases over time, but they still seem occupied by intentions to leave the profession. Whether they have received help and found ways to cope, developed new skills, or if they have found a better position or changed focus in their occupational (or private) life remains to be studied. By focusing on those who increased dramatically in burnout between years 1 and 2, and then either maintained high levels or recovered completely, we may learn more about crucial factors that moderate how work (and private) situational factors and concurrent commitment to the nursing profession is associated with job burnout in new graduate nurses. Hypotheses derived from The Job Demands-Resources model of burnout (Bakker and Demerouti, 2007) state that these increases in burnout between the first and second year of practice would be predicted by new or increased job demands with no additional resources (e.g. greater workload or new routines resulting in additional time pressures or role stress) or continuing high demands with additional draining of available resources (e.g. increasing tension among colleagues, decreased support for handling emotional labour, spillover from work to family or less time for recovery). In addition, predictions of the sharp decreases would comprise the occurrence of new resources, for example support from leaders and colleagues, supervision or mentorship, or more autonomy. Resources that can help to solve demanding situations increase the likelihood of achieving work goals and fostering the new professionals’ growth and mastery of new skills. 4.1. Strengths and limitations Longitudinal studies of burnout have previously found rather high stability correlations (Shirom, 2005), sometimes of a magnitude close to that found for personality traits (Roberts et al., 2000). In the present study of earlycareer burnout among new graduate nurses, moderately high stability correlations were also found. The search for typical individual change trajectories nevertheless identified eight different patterns that may both further explain/ confirm these stability estimates, but also point to substantial development hidden in mean levels and correlations. However, the internal validity of the classification of growth curves needs to be discussed with respect to common threats, concerning measurement issues, the choice of the most appropriate solution (Bergman et al., 2003) and influence of outliers on the clustering process (Bergman, 1988). First, from a psychometric perspective, one drawback of cluster analytic method is that it is purely descriptive and does not take measurement errors into account. In a sense, a cluster analysis of individual profiles resembles the classification of variables by a principal component analysis, in that both of these methods ignore that psychological measurement is only an indirect assessment of latent variables. Thus, the substantial difference between similar trajectories can be questioned. Moreover, the choice of the most appropriate cluster solution has been recommended to rest upon the combination of how much individual variation

is explained by the solution and how homogenous each cluster is in a solution (Bergman et al., 2003). The chosen solution presented here had both high explanatory power and homogeneity within clusters; but in addition to explanatory power and homogeneity, a meaningful solution is sought for. In other words, rejection of a solution where two clusters with fundamentally different trajectories come together is recommended (Bergman et al., 2003; Milligan, 1996). Therefore, in the present study, the cluster solution with seven clusters was rejected in favour of eight, since the two highest but entirely opposite development curves, G and H, were merged. More data reflecting strains at the workplace as well as private life events (and their interaction) are needed to further validate the differences between these two trajectories. A major limitation in the present study is that possible overlap and interactions among variables used to interpret the classification have not been taken into account. Thus, there is reason to believe that there are important interactions between explanatory variables that may better explain future development of burnout. Preliminary analyses show that the risk of future burnout varies, depending on combinations of age and educational outcome levels (occupational preparedness). For example, the odds ratio for the combination of young age and low occupational preparedness on future burnout development is 9.1 in comparison with an odds ratio of 3.6 for the combination of older age and low occupational preparedness. These odds ratios should be compared with the separate odds ratio for young vs. older age on future burnout (2.7) and the odds ratio for low vs. higher levels of occupational preparedness on future burnout (4.1). Thus, when predicting development of early-career burnout on the basis of variables assessed prior graduation, future research needs to model interactions among crucial predictors or apply a pattern-oriented approach in uncovering the most important clusters or latent classes. 4.2. Conclusion and implications An investigation of burnout symptoms over time disclosed numerous development patterns, some of which were stable while others changed significantly. Hence, this study gave a more nuanced picture of burnout development among new graduate nurses, highlighted by eight different trajectories. As far as the time frame for developing burnout is concerned, this study showed that nearly every second new graduate increased significantly in levels of burnout in their second year post graduation. This finding suggests that earlier studies on new graduate nurses, which look at the first six months (Cowin et al., 2006; Roche et al., 2004) or the first year (Boswell et al., 2004; Cowin and Hengstberger-Sims, 2006; Duchscher, 2008; Fink et al., 2008; Gardner, 1992; Jasper, 1996; Johnstone et al., 2008; Kelly, 1998; Whitehead, 2001; Zinsmeister and Schafer, 2009) after graduation, may miss the plausible subsequent increase. The increased levels two years after working life entry most likely relate to influence of practice work environments. Laschinger and co-workers showed a connection between supportive practice environments, where staff had an overall sense

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

of workplace empowerment, and new graduates’ experience of burnout and engagement at work (Cho et al., 2006; Laschinger et al., 2009). In order to reinforce good professional adjustment for individual nurses, it is important to address special needs for support (Duchscher, 2008, 2009). An examination of what characterizes nurses with differing trajectories of burnout may be useful in order to identify groups of novice nurses who are more likely to be at risk of developing burnout. Thus, an implication of this study is that it would be warranted to look at empowerment structures within nursing education (Siu et al., 2005). Given that individual nursing students have different psychological characteristics and resources, it is challenging to offer an education and induction into working life that suits everyone under the given circumstances and time frames. Nevertheless, it is crucial to discuss how education and practice can collaborate to help novice professionals manage in a way that reduces stressful impact on their health and ability to provide care. Conflicts of interest: None declared. Funding: Grants from AFA Insurance. Ethical approval: Permission to carry out the study was received from the Research Ethics Committee at Karolinska Institutet, Sweden, and all nursing students gave their written informed consent to participate in this study, well aware that they could terminate their participation at any time if they chose to. References Aiken, L.H., Clarke, S.P., Sloane, D.M., Lake, E.T., Cheney, T., 2009. Effects of hospital care environment on patient mortality and nurse outcomes. The Journal of Nursing Administration 39 (7–8 Suppl.), S45–S51. Aiken, L.H., Clarke, S.P., Sloane, D.M., Sochalski, J., Silber, J.H., 2002. Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA: The Journal of the American Medical Association 288 (16), 1987–1993. Aldenderfer, M.S., Blashfield, R.K., 1984. Cluster Analysis. Sage Publications, Beverly Hills and London. Bailey, K.D., 1994. Typologies and Taxonomies: An Introduction to Classifications Techniques. Sage, Thousand Oaks, CA. Bakker, A.B., Demerouti, E., 2007. The job demands-resources model: state of the art. Journal of Managerial Psychology 22 (3), 309–328. Baltes, P.B., Nesselroade, J.R., 1979. History and rationale of longitudinal research. In: Nesselroade, J.R., Baltes, P.B. (Eds.), Longitudinal Research in the Study of Behavior and Development. Academic Press, New York, pp. 1–39. 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. Bergman, H., Kallmen, H., 2002. Alcohol use among Swedes and a psychometric evaluation of the alcohol use disorders identification test. Alcohol Alcoholism 37 (3), 245–251. Bergman, L.R., 1988. You can’t classify all of the people all the time. Multivariate Behavioral Research 23, 425–441. Bergman, L.R., El-Khouri, B., 1986. On the Preparatory Analysis of Multivariate Data Before (Longitudinal) Cluster Analysis. Some Theoretical Considerations and a Data Program. The University of Stockholm. Bergman, L.R., El-Khouri, B.M., 2002. SLEIPNER. A statistical package for pattern-oriented analyses (Version 2.1). Department of Psychology, Stockholm University, Stockholm, Sweden, computer software, pp. 11–134. Bergman, L.R., Magnusson, D., El-Khouri, B.M., 2003. Studying Individual Development in an Interindividual Context: A Person-oriented Approach. LEA, Lawrence Earlbaum Associates, London. Boswell, S., Lowry, L.W., Wilhoit, K., 2004. New nurses’ perceptions of nursing practice and quality patient care. Journal of Nursing Care Quality 19 (1), 76–81.

305

Bradby, M., 1990. Status passage into nursing: another view of the process of socialization into nursing. Journal of Advanced Nursing 15 (10), 1220–1225. Cherniss, C., 1980. Professional Burnout in Human Service Organizations. Praeger Press, New York. Cho, J., Laschinger, H.K., Wong, C., 2006. Workplace empowerment, work engagement and organizational commitment of new graduate nurses. Nursing Leadership (Toronto, Ont.) 19 (3), 43–60. Cimiotti, J.P., Aiken, L.H., Poghosyan, L., 2008. Burnout syndrome in registered nurses. In: Halbesleben, J.R.B. (Ed.), Handbook of Stress and Burnout in Health Care. Nova Science Publishers, Inc., New York, pp. 51–63. Cowin, L.S., Craven, R.G., Johnson, M., Marsh, H.W., 2006. A longitudinal study of student and experienced nurses’ self-concept. Collegian 13 (3), 25–31. Cowin, L.S., Hengstberger-Sims, C., 2006. New graduate nurse self-concept and retention: a longitudinal survey. International Journal of Nursing Studies 43 (1), 59–70. Crocker, J., Park, L.E., 2003. Seeking self-esteem. Construction, maintenance, and protection of self-worth. In: Leary, M.R., Tangney, J.P. (Eds.), Handbook of Self and Identity. Guildford Press, New York, p. p703. Crow, S.M., Smith, S.A., Hartman, S.J., 2005. Attrition in nursing: perspectives from the national survey of college graduates. The Health Care Manager 24 (4), 336–346. Deary, I.J., Watson, R., Hogston, R., 2003. A longitudinal cohort study of burnout and attrition in nursing students. Journal of Advanced Nursing 43 (1), 71–81. Demerouti, E., Bakker, A.B., Nachreiner, F., Schaufeli, W.B., 2001. The job demands-resources model of burnout. Journal of Applied Psychology 86 (3), 499–512. Duchscher, J.E., 2008. A process of becoming: the stages of new nursing graduate professional role transition. The Journal of Continuing Education in Nursing 39 (10), 441–450. Duchscher, J.E., 2009. Transition shock: the initial stage of role adaptation for newly graduated Registered Nurses. Journal of Advanced Nursing 65 (5), 1103–1113. Duffield, C., Pallas, L.O., Aitken, L.M., 2004. Nurses who work outside nursing. Journal of Advanced Nursing 47 (6), 664–671. Estabrooks, C.A., Midodzi, W.K., Cummings, G.G., Wallin, L., 2007. Predicting research use in nursing organizations: a multilevel analysis. Nursing Research 56, S7–S23. Fink, R., Krugman, M., Casey, K., Goode, C., 2008. The graduate nurse experience: qualitative residency program outcomes. The Journal of Nursing Administration 38 (7–8), 341–348. Gardner, D.L., 1992. Career commitment in nursing. Journal of Professional Nursing 8 (3), 155–160. Glass, G.V., Hopkins, K.D., 1996. Statistical Methods in Education and Psychology. Allyn & Bacon, Needham Heights. Gustavsson, J.P., Hallsten, L., Rudman, A., 2010. Early career burnout among nurses: modelling a hypothesized process using an item response approach. International Journal of Nursing Studies 47, 864–875. Gustavsson, J.P., Linder, J., Jo¨nsson, E., Weinryb, R.M., 2003. Construction and assessment of five health-relevant personality traits from a Five-Factor Model perspective. Personality and Individual Differences 35, 69–89. Hallsten, L., Josephson, M., Torge´n, M., 2005. Performance-based selfesteem: a driving force in burnout processes and its assessment. Work and Health 2005, 4. National Institute for Working Life, Stockholm. Jasper, M., 1996. The first year as a staff nurse: the experiences of a first cohort of Project 2000 nurses in a demonstration district. Journal of Advanced Nursing 24 (4), 779–790. Johnstone, M.J., Kanitsaki, O., Currie, T., 2008. The nature and implications of support in graduate nurse transition programs: an Australian study. Journal of Professional Nursing 24 (1), 46–53. Kanai-Pak, M., Aiken, L.H., Sloane, D.M., Poghosyan, L., 2008. Poor work environments and nurse inexperience are associated with burnout, job dissatisfaction and quality deficits in Japanese hospitals. Journal of Clinical Nursing 17 (24), 3324–3329. Kelly, B., 1998. Preserving moral integrity: a follow-up study with new graduate nurses. Journal of Advanced Nursing 28 (5), 1134–1145. Kramer, M., 1974. Reality Shock: Why Nurses Leave Nursing. Mosby, London. Laschinger, H.K., Finegan, J., 2005. Empowering nurses for work engagement and health in hospital settings. The Journal of Nursing Administration 35 (10), 439–449. Laschinger, H.K., Finegan, J., Wilk, P., 2009. New graduate burnout: the impact of professional practice environment, workplace civility, and empowerment. Nursing Economics 27 (6), 377–383.

306

A. Rudman, J.P. Gustavsson / International Journal of Nursing Studies 48 (2011) 292–306

Laschinger, H.K., Gilbert, S., Smith, L.S., Leslie, K., 2010. Towards a comprehensive theory of nurse/patient empowerment: applying Kanter’s empowerment theory to patient care. Journal of Nursing Management 18, 4–13. Laschinger, H.K., Wong, C.A., Greco, P., 2006. The impact of staff nurse empowerment on person-job fit and work engagement/burnout. Nursing Administration Quarterly 30 (4), 358–367. Lee, R.T., Ashforth, B.E., 1996. A meta-analytic examination of the correlates of the three dimensions of job burnout. Journal of Applied Psychology 81 (2), 123–133. Leiter, M.P., Spence Laschinger, H.K., 2006. Relationships of work and practice environment to professional burnout: testing a causal model. Nursing Research 55 (2), 137–146. Levett-Jones, T., Lathlean, J., 2009. ‘Don’t rock the boat’: nursing students’ experiences of conformity and compliance. Nurse Education Today 29 (3), 342–349. Maben, J., Latter, S., Clark, J.M., 2006. The theory-practice gap: impact of professional-bureaucratic work conflict on newly-qualified nurses. Journal of Advanced Nursing 55 (4), 465–477. Mackintosh, C., 2006. Caring: the socialisation of pre-registration student nurses: a longitudinal qualitative descriptive study. International Journal of Nursing Studies 43 (8), 953–962. Maslach, C., Jackson, S.E., Leiter, M.P., 1996. Maslach Burnout Inventory Manual. Consulting Psychologists Press, Palo Alto, CA. Maslach, C., Leiter, M.P., 1997. The Truth About Burnout. How Organizations Causes Personal Stress and What to do About it. Jossey-Bass, New York. Maslach, C., Schaufeli, W.B., Leiter, M.P., 2001. Job burnout. Annual Review of Psychology 52, 397–422. Milligan, G.W., 1996. Clustering validation: results and implications for applied analyses. In: Arabie, P., Hubert, L., De Soete, G. (Eds.), Clustering and Classification. World Scientific Press Publishers Co., River Edge, NJ, pp. 341–375. Poncet, M.C., Toullic, P., Papazian, L., Kentish-Barnes, N., Timsit, J.F., Pochard, F., Chevret, S., Schlemmer, B., Azoulay, E., 2007. Burnout syndrome in critical care nursing staff. American Journal of Respiratory and Critical Care Medicine 175 (7), 698–704. Price, S.L., 2009. Becoming a nurse: a meta-study of early professional socialization and career choice in nursing. Journal of Advanced Nursing 65 (1), 11–19. Roberts, B.W., Walton, K.E., Viechtbauer, W., 2000. The rank-order consistency of personality traits from childhood to old age: a quantitative review of longitudinal studies. Psychological Bulletin 126, 3–25. Roche, J.P., Lamoureux, E., Teehan, T., 2004. A partnership between nursing education and practice: using an empowerment model to retain new nurses. The Journal of Nursing Administration 34 (1), 26–32.

Rudman, A., Omne-Ponten, M., Wallin, L., Gustavsson, P.J., 2010. Monitoring the newly qualified nurses in Sweden: the Longitudinal Analysis of Nursing Education (LANE) study. Human Resources for Health 8 (1), 10. Schaufeli, W.B., Buunk, B.P., 2003. Burnout: an overview of 25 years of research and theorizing. In: Schabracq, M.J., Winnubst, J.A.M., Cooper, C.L. (Eds.), Handbook of Work and Health Psychology. Wiley, Chichester, pp. 383–425. Schaufeli, W.B., Enzmann, D., 1998. The Burnout Companion to Study and Practice: A Critical Analysis. Taylor and Francis, London. Shirom, A., 2003. Job-related burnout. In: Campbell Quick, J., Tetrick, L.E. (Eds.), Handbook of Occupational Health Psychology. American Psychological Association, Washington, pp. 245–264. Shirom, A., 2005. Reflections on the study of burnout. Work and Stress 19, 263–270. Siu, H.M., Laschinger, H.K., Vingilis, E., 2005. The effect of problem-based learning on nursing students’ perceptions of empowerment. The Journal of Nursing Education 44 (10), 459–469. SPSS Inc, 2009. PASW Advanced Statistics 18. SPSS Inc, Chicago. Swider, B., 2010. Born to burnout: a meta-analytic path model of personality, job burnout, and work outcomes. Journal of Vocational Behavior 76 (3), 487–506. Thomas, N.K, 2004. Resident burnout. JAMA: The Journal of the American Medical Association 292 (23), 2880–2889. Vahey, D.C., Aiken, L.H., Sloane, D.M., Clarke, S.P., Vargas, D., 2004. Nurse burnout and patient satisfaction. Medical Care 42 (2 Suppl.), II57– II66. Watson, R., Deary, I., Thompson, D., Li, G., 2008. A study of stress and burnout in nursing students in Hong Kong: a questionnaire survey. International Journal of Nursing Studies 45 (10), 1534–1542. Watson, R., Gardiner, E., Hogston, R., Gibson, H., Stimpson, A., Wrate, R., Deary, I., 2009. A longitudinal study of stress and psychological distress in nurses and nursing students. Journal of Clinical Nursing 18 (2), 270–278. Whitehead, J., 2001. Newly qualified staff nurses’ perceptions of the role transition. British Journal of Nursing 10, 330–339. Wilson, A., Startup, R., 1991. Nurse socialization: issues and problems. Journal of Advanced Nursing 16 (12), 1478–1486. Wa¨nnstro¨m, I., Peterson, U., A˚sberg, M., Nygren, A., Gustavsson, J.P., 2009. Can scales assessing psychological and social factors at work be used across different occupations? Work 34 (1), 3–11. Zapf, D., Seifert, C., Schmutte, B., Mertini, H., Holz, M., 2001. Emotion work and job stressors and their effects on burnout. Psychology & Health 16 (5), 527–545. Zinsmeister, L.B., Schafer, D., 2009. The exploration of the lived experience of the graduate nurse making the transition to registered nurse during the first year of practice. Journal for Nurses in Staff Development 25 (1), 28–34.