nursing burnout research paper

• Open access
• Published: 05 June 2020

Burnout in nursing: a theoretical review

• Chiara Dall’Ora 1 ,
• Jane Ball 2 ,
• Maria Reinius 2 &
• Peter Griffiths 1 , 2  

Human Resources for Health volume  18 , Article number:  41 ( 2020 ) Cite this article

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Workforce studies often identify burnout as a nursing ‘outcome’. Yet, burnout itself—what constitutes it, what factors contribute to its development, and what the wider consequences are for individuals, organisations, or their patients—is rarely made explicit. We aimed to provide a comprehensive summary of research that examines theorised relationships between burnout and other variables, in order to determine what is known (and not known) about the causes and consequences of burnout in nursing, and how this relates to theories of burnout.

We searched MEDLINE, CINAHL, and PsycINFO. We included quantitative primary empirical studies (published in English) which examined associations between burnout and work-related factors in the nursing workforce.

Ninety-one papers were identified. The majority ( n = 87) were cross-sectional studies; 39 studies used all three subscales of the Maslach Burnout Inventory (MBI) Scale to measure burnout. As hypothesised by Maslach, we identified high workload, value incongruence, low control over the job, low decision latitude, poor social climate/social support, and low rewards as predictors of burnout. Maslach suggested that turnover, sickness absence, and general health were effects of burnout; however, we identified relationships only with general health and sickness absence. Other factors that were classified as predictors of burnout in the nursing literature were low/inadequate nurse staffing levels, ≥ 12-h shifts, low schedule flexibility, time pressure, high job and psychological demands, low task variety, role conflict, low autonomy, negative nurse-physician relationship, poor supervisor/leader support, poor leadership, negative team relationship, and job insecurity. Among the outcomes of burnout, we found reduced job performance, poor quality of care, poor patient safety, adverse events, patient negative experience, medication errors, infections, patient falls, and intention to leave.

Conclusions

The patterns identified by these studies consistently show that adverse job characteristics—high workload, low staffing levels, long shifts, and low control—are associated with burnout in nursing. The potential consequences for staff and patients are severe. The literature on burnout in nursing partly supports Maslach’s theory, but some areas are insufficiently tested, in particular, the association between burnout and turnover, and relationships were found for some MBI dimensions only.

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Introduction

The past decades have seen a growing research and policy interest around how work organisation characteristics impact upon different outcomes in nursing. Several studies and reviews have considered relationships between work organisation variables and outcomes such as quality of care, patient safety, sickness absence, turnover, and job dissatisfaction [ 1 , 2 , 3 , 4 ]. Burnout is often identified as a nursing ‘outcome’ in workforce studies that seek to understand the effect of context and ‘inputs’ on outcomes in health care environments. Yet, burnout itself—what constitutes it, what factors contribute to its development, and what the wider consequences are for individuals, organisations, or their patients—is not always elucidated in these studies.

The term burnout was introduced by Freudenberger in 1974 when he observed a loss of motivation and reduced commitment among volunteers at a mental health clinic [ 5 ]. It was Maslach who developed a scale, the Maslach Burnout Inventory (MBI), which internationally is the most widely used instrument to measure burnout [ 6 ]. According to Maslach’s conceptualisation, burnout is a response to excessive stress at work, which is characterised by feelings of being emotionally drained and lacking emotional resources—Emotional Exhaustion; by a negative and detached response to other people and loss of idealism—Depersonalisation; and by a decline in feelings of competence and performance at work—reduced Personal Accomplishment [ 7 ].

Maslach theorised that burnout is a state, which occurs as a result of a prolonged mismatch between a person and at least one of the following six dimensions of work [ 7 , 8 , 9 ]:

Workload: excessive workload and demands, so that recovery cannot be achieved.

Control: employees do not have sufficient control over the resources needed to complete or accomplish their job.

Reward: lack of adequate reward for the job done. Rewards can be financial, social, and intrinsic (i.e. the pride one may experience when doing a job).

Community: employees do not perceive a sense of positive connections with their colleagues and managers, leading to frustration and reducing the likelihood of social support.

Fairness: a person perceiving unfairness at the workplace, including inequity of workload and pay.

Values: employees feeling constrained by their job to act against their own values and their aspiration or when they experience conflicts between the organisation’s values.

Maslach theorised these six work characteristics as factors causing burnout and placed deterioration in employees’ health and job performance as outcomes arising from burnout [ 7 ].

Subsequent models of burnout differ from Maslach’s in one of two ways: they do not conceptualise burnout as an exclusively work-related syndrome; they view burnout as a process rather than a state [ 10 ].

The job resources-demands model [ 11 ] builds on the view of burnout as a work-based mismatch but differs from Maslach’s model in that it posits that burnout develops via two separate pathways: excessive job demands leading to exhaustion, and insufficient job resources leading to disengagement. Along with Maslach and Schaufeli, this model sees burnout as the negative pole of a continuum of employee’s well-being, with ‘work engagement’ as the positive pole [ 12 ].

Among those who regard burnout as a process, Cherniss used a longitudinal approach to investigate the development of burnout in early career human services workers. Burnout is presented as a process characterised by negative changes in attitudes and behaviours towards clients that occur over time, often associated with workers’ disillusionment about the ideals that had led them to the job [ 13 ]. Gustavsson and colleagues used this model in examining longitudinal data on early career nurses and found that exhaustion was a first phase in the burnout process, proceeding further only if nurses present dysfunctional coping (i.e. cynicism and disengagement) [ 14 ].

Shirom and colleagues suggested that burnout occurs when individuals exhaust their resources due to long-term exposures to emotionally demanding circumstances in both work and life settings, suggesting that burnout is not exclusively an occupational syndrome [ 15 , 16 ].

This review aims to identify research that has examined theorised relationships with burnout, in order to determine what is known (and not known) about the factors associated with burnout in nursing and to determine the extent to which studies have been underpinned by, and/or have supported or refuted, theories of burnout.

This was a theoretical review conducted according to the methodology outlined by Campbell et al. and Pare et al. [ 17 , 18 ]. Theoretical reviews draw on empirical studies to understand a concept from a theoretical perspective and highlight knowledge gaps. Theoretical reviews are systematic in terms of searching and inclusion/exclusion criteria and do not include a formal appraisal of quality. They have been previously used in nursing, but not focussing on burnout [ 19 ]. While no reporting guideline for theoretical reviews currently exists, the PRISMA-ScR was deemed to be suitable, with some modifications, to enhance the transparency of reporting for the purposes of this review. The checklist, which can be found as Additional file 2 , has been modified as follows:

Checklist title has been modified to indicate that the checklist has been adapted for theoretical reviews.

Introduction (item 3) has been modified to reflect that the review questions lend themselves to a theoretical review approach.

Selection of sources of evidence (item 9) has been modified to state the process for selecting sources of evidence in the theoretical review.

Limitations (item 20) has been amended to discuss the limitations of the theoretical review process.

Funding (item 22) has been amended to describe sources of funding and the role of funders in the theoretical review.

All changes from the original version have been highlighted.

Literature search

A systematic search of empirical studies examining burnout in nursing published in journal articles since 1975 was performed in May 2019, using MEDLINE, CINAHL, and PsycINFO. The main search terms were ‘burnout’ and ‘nursing’, using both free-search terms and indexed terms, synonyms, and abbreviations. The full search and the total number of papers identified are in Additional file 1 .

We included papers written in English that measured the association between burnout and work-related factors or outcomes in all types of nurses or nursing assistants working in a healthcare setting, including hospitals, care homes, primary care, the community, and ambulance services. Because there are different theories of burnout, we did not restrict the definition of burnout according to any specific theory. Burnout is a work-related phenomenon [ 8 ], so we excluded studies focussing exclusively on personal factors (e.g. gender, age). Our aim was to identify theorised relationships; therefore, we excluded studies which were only comparing the levels of burnout among different settings (e.g. in cancer services vs emergency departments). We excluded literature reviews, commentaries, and editorials.

Data extraction and quality appraisal

The following data were extracted from included studies: country, setting, sample size, staff group, measure of burnout, variables the relationship with burnout was tested against, and findings against the hypothesised relationships. One reviewer (MEB) extracted data from all the studies, with CDO and JEB extracting 10 studies each to check for agreement in data extraction. In line with the theoretical review methodology, we did not formally assess the quality of studies [ 19 ]. However, in Additional file 3 , we have summarised the key aspects of quality for each study, covering generalisability (e.g. a multisite study with more than 500 participants); risk of bias from common methods variance (e.g. burnout and correlates assessed with the same survey. This bias arises when there is a shared (common) variance because of the common method rather than a true (causal) association between variables); evidence of clustering (e.g. nurses nested in wards, wards nested in hospitals); and evidence of statistical adjustment (e.g. the association between burnout and correlates has been adjusted to control for potentially influencing variables). It should be noted that cells are shaded in green when the above-mentioned quality standards have been met, and in red when they have not. In the ‘Discussion’ section, we offer a reflection on the common limitations of research in the field and present a graphic summary of the ‘strength of evidence’ in Fig. 1 .

Graphical representation of strength of relationships with burnout

Data synthesis

Due to the breadth of the evidence, we summarised extracted data by identifying common categories through a coding frame. The starting point of the coding frame was the burnout multidimensional theory outlined by Maslach [ 7 ]. We then considered whether the studies’ variables fit into Maslach’s categorisation, and where they did not, we created new categories. We identified nine broad categories: (1) Areas of Worklife; (2) Workload and Staffing Levels; (3) Job Control, Reward, Values, Fairness, and Community; (4) Shift Work and Working Patterns; (5) Psychological Demands and Job Complexity; (6) Support Factors: Working Relationships and Leadership; (7) Work Environment and Hospital Characteristics; (8) Staff Outcomes and Job Performance; and (9) Patient Care and Outcomes. In the literature, categories 1–7 were treated as predictors of burnout and categories 8 and 9 as outcomes, with the exception of missed care and job satisfaction which were treated both as predictors and outcomes.

When the coding frame was finalised, CDO and MLR applied it to all studies. Where there was disagreement, a third reviewer (JEB) made the final decision.

The database search yielded 12 248 studies, of which 11 870 were rapidly excluded as either duplicates or titles and/or abstract not meeting the inclusion criteria. Of the 368 studies accessed in full text, 277 were excluded, and 91 studies were included in the review. Figure 2 presents a flow chart of the study selection.

Study selection flow chart

The 91 studies identified covered 28 countries; four studies included multiple countries, and in one, the country was not reported. Most were from North America ( n = 35), Europe ( n = 28), and Asia ( n = 18).

The majority had cross-sectional designs ( n = 87, 97%); of these, 84 were entirely survey-based. Three studies were longitudinal. Most studies were undertaken in hospitals ( n = 82). Eight studies surveyed nurses at a national level, regardless of their work setting.

Sample sizes ranged from hundreds of hospitals (max = 927) with hundreds of thousands of nurses (max = 326 750) [ 20 ] to small single-site studies with the smallest sample being 73 nurses [ 21 ] (see Additional file 3 ).

The relationships examined are summarised in Table 1 .

Measures of burnout

Most studies used the Maslach Burnout Inventory Scale ( n = 81), which comprises three subscales reflecting the theoretical model: Emotional Exhaustion, Depersonalisation, and reduced Personal Accomplishment. However, less than half (47%, n = 39) of the papers measured and reported results with all three subscales. Twenty-three papers used the Emotional Exhaustion subscale only, and 11 papers used the Emotional Exhaustion and Depersonalisation subscales. In nine studies, the three MBI subscales were summed up to provide a composite score of burnout, despite Maslach and colleagues advising against such an approach [ 22 ].

Five studies used the Copenhagen Burnout Inventory (CBI) [ 23 ]. This scale consists of three dimensions of burnout: personal, work-related, and client-related. Two studies used the Malach-Pines Scale [ 24 ], and one used the burnout subscale of the Professional Quality of Life Measure (ProQoL5) scale, which posits burnout as an element of compassion fatigue [ 25 ]. Two studies used idiosyncratic measures of burnout based on items from other instruments [ 20 , 26 ].

Factors examined in relation to burnout: an overview

The studies which tested the relationships between burnout and Maslach’s six areas of worklife—workload, control, reward, community, fairness, and values—typically supported Maslach’s theory that these areas are predictors of burnout. However, some evidence is based only on certain MBI dimensions. High scores on the Areas of Worklife Scale [ 27 ] (indicating a higher degree of congruence between the job and the respondent) were associated with less likelihood of burnout, either directly [ 28 , 29 ] or through high occupational coping self-efficacy [ 30 ] and presence of civility norms and co-worker incivility [ 31 ].

The majority of studies looking at job characteristics hypothesised by the Maslach model considered workload ( n = 31) and job control and reward ( n = 10). While only a few studies ( n = 9) explicitly examined the hypothesised relationships between burnout and community, fairness, or values, we identified 39 studies that covered ‘supportive factors’ including relationships with colleagues and leadership.

A large number of studies included factors that fall outside of the Maslach model. Six main areas were identified:

Working patterns and shifts working ( n = 15)

Features inherent in the job such as psychological demand and complexity ( n = 24)

Job support from working relationships and leadership ( n = 39)

Hospital or environmental characteristics ( n = 28)

Staff outcomes and job performance ( n = 33)

Patient outcomes ( n = 17)

Individual attributes (personal or professional) ( n = 16)

Workload and staffing levels

Workload and characteristics of jobs that contribute to workload, such as staffing levels, were the most frequently examined factor in relation to burnout. Thirty studies found an association between high workload and burnout.

Of these, 13 studies looked specifically at measures of workload as a predictor of burnout. Workload was associated with Emotional Exhaustion in five studies [ 32 , 33 , 34 , 35 , 36 ], with some studies also reporting a relationship with Depersonalisation, and others Cynicism. Janssen reported that ‘mental work overload’ predicted Emotional Exhaustion [ 37 ]. Three studies concluded that workload is associated with both Emotional Exhaustion and Depersonalisation [ 38 , 39 , 40 ]. Kitaoka-Higashiguchi tested a model of burnout and found that heavy workload predicted Emotional Exhaustion, which in turn predicted Cynicism [ 41 ]. This was also observed in a larger study by Greengrass et al. who found that high workload was associated with Emotional Exhaustion, which consequently predicted Cynicism [ 42 ]. One study reported no association between workload and burnout components [ 43 ], and one study found an association between manageable workload and a composite burnout score [ 44 ].

Further 15 studies looked specifically at nurse staffing levels, and most reported that when nurses were caring for a higher number of patients or were reporting staffing inadequacy, they were more likely to experience burnout. No studies found an association between better staffing levels and burnout.

While three studies did not find a significant association with staffing levels [ 32 , 45 , 46 ], three studies found that higher patient-to-nurse ratios were associated with Emotional Exhaustion [ 47 , 48 , 49 ], and in one study, higher patient-to-nurse-ratios were associated with Emotional Exhaustion, Depersonalisation, and Personal Accomplishment [ 50 ]. One study concluded that Emotional Exhaustion mediated the relationship between patient-to-nurse ratios and patient safety [ 51 ]. Akman and colleagues found that the lower the number of patients nurses were responsible for, the lower the burnout composite score [ 52 ]. Similar results were highlighted by Faller and colleagues [ 53 ]. Lower RN hours per patient day were associated with burnout in a study by Thompson [ 20 ].

When newly qualified RNs reported being short-staffed, they were more likely to report Emotional Exhaustion and Cynicism 1 year later [ 54 ]. In a further study, low staffing adequacy was associated with Emotional Exhaustion [ 55 ]. Similarly, Leineweber and colleagues found that poor staff adequacy was associated with Emotional Exhaustion, Depersonalisation, and Personal Accomplishment [ 56 ]. Leiter and Spence Laschinger explored the relationship between staffing adequacy and all MBI subscales and found that Emotional Exhaustion mediated the relationship between staffing adequacy and Depersonalisation [ 57 ]. Time pressure was investigated in three studies, which all concluded that reported time pressure was associated with Emotional Exhaustion [ 58 , 59 , 60 ].

In summary, there is evidence that high workload is associated with Emotional Exhaustion, nurse staffing levels are associated with burnout, and time pressure is associated with Emotional Exhaustion.

Job control, reward, values, fairness, and community

Having control over the job was examined in seven studies. Galletta et al. found that low job control was associated with all MBI subscales [ 40 ], as did Gandi et al. [ 61 ]. Leiter and Maslach found that control predicted fairness, reward, and community, and in turn, fairness predicted values, and values predicted all MBI subscales [ 35 ]. Low control predicted Emotional Exhaustion only for nurses working the day shift [ 62 ], and Emotional Exhaustion was significantly related to control over practice setting [ 63 ]; two studies reported no effect of job control on burnout [ 44 , 64 ].

Reward predicted Cynicism [ 35 ] and burnout on a composite score [ 44 ]. Shamian and colleagues found that a higher score in the effort and reward imbalance scale was associated with Emotional Exhaustion, and higher scores in the effort and reward imbalance scale were associated with burnout measured by the CBI [ 65 ].

Value congruence refers to a match between the requirements of the job and people’s personal principles [ 7 ]. Value conflicts were related with a composite score of burnout [ 44 ], and one study concluded that nurses with a high value congruence reported lower Emotional Exhaustion than those with a low value congruence, and nurses with a low value congruence experienced more severe Depersonalisation than nurses with a high value congruence [ 66 ]. Low value congruence was a predictor of all three MBI dimensions [ 35 ] and of burnout measured with the Malach-Pines Burnout Scale [ 67 ]. Two studies considered social capital, defined as a social structure that benefits its members including trust, reciprocity, and a set of shared values, and they both concluded that lower social capital in the hospital-predicted Emotional Exhaustion [ 33 , 36 ]. A single study showed fairness predicted values, which in turn predicted all MBI Scales [ 35 ]. Two studies looked at community, and one found that community predicts a composite score of burnout [ 44 ], while the other found no relationships [ 35 ].

While not directly expressed in the terms described by Maslach, other studies demonstrate associations with possible causal factors, many of which are reflected in Maslach’s theory.

In summary, there is evidence that control over the job is associated with reduced burnout, and value congruence is associated with reduced Emotional Exhaustion and Depersonalisation.

Working patterns and shift work

Shift work and working patterns variables were considered by 15 studies. Overall, there was mixed evidence on the relationship between night work, number of hours worked per week, and burnout, with more conclusive results regarding the association between long shifts and burnout, and the potential protective effect of schedule flexibility.

Working night shifts was associated with burnout (composite score) [ 68 ] and Emotional Exhaustion [ 62 ], but the relationship was not significant in two studies [ 69 , 70 ]. Working on permanent as opposed to rotating shift patterns did not impact burnout [ 71 ], but working irregular shifts did impact a composite burnout score [ 72 ]. When nurses reported working a higher number of shifts, they were more likely to report higher burnout composite scores [ 68 ], but results did not generalise in a further study [ 69 ]. One study found working that overtime was associated with composite MBI score [ 73 ]. On-call requirement was not significantly associated with any MBI dimensions [ 71 ].

The number of hours worked per week was not a significant predictor of burnout according to two studies [ 25 , 53 ], but having a higher number of weekly hours was associated with Emotional Exhaustion and Depersonalisation in one study [ 70 ]. Long shifts of 12 h or more were associated with all MBI subscales [ 74 ] and with Emotional Exhaustion [ 49 , 75 ]. A study using the ProQoL5 burnout scale found that shorter shifts were protective of burnout [ 25 ].

Having higher schedule flexibility was protective of Emotional Exhaustion [ 46 ], and so was the ability to schedule days off for a burnout composite score [ 76 ]. Having more than 8 days off per month was associated with lower burnout [ 69 ]. Stone et al. found that a positive scheduling climate was protective of Emotional Exhaustion only [ 77 ].

In summary, we found an association between ≥ 12-h shifts and Emotional Exhaustion and between schedule flexibility and reduced Emotional Exhaustion.

Psychological demands and job complexity

There is evidence from 24 studies that job demands and aspects intrinsic to the job, including role conflict, autonomy, and task variety, are associated with some burnout dimensions.

Eight studies considered psychological demands. The higher the psychological demands, the higher the likelihood of experiencing all burnout dimensions [ 72 ], and high psychological demands were associated with higher odds of Emotional Exhaustion [ 62 , 78 ]. Emotional demands, in terms of hindrances, had an effect on burnout [ 67 ]. One study reported that job demands, measured with the Effort-Reward Imbalance Questionnaire, were correlated with all burnout dimensions [ 79 ], and similarly, Garcia-Sierra et al. found that demands predict burnout, measured with a composite scale of Emotional Exhaustion and Cynicism [ 80 ]. According to one study, job demands were not associated with burnout [ 73 ], and Rouxel et al. concluded that the higher the job demands, the higher the impact on both Emotional Exhaustion and Depersonalisation [ 64 ].

Four studies looked at task nature and variety, quality of job content, in terms of skill variety, skill discretion, task identity, task significance, influenced Emotional Exhaustion through intrinsic work motivation [ 37 ]. Skill variety and task significance were related to Emotional Exhaustion; task significance was also related to Personal Accomplishment [ 60 ]. Having no administrative tasks in the job was associated with a reduced likelihood to experience Depersonalisation [ 71 ]. Higher task clarity was associated with reduced levels of Emotional Exhaustion and increased Personal Accomplishment [ 58 ].

Patient characteristics/requirements were investigated in four papers. When nurses were caring for suffering patients and patients who had multiple requirements, they were more likely to experience Emotional Exhaustion and Cynicism. Similarly, caring for a dying patient and having a high number of decisions to forego life-sustaining treatments were associated with a higher likelihood of burnout (measured with a composite score) [ 76 ]. Stress resulting from patient care was associated with a composite burnout score [ 73 ]. Patient violence also had an impact on burnout, measured with CBI [ 81 ], as did conflict with patients [ 76 ].

Role conflict is a situation in which contradictory, competing, or incompatible expectations are placed on an individual by two or more roles held at the same time. Role conflict predicted Emotional Exhaustion [ 41 ], and so it did in a study by Konstantinou et al., who found that role conflict was associated with Emotional Exhaustion and Depersonalisation [ 34 ]; Levert and colleagues reported that role conflict correlated with Emotional Exhaustion, Depersonalisation, and Personal Accomplishment. They also considered role ambiguity, which correlated with Emotional Exhaustion and Depersonalisation, but not Personal Accomplishment [ 39 ]. Andela et al. investigated the impact of emotional dissonance, defined as the mismatch between the emotions that are felt and the emotions required to be displayed by organisations. They reported that emotional dissonance is a mediator between job aspects (i.e. workload, patient characteristics, and team issues) and Emotional Exhaustion and Cynicism. Rouxel et al. found that perceived negative display rules were associated with Emotional Exhaustion [ 64 ].

Autonomy related to Emotional Exhaustion and Depersonalisation [ 60 ], and in another study, it only related to Depersonalisation [ 43 ]. Low autonomy impacted Emotional Exhaustion via organisational trust [ 82 ]. Autonomy correlated with burnout [ 67 ]. There was no effect of autonomy on burnout according to two studies [ 58 , 63 ]. Low decision-making at the ward level was associated with all MBI subscales [ 77 ]. Decision latitude impacted Personal Accomplishment only [ 36 ], and in one study, it was found to be related to Emotional Exhaustion [ 78 ]. High decision latitude was associated with Personal Accomplishment [ 41 ] and low Emotional Exhaustion [ 33 ].

Overall, high job and psychological demands were associated with Emotional Exhaustion, as was role conflict. Patient complexity was associated with burnout, while task variety, autonomy, and decision latitude were protective of burnout.

Working relationships and leadership

Overall, evidence from 39 studies supports that having positive support factors and working relationships in place, including positive relationships with physicians, support from the leader, positive leadership style, and teamwork, might play a protective role towards burnout.

The quality of the relationship with physicians was investigated by 12 studies. In two studies, having negative relationships with physicians was associated with all MBI dimensions [ 77 , 83 ]; quality of nurse-physician relationship was associated with Emotional Exhaustion and Depersonalisation, but not PA [ 50 ]. Two studies found an association with Emotional Exhaustion only [ 55 , 84 ], and one concluded that quality of relationship with physicians indirectly supported PA [ 36 ]. This was also found by Leiter and Laschinger, who found that positive nurse-physician collaborations predicted Personal Accomplishment [ 57 , 85 ]. When burnout was measured with composite scores of MBI and a not validated scale, two studies reported an association with nurse-physician relationship [ 20 , 76 ], and two studies found no associations [ 56 , 63 ].

Having support from the supervisor or leader was considered in 12 studies, which found relationships with different MBI dimensions. A relationship between low support from nurse managers and all MBI subscales was observed in one study [ 77 ], while two studies reported it is a protective factor from Emotional Exhaustion only [ 58 , 83 ], and one that it was also associated with Depersonalisation [ 86 ]. Kitaoka-Higashiguchi reported an association only with Cynicism [ 41 ], and Jansen et al. found it was only associated with Depersonalisation and Personal Accomplishment [ 60 ]. Van Bogaert and colleagues found that support from managers predicted low Emotional Exhaustion and high Personal Accomplishment [ 84 ], but in a later study, it only predicted high Personal Accomplishment [ 36 ]. Regarding the relationship with the manager, it had a direct effect on Depersonalisation, and it moderated the effect of time pressure on Emotional Exhaustion and Depersonalisation [ 59 ]; a protective effect of a quality relationship with the head nurse on a composite burnout score was also reported [ 76 ]. Two studies using different burnout scales found an association between manager support and reduced burnout [ 25 , 67 ]. Low trust in the leader showed a negative impact on burnout, measured with a composite score [ 87 ]. Two further studies focused on the perceived nurse manager’s ability: authors found that it was related to Emotional Exhaustion [ 46 ], and Emotional Exhaustion and Personal Accomplishment [ 50 ].

Fourteen studies looked at the leadership style and found that it affects burnout through different pathways and mechanisms. Boamah et al. found that authentic leadership—described as leaders who have high self-awareness, balanced processing, an internalised moral perspective, and transparency—predicted higher empowerment, which in turn predicted lower levels of Emotional Exhaustion and Cynicism a year later [ 54 ]. Authentic leadership had a negative direct effect on workplace bullying, which in turn had a direct positive effect on Emotional Exhaustion [ 88 ]. Effective leadership predicted staffing adequacy, which in turn predicted Emotional Exhaustion [ 57 , 85 ]. Authentic leadership predicted all areas of worklife, which in turn predicted all MBI dimensions of burnout [ 30 ], and a similar pathway was identified by Laschiner and Read, although authentic leadership impacted Emotional Exhaustion only and it was also through civility norms and co-worker incivility [ 31 ]. Emotional Exhaustion mediated the relationship between authentic leadership and intention to leave the job [ 89 ]. ‘Leader empowering behaviour’ had an indirect effect on Emotional Exhaustion through structural empowerment [ 29 ], and empowering leadership predicted trust in the leader, which in turn was associated with burnout composite score [ 87 ]. Active management-by-exception was beneficial for Depersonalisation and Personal Accomplishment, passive laissez-faire leadership negatively affected Emotional Exhaustion and Personal Accomplishment, and rewarding transformational leadership protected from Depersonalisation [ 90 ]. Contrary to this, Madathil et al. found that transformational leadership protected against Emotional Exhaustion, but not Depersonalisation, and promoted Personal Accomplishment [ 43 ]. Transformational leadership predicted positive work environments, which in turn predicted lower burnout (composite score) [ 44 ]. Positive leadership affected Emotional Exhaustion and Depersonalisation [ 56 ] and burnout measured with a non-validated scale [ 20 ].

Teamwork and social support were also explored. Co-worker cohesion was only related to Depersonalisation [ 58 ]; team collaboration problems predicted negative scores on all MBI subscales [ 38 ], and workplace support protected from Emotional Exhaustion [ 72 ]. Similarly, support received from peers had a protective effect on Emotional Exhaustion [ 60 ]. Collegial support was related to Emotional Exhaustion and Personal Accomplishment [ 39 ], and colleague support protected from burnout [ 67 ]. Interpersonal conflict affected Emotional Exhaustion through role conflict, but co-worker support had no effect on any burnout dimensions [ 41 ], and similarly, co-worker incivility predicted Emotional Exhaustion [ 31 ], and so did bullying [ 88 ]. Poor team communication was associated with all MBI dimensions [ 40 ], staff issues predicted burnout measured with a composite score [ 73 ], and so did verbal violence from colleagues [ 68 ]. One study found that seeking social support was not associated with any of the burnout dimensions, while another study found that low social support predicted Emotional Exhaustion [ 37 ], and social support was associated with lower Emotional Exhaustion and higher Personal Accomplishment [ 21 ]. Vidotti et al. found an association between low social support and all MBI dimensions [ 62 ].

Work environment and hospital characteristics

Eleven studies were considering the work environment measured with the PES-NWI scale [ 91 ], where higher scores indicate positive work environments. Five studies comprising diverse samples and settings concluded that the better rated the work environment, the lower the likelihood of experiencing Emotional Exhaustion [ 32 , 47 , 49 , 51 , 92 ], and four studies found the same relationship, but on both Emotional Exhaustion and Depersonalisation [ 50 , 66 , 93 , 94 ]; only one study concluded there is an association between work environment and all MBI dimensions [ 95 ]. Negative work environments affected burnout (measured with a composite score) via job dissatisfaction [ 96 ]. One study looked at organisational characteristics on a single scale and found that a higher rating of organisational characteristics predicted lower Emotional Exhaustion [ 82 ]. Environmental uncertainty was related to all MBI dimensions [ 86 ].

Structural empowerment was also considered in relation to burnout: high structural empowerment led to lower Emotional Exhaustion and Cynicism via staffing levels and worklife interference [ 54 ]; in a study using a similar methodology, structural empowerment affected Emotional Exhaustion via Areas of Worklife [ 29 ]. The relationship between Emotional Exhaustion and Cynicism was moderated by organisational empowerment [ 40 ], and organisational support had a protective effect on burnout [ 67 ]. Hospital management and organisational support had a direct effect on Emotional Exhaustion and Personal Accomplishment [ 84 ]. Trust in the organisation predicted lower levels of Emotional Exhaustion [ 82 ] and of burnout measured with a composite MBI score [ 87 ].

Three studies considered whether policy involvement had an effect on burnout. Two studies on the same sample found that having the opportunity to participate in policy decisions was associated with reduced burnout (all subscales) [ 57 , 85 ], and one study did not report results for the association [ 20 ]. Emotional Exhaustion mediated the relationship between nurses’ participation in hospital affairs and their intention to leave the job [ 97 ]; a further study did not found an association between participation in hospital affairs and Emotional Exhaustion, but only with Personal Accomplishment [ 50 ]. Lastly, one study investigated participation in research groups and concluded it was associated with reduced burnout measured with a composite score [ 76 ].

There was an association between opportunity for career advancement and all MBI dimensions [ 77 ]; however, another study found that having promotion opportunities was not related to burnout [ 79 ]. Moloney et al. found that professional development was not related to burnout [ 67 ]. Two studies considered pay. In one study, no effect was found on any MBI dimension [ 73 ], and a very small study ( n = 78 nurses) reported an effect of satisfaction with pay on Emotional Exhaustion and Depersonalisation [ 34 ]. Job insecurity predicted Depersonalisation and PA [ 79 ].

When the hospital adopted nursing models of care rather than medical models of care, nurses were more likely to report high levels of Personal Accomplishment [ 57 , 85 ]. However, another study found no significant relationship [ 20 ]. Regarding ward and hospital type, Aiken and Sloane found that RNs working in specialised AIDS units reported lower levels of Emotional Exhaustion [ 98 ]; however, ward type was not found to be significantly associated with burnout in a study on temporary nurses [ 53 ]. Working in different ward settings was not associated with burnout, but working in hospitals as opposed to in primary care was associated with lower Emotional Exhaustion [ 71 ]. Working in a small hospital was associated with a lower likelihood of Emotional Exhaustion, when compared to working in a community hospital [ 63 ]. Faller’s study also concluded that working in California was a significant predictor of reduced burnout.

When the hospitals’ investment in the quality of care was considered, one study found that having foundations for quality of care was associated with reduced Emotional Exhaustion only [ 50 ], but in another study, foundations for quality of care were associated with all MBI dimensions [ 83 ]. Working in a Magnet hospital was not associated with burnout [ 53 ].

In summary, having a positive work environment (generally work environments scoring higher on the PES-NWI scale) was associated with reduced Emotional Exhaustion, and so was higher structural empowerment. However, none of the organisational characteristics at the hospital level was consistently associated with burnout.

Staff outcomes and job performance

Nineteen studies considered the impact of burnout on intention to leave. Two studies found that Emotional Exhaustion and Cynicism had a direct effect on turnover intentions [ 28 , 99 ], and four studies reported that only Emotional Exhaustion affected intentions to leave the job [ 21 , 32 , 37 , 100 ], with one of these indicating that Emotional Exhaustion affected also intention to leave the organisation [ 32 ], but one study did not replicate such findings [ 101 ] and concluded that only Cynicism was associated with intention to leave the job and nursing. Similarly, one study found that Cynicism was directly related to intention to leave [ 35 ]. A further study found that Emotional Exhaustion affected turnover intentions via job satisfaction [ 88 ], and one article reported that Emotional Exhaustion mediated the effect of authentic leadership on intention to leave [ 89 ]. Emotional Exhaustion was a mediator between nurses’ involvement with decisions and intention to leave the organisation [ 97 ]. Burnout measured on a composite score was associated with a higher intention to leave [ 96 ]. Laeeque et al. reported that burnout, captured with CBI, related to intention to leave [ 81 ]; Estryn-Behar et al. used the same scale to measure burnout and found that high burnout was associated with higher intention to leave in all countries, except for Slovakia [ 102 ]. Burnout, measured with the Malach-Pines Scale, was associated with intention to quit, and stronger associations were found for nurses who had higher perceptions of organisational politics [ 103 ]. Burnout (Malach-Pines Scale) predicted both the intention to leave the job and nursing [ 67 ]. Three studies investigated the relationship between burnout and intention to leave; one of these aggregated all job outcomes in a single variable (i.e. job satisfaction, intention to leave the hospital, applied for another job, and intention to leave nursing) and reported that Depersonalisation and Personal Accomplishment predict job outcomes [ 84 ]; they replicated a similar approach and found the same associations [ 36 ]. They later found that all MBI dimensions were associated with leaving the nursing profession [ 104 ]. Only one study in a sample of 106 nurses from one hospital found an association between Depersonalisation and turnover within 2 years [ 105 ].

Two studies looked at the effect of burnout on job performance: one found a negative association between burnout (measured with CBI) and both task performance and contextual performance [ 106 ]. Only Emotional Exhaustion was associated with self-rated and supervisor-rated job performance of 73 RNs [ 21 ]. Missed care was investigated in three studies, and it was found to be both predictor of Emotional Exhaustion [ 32 ], an outcome of burnout [ 20 , 103 ].

Four studies considered sickness absence. When RNs had high levels of Emotional Exhaustion, they were more likely to experience short-term sickness absence (i.e. 1–10 days of absence), which was obtained from hospital administrative records. Similarly, Emotional Exhaustion was associated with seven or more days of absence in a longitudinal study [ 105 ]. Emotional Exhaustion was significantly associated with reported mental health absenteeism, but not reported physical health absenteeism, and sickness absence from administrative records [ 21 ]. One study did not find any meaningful relationships between burnout and absenteeism [ 107 ].

Emotional Exhaustion was a significant predictor of general health [ 73 ], and in a further study, both Emotional Exhaustion and Personal Accomplishment were associated with perceived health [ 70 ]. Final-year nursing students who experienced health issues were more likely to develop high burnout when entering the profession [ 26 ]. When quality of sleep was treated both as a predictor and outcome of burnout, relationships were found in both instances [ 106 ].

Focussing on mental health, one study found that burnout predicted mental health problems for newly qualified nurses [ 30 ], and Emotional Exhaustion and Cynicism predicted somatisation [ 42 ]. Depressive symptoms were predictive of Emotional Exhaustion and Depersonalisation, considering therefore depression as a predictor of burnout [ 108 ]. Rudman and Gustavsson also found that having depressive mood and depressive episodes were common features of newly qualified nurses who developed or got worse levels of burnout throughout their first years in the profession [ 26 ]. Tourigny et al. considered depression as a predictor and found it was significantly related to Emotional Exhaustion [ 107 ].

Eleven studies considered job satisfaction: of these, three treated job satisfaction as a predictor of burnout and concluded that higher levels of job satisfaction were associated with a lower level of composite burnout scores [ 52 , 96 ] and all MBI dimensions [ 94 ]. According to two studies, Emotional Exhaustion and Cynicism predicted job dissatisfaction [ 54 , 101 ], while four studies reported that Emotional Exhaustion only was associated with increased odds to report job dissatisfaction [ 73 , 82 , 88 , 100 ]; one study reported that Cynicism only was associated with job dissatisfaction [ 99 ]. Rouxel et al. did not find support in their hypothesised model that Emotional Exhaustion and Depersonalisation predicted job satisfaction [ 64 ].

In summary, considering 39 studies, there is conflicting evidence on the direction of the relationship between burnout and missed care, mental health, and job satisfaction. An association between burnout and intention to leave was found, although only one small study reported an association between burnout and turnover. A moderate relationship was found for the effect of burnout on sickness absence, job performance, and general health.

Patient care and outcomes

Among the patient outcomes of burnout, quality of care was investigated by eight studies. Two studies in diverse samples and settings reported that high Emotional Exhaustion, high Depersonalisation, and low Personal Accomplishment were associated with poor quality of care [ 109 , 110 ], but one study found that only Personal Accomplishment was related to better quality of care at the last shift [ 104 ]; Emotional Exhaustion and Cynicism predict low quality of care [ 54 ]; two articles reported that Emotional Exhaustion predicts poor nurse ratings of quality of care [ 82 , 84 ]. A high burnout composite score predicted poor nurse-assessed quality of care [ 96 ]. In one instance, no associations were found between any of the burnout dimensions and quality of care [ 36 ].

Five studies considered aspects of patient safety: burnout was correlated with negative patient safety climate [ 111 ]. Emotional Exhaustion and Depersonalisation were both associated with negative patient safety grades and safety perceptions [ 112 ], and burnout fully mediated the relationship between depression and individual-level safety perceptions and work area/unit level safety perceptions [ 108 ]. Emotional Exhaustion mediated the relationship between workload and patient safety [ 51 ], and a higher composite burnout score was associated with lower patient safety ratings [ 113 ].

Regarding adverse events, high DEP and low Personal Accomplishment predicted a higher rate of adverse events [ 85 ], but in another study, only Emotional Exhaustion predicted adverse events [ 51 ]. When nurses were experiencing high levels of Emotional Exhaustion, they were less likely to report near misses and adverse events, and when they were experiencing high levels of Depersonalisation, they were less likely to report near misses [ 112 ].

All three MBI dimensions predicted medication errors in one study [ 109 ], but Van Bogaert et al. found that only high levels of Depersonalisation were associated with medication errors [ 104 ]. High scores in Emotional Exhaustion and Depersonalisation predicted infections [ 109 ]. Cimiotti et al. found that Emotional Exhaustion was associated with catheter-associated urinary tract infections and surgical site infections [ 114 ], while in another study, Depersonalisation was associated with nosocomial infections [ 104 ]. Lastly, patient falls were also explored, and Depersonalisation and low Personal Accomplishment were significant predictors in one study [ 109 ], while in a further study, only Depersonalisation was associated with patient falls [ 104 ]. There was no association between burnout and hospital-acquired pressure ulcers [ 20 ].

Considering patient experience, Vahey et al. concluded that higher Emotional Exhaustion and low Personal Accomplishment levels were associated with patient dissatisfaction [ 93 ], and Van Bogaert et al. found that Emotional Exhaustion was related to patient and family verbal abuse, and Depersonalisation was related to both patient and family verbal abuse and patient and family complaints [ 104 ].

In summary, evidence deriving from 17 studies points to a negative effect of burnout on quality of care, patient safety, adverse events, error reporting, medication error, infections, patient falls, patient dissatisfaction, and family complaints, but not on pressure ulcers.

Individual characteristics

In total, 16 studies, which had examined work characteristics related to burnout, also considered the relationship between characteristics of the individual and burnout. Relationships were tested on demographic variables, including gender, age, and family status; on personality aspects; on work-life interference; and on professional attributes including length of experience and educational level. Because our focus on burnout is as a job-related phenomenon, we have not reported results of these studies into detail, but overall evidence on demographic and personality factors was inconclusive, and having family issues and high work-life interference was associated with different burnout dimensions. Being younger and not having a bachelor’s degree were found to be associated with a higher incidence of burnout.

This review aimed to identify research that had examined theorised relationships with burnout, in order to determine what is known (and not known) about the factors associated with burnout in nursing and to determine the extent to which studies have been underpinned by, and/or have supported or refuted, theories of burnout. We found that the associations hypothesised by Maslach’s theory between mismatches in areas of worklife and burnout were generally supported.

Research consistently found that adverse job characteristics—high workload, low staffing levels, long shifts, low control, low schedule flexibility, time pressure, high job and psychological demands, low task variety, role conflict, low autonomy, negative nurse-physician relationship, poor supervisor/leader support, poor leadership, negative team relationship, and job insecurity—were associated with burnout in nursing.

However few studies used all three MBI subscales in the way intended, and nine used different approaches to measuring burnout.

The field has been dominated by cross-sectional studies that seek to identify associations with one or two factors, rarely going beyond establishing correlation. Most studies were limited by their cross-sectional nature, the use of different or incorrectly applied burnout measures, the use of common methods (i.e. survey to capture both burnout and correlates), and omitted variables in the models. The 91 studies reviewed, while highlighting the importance of burnout as a feature affecting nurses and patient care, have generally lacked a theoretical approach, or identified mechanisms to test and develop a theory on the causes and consequences of burnout, but were limited in their testing of likely mechanisms due to cross-sectional and observational designs.

For example, 19 studies showed relationships between burnout and job satisfaction, missed care, and mental health. But while some studies treated these as predictors of burnout, others handled as outcomes of burnout. This highlights a further issue that characterises the burnout literature in nursing: the simultaneity bias, due to the cross-sectional nature of the evidence. The inability to establish a temporal link means limits the inference of causality [ 115 ]. Thus, a factor such as ‘missed care’ could lead to a growing sense of compromise and ‘crushed ideals’ in nurses [ 116 ], which causes burnout. Equally, it could be that job performance of nurses experiencing burnout is reduced, leading to increased levels of ‘missed care’. Both are plausible in relation to Maslach’s original theory of burnout, but research is insufficient to determine which is most likely, and thereby develop the theory.

To help address this, three areas of development within research are proposed. Future research adopting longitudinal designs that follow individuals over time would improve the potential to understand the direction of the relationships observed. Research using Maslach’s theory should use and report all three MBI dimensions; where only the Emotional Exhaustion subscale is used, this should be explicit and it should not be treated as being synonymous to burnout. Finally, to move our theoretical understanding of burnout forward, research needs to prioritise the use of empirical data on employee behaviours (such as absenteeism, turnover) rather than self-report intentions or predictions.

Addressing these gaps would provide better evidence of the nature of burnout in nursing, what causes it and its potential consequences, helping to develop evidence-based solutions and motivate work-place change. With better insight, health care organisations can set about reducing the negative consequences of having patient care provided by staff whose work has led them to become emotionally exhausted, detached, and less able to do the job, that is, burnout.

Limitations

Our theoretical review of the literature aimed to summarise information from a large quantity of studies; this meant that we had to report studies without describing their context in the text and also without providing estimates (i.e. ORs and 95% CIs). In appraising studies, we did not apply a formal quality appraisal instrument, although we noted key omissions of important details. However, the results of the review serve to illustrate the variety of factors that may influence/result from burnout and demonstrate where information is missing. We did not consider personality and other individual variables when extracting data from studies. However, Maslach and Leiter recently reiterated that although some connections have been made between burnout and personality characteristics, the evidence firmly points towards work characteristics as the primary drivers of burnout [ 8 ].

While we used a reproducible search strategy searching MEDLINE, CINAHL, and PsycINFO, it is possible that there are studies indexed elsewhere and we did not identify them, and we did not include grey literature. It seems unlikely that these exist in sufficient quantity to substantively change our conclusions.

Patterns identified across 91 studies consistently show that adverse job characteristics are associated with burnout in nursing. The potential consequences for staff and patients are severe. Maslach’s theory offers a plausible mechanism to explain the associations observed. However incomplete measurement of burnout and limited research on some relationships means that the causes and consequences of burnout cannot be reliably identified and distinguished, which makes it difficult to use the evidence to design interventions to reduce burnout.

Availability of data and materials

Not applicable

Abbreviations

• Maslach Burnout Inventory

Copenhagen Burnout Inventory

Professional Quality of Life Measure

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Studies’ settings, sample sizes, burnout and correlates measurement, and appraisal of quality.

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Dall’Ora, C., Ball, J., Reinius, M. et al. Burnout in nursing: a theoretical review. Hum Resour Health 18 , 41 (2020). https://doi.org/10.1186/s12960-020-00469-9

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Nurses’ burnout and quality of life: A systematic review and critical analysis of measures used

Haitham khatatbeh.

1 Doctoral School of Health Sciences, Faculty of Health Sciences, University of Pécs, Pécs Hungary

Annamária Pakai

2 Institute of Nursing Sciences, Basic Health Sciences and Health Visiting, Faculty of Health Sciences, University of Pécs, Pécs Hungary

Tariq Al‐Dwaikat

3 Jordan University of Science and Technology, Ar‐Ramtha Jordanz

David Onchonga

Viktória prémusz, andrás oláh, associated data.

The data that support the results of this research are available from the corresponding author upon a reasonable request.

Nurses’ burnout might affect their quality of life, productivity and nursing care services.

The aim of this systematic review was to systemically review the relationship between nurses’ burnout and quality of life and to introduce practical recommendations to reduce nurses’ BO and improve their QOL.

In April 2021, MeSH terms (("Nurses"[Mesh]) AND "Burnout, Professional"[Mesh]) AND "Quality of Life"[Majr] were used to search five electronic databases: CINAHL, PubMed, Medline, Psychology and Behavioral Sciences Collection and Google Scholar.

The search produced 21 studies exploring nurses’ burnout and their quality of life within the last ten years (2009–2021). Most of these studies found significant relationships between the burnout dimension(s) and quality of life dimension(s) among the nurses.

Nurses have moderate to high levels of burnout and were negatively associated with poor quality of life. Interventional programs are needed to decrease nurses’ burnout and improve their quality of life.

1. INTRODUCTION

Burnout (BO) is attracting considerable attention due to its serious consequences, whether on staff productivity, client satisfaction or institutions’ reputation (Manzano‐García & Ayala,  2017 ; Maslach et al.,  1986 ). BO also has several physical effects, such as musculoskeletal diseases, mental effects such as depression and job‐related effects such as absenteeism (Salvagioni et al.,  2017 ).

It is well known that the nurses are among those staff dealing and working with many people, including patients, families and other co‐workers, which make them vulnerable to BO. (Chou et al.,  2014 ; Gómez‐Urquiza et al.,  2017 ; Manzano‐García & Ayala,  2017 ; Messias et al.,  2019 ). The possible reasons that make nurses particularly vulnerable to BO might include the extra time needed to follow‐up patients and families’ requests, lack of respect, teamwork and collaboration between nurses and other healthcare professionals, and nurses’ poor coping skills to deal with these stressors.

In addition to other factors such as poor work environment, high workload and low salaries, BO might affect nurses’ Quality of Life (QOL) (Naz et al.,  2016 ). Furthermore, nurses’ BO might also increase absenteeism and affect their QOL (Aytekin et al.,  2013 ; Wu et al.,  2011 ). Nurses’ absenteeism and low QOL might ultimately affect the patient safety and quality of nursing care provided to patients (Kelleci et al.,  2011 ). So, BO and its consequences might affect nurses’ QOL (Aytekin et al.,  2013 ; Azari & Rasouyar,  2016 ; Hatamipour et al.,  2017 ).

Nurses’ QOL is also getting more attention because they are prone to physical, psychological and social stressors (Serinkan & Kaymakçi, 2013 ). Many researchers have systematically reviewed BO in paediatric, gynaecology, emergency and primary nursing (De La Fuente‐Solana et al.,  2019 ; Gómez‐Urquiza et al.,  2017 ; Monsalve Reyes et al.,  2018 ; Pradas‐Hernández et al.,  2018 ), and another researcher has systematically reviewed BO associations with social support (Velando‐Soriano et al.,  2020 ). However, none of these systematic reviews has examined the relationship between nurses’ BO and their QOL.

1.1. Definitions of BO and QOL

According to Maslach et al., ( 1986 ), BO is a syndrome of combined emotional exhaustion, depersonalization and reduced personal accomplishment. Emotional exhaustion entails a psychological feeling of being unable to give because of depleted emotional resources (Maslach et al.,  1986 ). In depersonalization, the staff becomes unfeeling or hard‐hearted with clients (Maslach et al.,  1986 ). The reduced personal accomplishment is to be dissatisfied about own job accomplishments (Maslach et al.,  1986 ).

Similarly, World Health Organization (WHO) described BO as a syndrome of exhaustion, feeling of negativism and decreased personal efficacy due to long‐lasting work stress that was not effectively treated (World Health Organization,  2018 ). On the other hand, Kristensen et al., ( 2005 ) described BO’s essence as fatigue and exhaustion, which attribute to different domains in the person's life. Also, the Conversation of Resources theory was used in defining BO as a feeling of emotional exhaustion, physical fatigue and cognitive weariness (Schilling et al.,  2019 ; Shirom,  2004 ).

BO definitions were different from each other; each definition included a set of BO components. For example, the definition of Maslach et al., ( 1986 ) had emotional exhaustion, depersonalization and reduced personal accomplishment. In the Shirom–Melamed definition, the components were different: emotional exhaustion, physical fatigue and cognitive weariness (Schilling et al.,  2019 ; Shirom,  2004 ). On the other hand, the WHO definition included exhaustion, negativism and decreased personal efficacy (World Health Organization,  2018 ).

QOL is a general and relatively new expression that replaced old words like happiness and well‐being (Serinkan & Kaymakçi, 2013 ). QOL is defined by WHO as a humans’ impression about their situation in life within their environment regarding their aims, values, prospects and worries (WHO,  1997 ). Professional QOL (ProQOL) is a subtype of the QOL for helping others overcome their suffering and trauma (Stamm,  2010 ).

The WHO definition was very comprehensive and related to general health (WHO,  1997 ). On the other hand, the definition of professional QOL is related to work‐related QOL. However, the definition of professional QOL is very comprehensive regarding the work environment (Stamm,  2010 ).

1.2. Measures of BO and QOL

The Maslach Burnout Inventory (MBI) is the most widely used instrument to measure the individual's experience of BO (Kristensen et al.,  2005 ). It measures the three aspects of BO syndrome, namely emotional exhaustion, depersonalization and personal accomplishment (Kristensen et al.,  2005 ). The MBI is composed of 16–22 Likert‐type items depending on the used version, general, human services, students, medical personnel or educators’ version (Maslach et al.,  1986 ).

The Copenhagen Burnout Inventory (CBI) is another valid instrument to measure BO (Kristensen et al.,  2005 ). It was developed as a part of the Danish Project on BO, Motivation and Job Satisfaction (Borritz et al.,  2006 ; Kristensen et al.,  2005 ). The CBI is composed of 19 Likert‐type items to measure three dimensions of BO: personal BO, work‐related BO and client‐related BO among professionals who work with clients (Kristensen et al.,  2005 ).

The Oldenburg Burnout Inventory (OLBI) is another valid instrument used to measure BO among the various professionals using 16 Likert‐type items (Janko & Smeds,  2019 ; Reis et al.,  2015 ). Like MBI, the OLBI measures BO as a syndrome but encompasses only two dimensions: exhaustion and disengagement from work (Reis et al.,  2015 ).

The Shirom–Melamed Burnout Questionnaire (SMBQ) is composed of twelve items to measure BO’s three dimensions, namely emotional exhaustion, physical fatigue and cognitive worn‐out, as‐built according to Conversation of Resources theory (Schilling et al.,  2019 ).

Although MBI is considered the golden instrument in measuring BO, Kristensen et al., ( 2005 ) criticized the MBI because it measures the three dimensions of BO syndrome independently. This conflicts with Maslach's definition that the three dimensions of BO co‐occur (Kristensen et al.,  2005 ). On the other hand, it is unnecessary to use the three CBI subscales to measure the BO (Kristensen et al.,  2005 ). Depending on the target population, only one or two subscales of the CBI can be used (Kristensen et al.,  2005 ). The CBI was translated into other languages and found to have acceptable validity and reliability (Berat et al.,  2016 ; Chin et al.,  2018 ; Fiorilli et al.,  2015 ; Kristensen et al.,  2005 ; Mahmoudi et al.,  2017 ; Yeh et al.,  2007 ).

WHO developed one of the most important tools to measure QOL (WHOQOL). WHOQOL comprises 100 Likert‐type items covering six main areas: physical health, psychological health, social relationships, and environment, the level of independence and spirituality (WHOQOL‐Group,  1998 ). The short version of WHOQOL is WHOQOL‐BREF, which comprises 26 Likert‐type items that cover four main areas: physical health, psychological health, social relationships and environment (WHOQOL‐Group,  1998 ).

The Short‐Form Health Survey (SF‐36) is another tool to assess QOL. SF‐36 is composed of 36‐items measuring different health domains: physical and psychological (Ware & Sherbourne,  1992 ). The physical health domains in SF‐36 are physical working, physical role, pain and overall health (Ware & Sherbourne,  1992 ). On the other hand, the mental health domains in SF‐36 are vitality, social functioning, emotional role and psychological health (Ware & Sherbourne,  1992 ). SF‐36 was further shortened into SF‐12, measuring only two dimensions physical and mental component (Ware et al.,  1994 ).

ProQOL tool is composed of 30 Likert‐type items to assess QOL (Stamm,  2010 ). ProQOL measures both positive and negative consequences of dealing with humans suffering from traumatic situations (Stamm,  2010 ). ProQOL measures Compassion Satisfaction and Compassion Fatigue , which is composed of BO and Secondary Traumatic Stress (Stamm,  2010 ). Compassion Satisfaction is to like and be happy doing your job tasks effectively (Stamm,  2010 ). As a Compassion Fatigue sub‐domain, BO was described as a feeling of hopelessness and problems dealing with work or doing your tasks well (Stamm,  2010 ). Secondary Traumatic Stress is related to job nature and interaction with persons complaining of severe stressful situations (Stamm,  2010 ).

Although WHOQOL, SF‐36 and ProQOL are the most widely used tools to measure QOL, some researchers used other validated tools. For instance, the Work‐Related Quality of Life Scale (WR‐QOLS) is another validated questionnaire measuring QOL. WR‐QOLS assesses six dimensions of QOL: general well‐being, home‐work interface, job and career satisfaction, control at work, working conditions and stress at work (Casida et al.,  2019 ; Wang et al.,  2019 ). WR‐QOLS comprises 23 items of 5‐point Likert‐type scale ranging from strongly disagree to strongly agree (Casida et al.,  2019 ; Wang et al.,  2019 ). Additionally, Work‐Life Quality (QWL) encompasses 35‐items measuring eight dimensions of work‐related QOL (Permarupan et al.,  2020 ). Last, another QOL scale comprises 28‐items assessing four dimensions: working life, social life, BO and satisfaction (Çelmeçe & Menekay,  2020 ).

1.3. Purpose

The purpose of this systematic review is to examine the relationship between nurses’ BO and their QOL based on the existing research. The objectives of this review include describing nurses’ BO and how it was measured, describing nurses’ QOL and how it was measured, assessing the relationship between nurses’ BO and their QOL, and introducing practical recommendations to reduce nurses’ BO and improve their QOL.

PRISMA guidelines were followed to perform this systematic review (Liberati et al.,  2009 ). PRISMA includes evidence‐based items for reporting systematic reviews and meta‐analyses (Liberati et al.,  2009 ). PRISMA illustrates how researchers can ensure the objective and complete reporting of systematic reviews and meta‐analyses (Liberati et al.,  2009 ).

2.1. Search strategy

Five electronic databases, CINAHL, PubMed, Medline, Psychology and Behavioral Sciences Collection and Google Scholar, were selected for this systematic review. These databases were selected because they include bibliographic information for articles covering our research topic: nursing and psychology. Two members of the review team searched the chosen databases in April 2021. First, the terms “nurses AND burnout AND quality of life” were used to find the MeSH terms on PubMed. The command line used in searching PubMed was (("Nurses"[Mesh]) AND "Burnout, Professional"[Mesh]) AND "Quality of Life"[Majr]. Searching restrictions included English language, scholarly journals and last twelve years publications (2009–2021).

2.2. Study selection

To ensure the reliability of the study selection process, it was independently done by two members of the review team. The selection process started by screening titles and abstracts, followed by full reading for the initially selected studies. The chosen studies meeting the inclusion criteria were finally assessed for possible methodological bias using Ciaponni's critical reading checklist. To resolve any disagreement, a third member of the review team was consulted. See PRISMA flow diagram, Figure  1 .

PRISMA flow diagram

2.3. Quality assessment

According to similar systematic reviews (De La Fuente‐Solana et al.,  2019 ; Gómez‐Urquiza et al.,  2017 ; Monsalve Reyes et al.,  2018 ) and because all of the included articles were cross‐sectional studies, items related to internal validity (2, 3, 4,5, 6, 11, 12, 13, 14‐b, 15, 16, 17 and 18) from Ciaponni's critical reading checklist (Ciapponi,  2010 ) were used to assess the quality of the articles. Each article was assessed for methodological quality, and no one was excluded for a methodological bias. The results of critical reading are shown in Additional file 1.

2.4. Inclusion criteria

According to the predetermined inclusion criteria for this systematic review, we included only the cross‐sectional and peer‐reviewed studies measuring both nurses’ BO and QOL using separate validated measures. The exclusion criteria were as follows: (a) studies that didn't measure both BO and QOL, (b) studies that did not specify proportion or number of the nurses, (c) psychometric studies, (d) qualitative studies as they do not provide numerical measurements BO and QOL, (e) interventional studies, (f) preprints and (g) studies with other languages.

2.5. Data extraction

After applying the predetermined inclusion and exclusion criteria for the search performed in April 2021, 21 studies were included in this systematic review (Figure  1 ). Most of the studies ( n  = 12) included were from China, Turkey, Iran, Greece or Pakistan (Aytekin et al.,  2013 ; Azari & Rasouyar,  2016 ; Çelmeçe & Menekay,  2020 ; Erkorkmaz et al.,  2018 ; Fradelos et al.,  2014 ; Hatamipour et al.,  2017 ; Kelleci et al.,  2011 ; Naz et al.,  2016 ; Paniora et al.,  2017 ; Wang et al.,  2019 ; Wu et al.,  2011 ; Zeng et al.,  2020 ). The rest of the studies was from Brazil (Ribeiro et al.,  2021 ), Egypt (Abdel‐Aziz & Adam,  2020 ), Korea (Kim et al.,  2019 ), India (Abraham & D’silva,  2013 ), Jordan (Khatatbeh et al.,  2020 ), Malaysia (Permarupan et al.,  2020 ), Poland (Kupcewicz & Jóźwik,  2020 ), Saudi Arabia (Alotni & Elgazzar,  2020 ) and the USA (Casida et al.,  2019 ). All of the studies included in this systematic review utilized a cross‐sectional design, and most of them ( n  = 11) were published between 2019 and 2021, Table  1 .

Results of searching electronic databases

For the 21 included articles, the following information was independently extracted by two researchers: (a) the first author's surname, (b) year of publication, (c) research design, (d) sampling method and size, (e) BO instrument, (f) QOL instrument and (g) results. If there was a disagreement about a certain article, a third member of the research team was consulted until an agreement was reached.

3.1. Summary of the reviewed studies

The total number of nurses in the 21 included studies was 9,859. Regarding the gender of participants, three studies surveyed only female nurses (Azari & Rasouyar,  2016 ; Naz et al.,  2016 ; Wu et al.,  2011 ). Concerning the profession of participants, one of these studies compared female nurses to female doctors (Wu et al.,  2011 ), another study compared nurses to nurse educators (Abraham & D’silva,  2013 ), and one study studied different healthcare providers, including nurses (Çelmeçe & Menekay,  2020 ). Regarding the working area of the participants, four studies surveyed mental nurses (Abdel‐Aziz & Adam,  2020 ; Fradelos et al.,  2014 ; Paniora et al.,  2017 ; Zeng et al.,  2020 ), one study surveyed only Neonatal Intensive Care Unit (NICU) nurses (Aytekin et al.,  2013 ), one study surveyed paediatric nurses (Khatatbeh et al.,  2020 ), one study surveyed emergency nurses (Ribeiro et al.,  2021 ) and one study included nurses working at critical care units (Alotni & Elgazzar,  2020 ) (Table  2 ). Also, one study surveyed nurses caring for COVID‐19 patients (Çelmeçe & Menekay,  2020 ).

Summary of the included studies

3.2. Definition of BO and QOL in the reviewed studies

The definition of Maslach et al., ( 1986 ) was explicitly adopted by five studies (Aytekin et al.,  2013 ; Erkorkmaz et al.,  2018 ; Hatamipour et al.,  2017 ; Wu et al.,  2011 ; Zeng et al.,  2020 ). Six studies implicitly adopted Maslach & Jackson's ( 1981 ) definition of BO because they used the MBI without including a BO definition (Abraham & D’silva,  2013 ; Azari & Rasouyar,  2016 ; Çelmeçe & Menekay,  2020 ; Kim et al.,  2019 ; Permarupan et al.,  2020 ; Ribeiro et al.,  2021 ). Two studies adopted the definition of Freudenberger ( 1974 ), which described BO as bodily and behavioural signs and symptoms caused by physical and psychological tiredness (Paniora et al.,  2017 ; Wang et al.,  2019 ). One study defined BO as a chronic mental syndrome that results from social stressors (Abdel‐Aziz & Adam,  2020 ).

QOL was described in three studies as the bodily, psychological and social health interacting with the environment (Aytekin et al.,  2013 ; Erkorkmaz et al.,  2018 ; Paniora et al.,  2017 ). Similarly, other studies ( n  = 3) described QOL as a vital feature of human well‐being established in a bodily, public and community frame (Fradelos et al.,  2014 ; Naz et al.,  2016 ; Wu et al.,  2011 ). Another study by Azari and Rasouyar ( 2016 ) described QOL as a multidimensional and multifaceted concept characterized by objective and subjective features and helps finally to assess human well‐being (Azari & Rasouyar,  2016 ). On the other hand, six studies have adopted the definition of the WHO (Abraham & D’silva,  2013 ; Alotni & Elgazzar,  2020 ; Hatamipour et al.,  2017 ; Kelleci et al.,  2011 ; Kupcewicz & Jóźwik,  2020 ; Ribeiro et al.,  2021 ). Last, some studies examined work‐related or professional QOL, not general QOL (Abdel‐Aziz & Adam,  2020 ; Casida et al.,  2019 ; Erkorkmaz et al.,  2018 ; Kim et al.,  2019 ).

3.3. Measures of BO and QOL used in the reviewed studies

Out of the 21 studies included in this review, 17 studies measured BO using a version of MBI (Abdel‐Aziz & Adam,  2020 ; Alotni & Elgazzar,  2020 ; Aytekin et al.,  2013 ; Azari & Rasouyar,  2016 ; Çelmeçe & Menekay,  2020 ; Erkorkmaz et al.,  2018 ; Fradelos et al.,  2014 ; Hatamipour et al.,  2017 ; Kelleci et al.,  2011 ; Kim et al.,  2019 ; Naz et al.,  2016 ; Paniora et al.,  2017 ; Permarupan et al.,  2020 ; Ribeiro et al.,  2021 ; Wang et al.,  2019 ; Wu et al.,  2011 ; Zeng et al.,  2020 ). Three studies used the CBI (Casida et al.,  2019 ; Khatatbeh et al.,  2020 ; Kupcewicz & Jóźwik,  2020 ), and one study used Shirom–Melamed BO inventory (Abraham & D’silva,  2013 ). To measure nurses’ QOL, the included 21 studies have used either WHOQOL‐BREF ( n  = 8), SF‐36 or SF‐12 ( n  = 6), ProQOL ( n  = 3) or another tool ( n  = 4). Most of the included studies found moderate to high levels of BO. However, psychiatric nurses showed low levels of BO in one study (Paniora et al.,  2017 ).

3.4. The relationship between BO and QOL in the reviewed studies

The majority of the studies ( n  = 16) found a negative correlation between nurses’ burnout and their QOL or professional QOL (Abdel‐Aziz & Adam,  2020 ; Abraham & D’silva,  2013 ; Alotni & Elgazzar,  2020 ; Aytekin et al.,  2013 ; Casida et al.,  2019 ; Erkorkmaz et al.,  2018 ; Fradelos et al.,  2014 ; Hatamipour et al.,  2017 ; Kelleci et al.,  2011 ; Khatatbeh et al.,  2020 ; Kim et al.,  2019 ; Kupcewicz & Jóźwik,  2020 ; Permarupan et al.,  2020 ; Ribeiro et al.,  2021 ; Wang et al.,  2019 ; Zeng et al.,  2020 ). For example, nurses’ QOL was negatively correlated with emotional exhaustion and depersonalization, and positively with personal accomplishment (Kelleci et al.,  2011 ). Similarly, the emotional exhaustion among NICU nurses was negatively associated with all QOL subscales; and depersonalization was negatively associated with physical, psychological health and social relationships subscales (Aytekin et al.,  2013 ). Two domains of QOL, psychological and social relationships, were negatively correlated with BO (Abraham & D’silva,  2013 ). Similarly, another study found that personal accomplishment affects nurses’ QOL (Erkorkmaz et al.,  2018 ). One study found a significant correlation between emotional exhaustion and QOL measured by SF‐36 (Azari & Rasouyar,  2016 ). An intermediate effect was found for emotional exhaustion on Compassion Fatigue, the subscale of ProQOL (Erkorkmaz et al.,  2018 ). Similar results were found between the depersonalization subscale and two subscales of ProQOL: BO and Compassion Fatigue (Erkorkmaz et al.,  2018 ). Another study found a strong negative correlation between both emotional exhaustion and depersonalization with nurses’ QOL (Fradelos et al.,  2014 ). Also, some studies ( n  = 4) found that professional or work‐related QOL was also negatively associated with nurses’ BO (Abdel‐Aziz & Adam,  2020 ; Casida et al.,  2019 ; Kim et al.,  2019 ; Wang et al.,  2019 ).

4. DISCUSSION

Assessment of nurses’ BO, their QOL, and the relationship between BO and QOL were the aims of this systematic review. The high levels of nurses’ BO in the reviewed articles were explained by the challenging work conditions and working environments such as changing shifts, low nurse‐to‐patient ratio, and poor teamwork and collaboration with other healthcare workers (Erkorkmaz et al.,  2018 ). However, the varying levels of BO across the included studies can be explained by the various working environments such as unit/ward, the different working shifts and the different working loads. For example, some studies studied only NICU, mental, critical or paediatric nurses; and some studies included only one or two hospitals in their studies. The NICU’s busy environment, the critical patients’ cases, ventilator sounds and cardiac monitor alarms might make the nurses more susceptible to BO than those in other units. Furthermore, the nurses who work on the night or alternate shifts and the associated sleep problems might have higher BO than other nurses who work on the day and regular shifts. For instance, the low BO levels found among psychiatric nurses in the study of Paniora et al., ( 2017 ) might not be generalizable to all nurses because of the low sample size. However, this finding is relatively consistent with a study that revealed low to moderate scores on MBI subscales (Kilfedder et al.,  2001 ). On the other hand, this result is different from a previous study that showed moderate to high scores on MBI subscales (Hamaideh,  2011 ).

Most of the included studies have explicitly concluded that nurses’ BO or its’ subscales negatively impacts their QOL or its’ subscales (Abraham & D’silva,  2013 ; Alotni & Elgazzar,  2020 ; Aytekin et al.,  2013 ; Fradelos et al.,  2014 ; Hatamipour et al.,  2017 ; Kelleci et al.,  2011 ; Khatatbeh et al.,  2020 ; Kupcewicz & Jóźwik,  2020 ; Ribeiro et al.,  2021 ; Zeng et al.,  2020 ). Similarly, some of the included studies found a negative association between professional or work‐related QOL and nurses’ BO (Abdel‐Aziz & Adam,  2020 ; Casida et al.,  2019 ; Erkorkmaz et al.,  2018 ; Kim et al.,  2019 ; Permarupan et al.,  2020 ; Wang et al.,  2019 ). Although some studies did not find a significant correlation between nurses’ BO and QOL, they found moderate to high levels of BO and relatively poor QOL (Kupcewicz & Jóźwik,  2020 ; Naz et al.,  2016 ; Paniora et al.,  2017 ; Permarupan et al.,  2020 ; Wu et al.,  2011 ).

In the study of Kelleci et al., ( 2011 ), the negative relationship between nurses’ BO and their QOL was explained by their low job satisfaction. In the study of Aytekin et al. ( 2013 ), the moderate levels of nurses’ BO impacting their QOL might be explained by NICU’s environment and high workload. The low personal accomplishment scores and their relationship with QOL, in the study of Erkorkmaz et al., ( 2018 ), were explained by the high occupational stress.

Due to their impact on nurses’ health and patient care, comprehensive interventional programs such as salary increment, decreasing the working hours and counselling sessions on stress management are needed to prevent nurses’ BO and improve their QOL. Moreover, social and manager supports are also essential to prevent nurses’ BO and improve their QOL (Hamaideh,  2011 ), improving the patient safety and quality of nursing care provided to their patients (Khatatbeh et al.,  2020 ). Furthermore, it is essential to control the reasons that initially make nurses susceptible to BO, such as high workload and low satisfaction (Van Bogaert et al.,  2013 ). Traditional and social media can be utilized in showing the bright sides of the nursing profession to enhance respect for nurses, improving the teamwork and collaboration between nurses and other healthcare professionals, and teaching nurses the necessary coping skills and strategies to deal with stressors.

Our systematic review suggests that nurses are complaining of moderate to high levels of BO. Also, the high levels of BO among nurses are negatively associated with low QOL. So, nurses’ BO needs to be controlled because it might affect their QOL and the quality of nursing care. Many possible measures that might decrease nurses’ BO and improve their QOL, such as manager support (Khatatbeh et al.,  2020 ), counselling sessions and monetary bonuses. Other targeted interventions might be helpful in addressing the sociodemographic factors such as gender, unit and shift that were found to be associated with higher levels of BO and/or lower QOL scores. For instance, female nurses who are married or having families to care for should get more off days, nurses working in critical care units should be assigned to fewer cases, and nurses who work on alternate shifts should get more off days or longer break times.

4.1. Limitations

A key problem with some of the studies included in this systematic review is the small sample sizes (Abdel‐Aziz & Adam,  2020 ; Abraham & D’silva,  2013 ; Alotni & Elgazzar,  2020 ; Aytekin et al.,  2013 ; Azari & Rasouyar,  2016 ; Casida et al.,  2019 ; Çelmeçe & Menekay,  2020 ; Erkorkmaz et al.,  2018 ; Fradelos et al.,  2014 ; Naz et al.,  2016 ; Paniora et al.,  2017 ; Ribeiro et al.,  2021 ). Moreover, three studies selected nurses from only one or two hospitals (Aytekin et al.,  2013 ; Erkorkmaz et al.,  2018 ; Wu et al.,  2011 ). Additionally, three studies (Alotni & Elgazzar,  2020 ; Aytekin et al.,  2013 ; Ribeiro et al.,  2021 ) have studied nurses working at critical care units, who have more stressful environment than other nurses. This systematic review might also be limited by including only those studies in English and excluding qualitative studies. Last, the different tools used in the included studies to measure BO and QOL might be another limitation. Future systematic reviews are encouraged to have meta‐analysis by including studies using the same measures. However, the studies included in this systematic review were peer‐reviewed, were done in different countries and continents, and included nurses working in different working areas.

5. CONCLUSION

This systematic review aimed to assess the relationship between nurses’ BO and QOL and analyse the measures used. The review results showed moderate to high levels of BO across the included studies, varying levels of QOL and negative relationships between BO and QOL. MBI remains the most widely used instrument in assessing nurses’ BO. Both WHOQOL‐BREF and SF‐36 are the most used tools in measuring nurses’ QOL.

CONFLICT OF INTEREST

The authors have no personal or financial concern that might lead to a conflict of interest regarding this research.

AUTHOR CONTRIBUTIONS

H.K., T.D., A.P. and A.O: Plan and design the systematic review. H.K. and T.D: Search and data extraction. H.K: Paper writing. T.D., V.P., F.A., A.P. and D.O: Paper review. All authors: Responsible for research report and approval of the manuscript submission.

ETHICAL APPROVAL

As this is a systematic review, no ethical approval was needed.

PATIENT CONSENT FORM

As this is a systematic review, no patients were involved in this study and no consents were needed.

ACKNOWLEDGEMENT

The researchers thank everyone who contributed to the current study.

Khatatbeh H, Pakai A, Al‐Dwaikat T, et al. Nurses’ burnout and quality of life: A systematic review and critical analysis of measures used . Nurs Open . 2022; 9 :1564–1574. 10.1002/nop2.936 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

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Research Article

The relationship between workload and burnout among nurses: The buffering role of personal, social and organisational resources

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Institute of Occupational, Social and Environmental Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany

Roles Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Software, Writing – review & editing

Roles Conceptualization, Funding acquisition, Investigation, Resources, Writing – review & editing

Roles Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing

Affiliation Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf, Hamburg, Germany

Roles Conceptualization, Investigation, Methodology, Resources, Supervision, Writing – review & editing

Affiliations Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf, Hamburg, Germany, Department for Occupational Medicine, Hazardous Substances and Health Science, Institution for Accident Insurance and Prevention in the Health and Welfare Services (BGW), Hamburg, Germany

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing

¶ ‡ These authors are joint senior authors on this work.

Affiliations Institute of Occupational, Social and Environmental Medicine, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany, Federal Institute for Occupational Safety and Health (BAuA), Berlin, Germany

Roles Supervision, Writing – review & editing

* E-mail: [email protected]

• Elisabeth Diehl, 
• Sandra Rieger, 
• Stephan Letzel, 
• Anja Schablon, 
• Albert Nienhaus, 
• Luis Carlos Escobar Pinzon, 
• Pavel Dietz

• Published: January 22, 2021
• https://doi.org/10.1371/journal.pone.0245798
• Peer Review
• Reader Comments

Workload in the nursing profession is high, which is associated with poor health. Thus, it is important to get a proper understanding of the working situation and to analyse factors which might be able to mitigate the negative effects of such a high workload. In Germany, many people with serious or life-threatening illnesses are treated in non-specialized palliative care settings such as nursing homes, hospitals and outpatient care. The purpose of the present study was to investigate the buffering role of resources on the relationship between workload and burnout among nurses. A nationwide cross-sectional survey was applied. The questionnaire included parts of the Copenhagen Psychosocial Questionnaire (COPSOQ) (scale ‘quantitative demands’ measuring workload, scale ‘burnout’, various scales to resources), the resilience questionnaire RS-13 and single self-developed questions. Bivariate and moderator analyses were performed. Palliative care aspects, such as the ‘extent of palliative care’, were incorporated to the analyses as covariates. 497 nurses participated. Nurses who reported ‘workplace commitment’, a ‘good working team’ and ‘recognition from supervisor’ conveyed a weaker association between ‘quantitative demands’ and ‘burnout’ than those who did not. On average, nurses spend 20% of their working time with palliative care. Spending more time than this was associated with ‘burnout’. The results of our study imply a buffering role of different resources on burnout. Additionally, the study reveals that the ‘extent of palliative care’ may have an impact on nurse burnout, and should be considered in future studies.

Citation: Diehl E, Rieger S, Letzel S, Schablon A, Nienhaus A, Escobar Pinzon LC, et al. (2021) The relationship between workload and burnout among nurses: The buffering role of personal, social and organisational resources. PLoS ONE 16(1): e0245798. https://doi.org/10.1371/journal.pone.0245798

Editor: Adrian Loerbroks, Universtiy of Düsseldorf, GERMANY

Received: July 30, 2020; Accepted: January 7, 2021; Published: January 22, 2021

Copyright: © 2021 Diehl et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: According to the Ethics Committee of the Medical Association of Rhineland-Palatinate (Study ID: 837.326.16 (10645)), the Institute of Occupational, Social and Environmental Medicine of the University Medical Center of the University Mainz is specified as data holding organization. The institution is not allowed to share the data publically in order to guarantee anonymity to the institutions that participated in the survey because some institution-specific information could be linked to specific institutions. The data set of the present study is stored on the institution server at the University Medical Centre of the University of Mainz and can be requested for scientific purposes via the institution office. This ensures that data will be accessible even if the authors of the present paper change affiliation. Postal address: University Medical Center of the University of Mainz, Institute of Occupational, Social and Environmental Medicine, Obere Zahlbacher Str. 67, D-55131 Mainz. Email address: [email protected] .

Funding: The research was funded by the BGW - Berufsgenossenschaft für Gesundheitsdienst und Wohlfahrtspflege (Institution for Statutory Accident Insurance and Prevention in Health and Welfare Services). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: The project was funded by the BGW - Berufsgenossenschaft für Gesundheitsdienst und Wohlfahrtspflege (Institution for Statutory Accident Insurance and Prevention in Health and Welfare Services). The BGW is responsible for the health concerns of the target group investigated in the present study, namely nurses. Prof. Dr. A. Nienhaus is head of the Department for Occupational Medicine, Hazardous Substances and Health Science of the BGW and co-author of this publication. All other authors declare to have no potential conflict of interest. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Introduction

Our society has to face the challenge of a growing number of older people [ 1 ], combined with an expected shortage of skilled workers, especially in nursing care [ 2 ]. At the same time, cancer patients, patients with non-oncological diseases, multimorbid patients [ 3 ] and patients suffering from dementia [ 4 ] are to benefit from palliative care. In Germany, palliative care is divided into specialised and general palliative care ( Table 1 ). The German Society for Palliative Medicine (DGP) estimated that 90% of dying people are in need of palliative care, but only 10% of them are in need of specialised palliative care, because of more complex needs, such as complex pain management [ 5 ]. The framework of specialised palliative care encompasses specialist outpatient palliative care, inpatient hospices and palliative care units in hospitals. In Germany, most nurses in specialised palliative care have an additional qualification [ 6 ]. Further, nurses in specialist palliative care in Germany have fewer patients to care for than nurses in other fields which results in more time for the patients [ 7 ]. Most people are treated within general palliative care in non-specialized palliative care settings, which is provided by primary care suppliers with fundamental knowledge of palliative care. These are GPs, specialists (e.g. oncologists) and, above all, staff in nursing homes, hospitals and outpatient care [ 8 ]. Nurses in general palliative care have basic skills in palliative care from their education. However, there is no data available on the extent of palliative care they provide, or information on an additional qualification in palliative care. Palliative care experts from around the world consider the education and training of all staff in the fundamentals of palliative care to be essential [ 9 ] and a study conducted in Italy revealed that professional competency of palliative care nurses was positively associated with job satisfaction [ 10 ]. Thus, it is possible that the extent of palliative care or an additional qualification in palliative care may have implications on the working situation and health status of nurses. In Germany, there are different studies which concentrate on people dying in hospitals or nursing homes and the associated burden on the institution’s staff [ 11 , 12 ], but studies considering palliative care aspects concentrate on specialised palliative care settings [ 6 , 13 , 14 ]. Because the working conditions of nurses in specialised and general palliative care are somewhat different, as stated above, this paper focuses on nurses working in general palliative care, in other words, in non-specialized palliative care settings.

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https://doi.org/10.1371/journal.pone.0245798.t001

Burnout is a large problem in social professions, especially in health care worldwide [ 19 ] and is consistently associated with nurses intention to leave their profession [ 20 ]. Burnout is a state of emotional, physical, and mental exhaustion caused by a long-term mismatch of the demands associated with the job and the resources of the worker [ 21 ]. One of the causes for the alarming increase in nursing burnout is their workload [ 22 , 23 ]. Workload can be either qualitative (pertaining to the type of skills and/or effort needed in order to perform work tasks) or quantitative (the amount of work to be done and the speed at which it has to be performed) [ 24 ].

Studies analysing burnout in nursing have recognised different coping strategies, self-efficacy, emotional intelligence factors, social support [ 25 , 26 ], the meaning of work and role clarity [ 27 ] as protective factors. Studies conducted in the palliative care sector identified empathy [ 28 ], attitudes toward death, secure attachment styles, and meaning and purpose in life as protective factors [ 29 ]. Individual factors such as spirituality and hobbies [ 30 ], self-care [ 31 ], coping strategies for facing the death of a patient [ 32 ], physical activity [ 33 ] and social resources, like social support [ 33 , 34 ], the team [ 6 , 13 ] and time for patients [ 32 ] were identified, as effectively protecting against burnout. These studies used qualitative or descriptive methods or correlation analyses in order to investigate the relationship between variables. In contrast to this statistical approach, fewer studies examined the buffering/moderating role of resources on the relationship between workload and burnout in nursing. A moderator variable affects the direction and/or the strength of the relationship between two other variables [ 35 ]. A previous study has showed resilience as being a moderator for emotional exhaustion on health [ 36 ], and other studies revealed professional commitment or social support moderating job demands on emotional exhaustion [ 37 , 38 ]. Furthermore, work engagement and emotional intelligence was recognised as a moderator in the work demand and burnout relationship [ 39 , 40 ].

We have analysed the working situation of nurses using the Rudow Stress-Strain-Resources model [ 41 ]. According to this model, the same stressor can lead to different strains in different people depending on available resources. These resources can be either individual, social or organisational. Individual resources are those resources which are owned by an individual. This includes for example personal capacities such as positive thinking as well as personal qualifications. Social resources consist of the relationships an individual has, this includes for example relationships at work as well as in his private life. Organisational resources refer to the concrete design of the workplace and work organisation. For example, nurses reporting a good working team may experience workload as less threatening and disruptive because a good working team gives them a feeling of security, stability and belonging. According to Rudow, individual, social or organisational resources can buffer/moderate the negative effects of job demands (stressors) on, for example, burnout (strain).

Nurses’ health may have an effect on the quality of the services offered by the health care system [ 42 ], therefore, it is of great interest to do everything possible to preserve their health. This may be achieved by reducing the workload and by strengthening the available resources. However, to the best of our knowledge, we are not aware of any study which considers palliative care aspects within general palliative care in Germany. Therefore, the aim of the study was to investigate the buffering role of resources on the relationship between workload (‘quantitative demands’) and burnout among nurses. Palliative care aspects, such as information on the extent of palliative care were incorporated to the analyses as covariates.

Study design and participants

An exploratory cross-sectional study was conducted in 2017. In Germany, there is no national register for nurses. Data for this study were collected from a stratified 10% random sample of a database with outpatient facilities, hospitals and nursing homes in Germany from the Institution for Statutory Accident Insurance and Prevention in Health and Welfare Services in Germany. This institution is part of the German social security system. It is the statutory accident insurer for nonstate institutions in the health and welfare services in Germany and thus responsible for the health concerns of the target group investigated in the present study, namely nurses. Due to data protection rules, this institution was also responsible for the first contact with the health facilities. 126 of 3,278 (3.8%) health facilities agreed to participate in the survey. They informed the study team about how many nurses worked in their institution, and whether the nurses would prefer to answer a paper-and-pencil questionnaire (with a pre-franked envelope) or an online survey (with an access code ). 2,982 questionnaires/access codes were sent out to the participating health facilities (656 to outpatient care, 160 to hospitals and 2,166 to nursing homes), where they were distributed to the nurses ( S1 Table ). Participation was voluntary and anonymous. Informed consent was obtained written at the beginning of the questionnaire. Approval to perform the study was obtained by the ethics committee of the State Chamber of Medicine in Rhineland-Palatinate (Clearance number 837.326.16 (10645)).

Questionnaire

The questionnaire contained questions regarding i) nurse’s sociodemographic information and information on current profession as well as ii) palliative care aspects. Furthermore, iii) parts of the German version of the Copenhagen Psychosocial Questionnaire (COPSOQ), iv) a resilience questionnaire [RS-13] and v) single questions relating to resources were added.

i) Sociodemographic information and information on current profession.

The nurse’s sociodemographic information and information on current profession included the variables ‘age’, ‘gender’, ‘marital status’, ‘education’, ‘professional qualification’, ‘working area’, ‘professional experience’ and ‘extent of employment’.

ii) Palliative care aspects.

Palliative care aspects included self-developed questions on ‘additional qualification in palliative care’, the ‘number of patients’ deaths within the last month (that the nurses cared for personally)’ and the ‘extent of palliative care’. The latter was evaluated by asking: how much of your working time (as a percentage) do you spend with care of palliative patients? The first two items were already used in the pilot study. The pilot study consisted of a qualitative part, where interviews with experts in general and specialised palliative care were performed [ 43 ]. These interviews were used to develop a standardized questionnaire which was used for a cross-sectional pilot survey [ 6 , 44 ].

iii) Copenhagen Psychosocial Questionnaire (COPSOQ).

The questionnaire included parts of the German standard version of the Copenhagen Psychosocial Questionnaire (COPSOQ) [ 45 ]. The COPSOQ is a valid and reliable questionnaire for the assessment of psychosocial work environmental factors and health in the workplace [ 46 , 47 ]. The scales selected were ‘quantitative demands’ (four items, for example: “Do you have to work very fast?”) measuring workload, ‘burnout’ (six items, for example: “How often do you feel emotionally exhausted?”), ‘meaning of work’ (three items, for example: “Do you feel that the work you do is important?”) and ‘workplace commitment’ (four items, for example: “Do you enjoy telling others about your place of work?”).

iv) Resilience questionnaire RS-13.

The RS-13 questionnaire is the short German version of the RS-25 questionnaire developed by Wagnild & Young [ 48 ]. The questionnaire postulates a two-dimensional structure of resilience formed by the factors “personal competence” and “acceptance of self and life”. The RS-13 questionnaire measures resilience with 13 items on a 7-point scale (1 = I do not agree, 7 = I totally agree with different statements) and has been validated in representative samples [ 49 , 50 ]. The results of the questionnaire were grouped into persons with low, moderate or high resilience.

v) Questions on resources.

Single questions on personal, social and organizational resources assessed the nurses’ views of these resources in being helpful in dealing with the demands of their work. Further, single questions collected the agreement to different statements such as ‘Do you receive recognition for your work from the supervisor? ’ (see Table 4 ). These resources were frequently reported in the pilot study by nurses in specialised palliative care [ 6 ].

Data preparation and analysis

The data from the paper-and-pencil and online questionnaires were merged, and data cleaning was done (e.g. questionnaires without specification to nursing homes, hospitals or outpatient care were excluded). The scales selected from the COPSOQ were prepared according to the COPSOQ guidelines. In general, COPSOQ items have a 5-point Likert format, which are then transformed into a 0 to 100 scale. The scale score is calculated as the mean of the items for each scale, if at least half of the single items had valid answers. Nurses who answered less than half of the items in a scale were recorded as missing. If at least half of the items were answered, the scale value was calculated as the average of the items answered [ 46 ]. High values for the scales ‘quantitative demands‘ and ‘burnout‘ were considered negative, while high values for the scales ‘meaning of work’ and ‘workplace commitment’ were considered positive. The proportion of missing values for single scale items was between 0.5% and 2.7%. Cronbach’s Alpha was used to assess the internal consistency of the scales. A Cronbach’s Alpha > 0.7 was regarded as acceptable [ 35 ]. The score of the RS-13 questionnaire ranges from 13 to 91. The answers were grouped according to the specifications in groups with low resilience (score 13–66), moderate resilience (67–72) and high resilience (73–91) [ 49 ]. The categorical resource variables were dichotomised (example: not helpful/little helpful vs. quite helpful/very helpful).

The study was conceptualised as an exploratory study. Consequently, no prior hypotheses were formulated, so the p-values merely enable the recognition of any statistically noteworthy findings [ 51 ]. Descriptive statistics (absolute and relative frequency, M = mean, SD = standard deviation) were used to depict the data. Bivariate analyses (Pearson correlation, t-tests, analysis of variance) were performed to infer important variables for the regression-based moderation analysis. Variables which did not fulfil all the conditions for linear regression analysis were recoded as categorical variables [ 35 ]. The variable ‘extent of palliative care’ was categorised as ‘≤ 20 percent of working time’ vs. ‘> 20 percent of working time’ due to the median of the variable (median = 20).

The first step with regard to the moderation analysis was to determine the resource variables. Therefore all resource variables that reached a p-value < 0.05 in the bivariate analysis with the scale ‘burnout’ were further analysed (scale ‘meaning of work’, scale ‘workplace commitment’, variables presented in Table 4 ). The moderator analysis was conducted using the PROCESS program developed by Andrew F. Hayes. First, scales were mean-centred to reduce possible scaling problems and multicollinearity. Secondly, for all significant resource variables the following analysis were done: the ‘quantitative demand’, one resource (one per model) and the interaction term between the ‘quantitative demand’ and the resource, as well as the covariates ‘age’, ‘gender’, ‘working area’, ‘extent of employment’, the ‘extent of palliative care’ and the ‘number of patient deaths within the last month’ were added to the moderator analysis, in order to control for confounding influence. If the interaction term between the ‘quantitative demand’ and the resource accounted for significantly more variance than without interaction term (change in R 2 denoted as ΔR 2 , p < 0.05), a moderator effect of the resource was present. The interaction of the variables (± 1 SD the mean or variable manifestation such as yes and no) was plotted.

All the statistical calculations were performed using the Statistical Package for Social Science (SPSS, version 23.5) and the PROCESS macro for SPSS (version 3.5 by Hayes) for the moderator analysis.

Of the 2,982 questionnaires/access codes sent out, 497 were eligible for the analysis. The response rate was 16.7% (response rate of outpatient care 14.6%, response rate of hospitals 18.1% and response rate of nursing homes 16.0%). Since only n = 29 nurses from hospitals participated, these were excluded from data analysis. After data cleaning , the final number of participants was n = 437.

Descriptive results

The basic characteristics of the study population are presented in Table 2 . The average age of the nurses was 42.8 years, and 388 (89.6%) were female. In total, 316 nurses answered the question how much working time they spend caring for palliative patients. Sixteen (5.1%) nurses reported spending no time caring for palliative patients, 124 (39.2%) nurses reported between 1% to 10%, 61 (19.30%) nurses reported between 11% to 20% and 115 (36.4%) nurses reported spending more than 20% of their working time for caring for palliative patients. Approximately one-third (n = 121, 27.7%) of the nurses in this study did not answer this question. One hundred seventeen (29.5%) nurses reported 4 or more patient deaths, 218 (54.9%) reported 1 to 3 patient deaths and 62 (15.6%) reported 0 patient deaths within the last month.

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Table 3 presents the mean values and standard deviations of the scales ‘quantitative demands’, ‘burnout’, and the resource scales ‘meaning of work’ and ‘workplace commitment’. All scales achieved a satisfactory level of internal consistency.

https://doi.org/10.1371/journal.pone.0245798.t003

Bivariate analyses

There was a strong positive correlation between the ‘quantitative demands’ and ‘burnout’ scales (r = 0.498, p ≤ 0.01), and a small negative correlation between ‘burnout’ and ‘meaning of work’ (r = -0.222, p ≤ 0.01) and ‘workplace commitment’ (r = -0.240, p ≤ 0.01). Regarding the basic and job-related characteristics of the sample shown in Table 2 , ‘burnout’ was significantly related to ‘extent of palliative care’ (≤ 20% of working time: n = 199, M = 46.06, SD = 20.28; > 20% of working time: n = 115, M = 53.80, SD = 20.24, t(312) = -3.261, p = 0.001). Furthermore, there was a significant effect regarding the ‘number of patient deaths during the last month’ (F (2, 393) = 5.197, p = 0.006). The mean of the burnout score was lower for nurses reporting no patient deaths within the last month than for nurses reporting four or more deaths (n = 62, M = 42.47, SD = 21.66 versus n = 116, M = 52.71, SD = 20.03). There was no association between ‘quantitative demands’ and an ‘additional qualification in palliative care’ (no qualification: n = 328, M = 55.77, SD = 21.10; additional qualification: n = 103, M = 54.39, SD = 20.44, p = 0.559).

The association between ‘burnout’ and the evaluated (categorical) resource variables is presented in Table 4 . Nurses mostly had a lower value on the ‘burnout’ scale when reporting various resources. Only the resources ‘family’, ‘religiosity/spirituality’, ‘gratitude of patients’, ‘recognition through patients/relatives’ and an ‘additional qualification in palliative care’ were not associated with ‘burnout’.

https://doi.org/10.1371/journal.pone.0245798.t004

Moderator analyses

In total, 16 moderation analyses were conducted. Table 5 presents the results of the moderation analyses where a significant moderation was found. For ‘workplace commitment’, there was a positive and significant association between ‘quantitative demands’ and ‘burnout’ (b = 0.47, SE = 0.051, p < 0.001). An increase of one value on the scale ‘quantitative demands’ increased the scale ‘burnout’ by 0.47. ‘Workplace commitment’ was negatively related to ‘burnout’, meaning that a higher degree of ‘workplace commitment’ was related to a lower level of ‘burnout’ (b = -0.11, SE = 0.048, p = 0.030). A model with the interaction term of ‘quantitative demands’ and the resource ‘workplace commitment’ accounted for significantly more variance in ‘burnout’ than a model without interaction term (ΔR 2 = 0.021, p = 0.004). The impact of ‘quantitative demands’ on ‘burnout’ was dependent on ‘workplace commitment’ (b = -0.01, SE = 0.002 p = 0.004). The variables explained 31.9% of the variance in ‘burnout’.

https://doi.org/10.1371/journal.pone.0245798.t005

Regarding the ‘good working team’ resource, the variables ‘quantitative demands’ and ‘burnout’ were positively and significantly associated (b = 0.76, SE = 0.154, p < 0.001), and the variables ‘good working team’ and ‘burnout’ were not associated (b = -3.15, SE = 3.52, p = 0.372). A model with the interaction term of ‘quantitative demands’ and the ‘good working team’ resource accounted for significantly more variance in ‘burnout’ than a model without interaction term (ΔR 2 = 0.011, p = 0.040). The ‘good working team’ resource moderated the impact of ‘quantitative demands’ on ‘burnout’ (b = -0.34, SE = 0.165, p = 0.004). The variables explained 29.7% of the variance in ‘burnout’.

The associations between ‘quantitative demands’ and ‘burnout’ (b = 0.63, SE = 0.085, p < 0.001), between ‘recognition supervisor’ and ‘burnout’ (b = -7.29, SE = 2.27, p = 0.001), and the interaction term of ‘quantitative demands’ and the resource ‘recognition supervisor’ (b = -0.34, SE = 0.108, p = 0.002) were significant. Again, a model with the interaction term accounted for significantly more variance in ‘burnout’ than a model without interaction term (ΔR 2 = 0.024, p = 0.002). ‘Recognition from supervisor’ influenced the impact of ‘quantitative demands’ on burnout for -0.34 on the 0 to 100 scale. The variables explained 33.7% of the variance in ‘burnout’.

Figs 1 – 3 demonstrates simple slopes of the interaction effects of ‘workplace commitment’ predicting ‘burnout’ at high, average and low levels ( Fig 1 ) respectively with and without the resource ‘good working team’ ( Fig 2 ) and ‘recognition from supervisor’ ( Fig 3 ). Higher ‘quantitative demands’ were associated with higher levels of ‘burnout’. At low ‘quantitative demands’, the ‘burnout’ level was quite similar for all nurses. However, when ‘quantitative demands’ increased, nurses who confirmed that they had the resources stated a lower ‘burnout’ level than nurses who denied having them. This trend is repeated by the resources ‘workplace commitment’, ‘good working team’ and ‘recognition from supervisor’.

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The palliative care aspect ‘extent of palliative care’ showed that spending more than 20 percent of working time in care for palliative patients increased burnout significantly by a value of approximately 5 on a 0 to 100 scale ( Table 5 ).

The aim of the present study was to analyse the buffering role of resources on the relationship between workload and burnout among nurses. This was done for the first time by considering palliative care aspects, such as information on the extent of palliative care.

The study shows that higher quantitative demands were associated with higher levels of burnout, which is in line with other studies [ 37 , 39 ]. Furthermore, the results of this study indicate that working in a good team, recognition from supervisor and workplace commitment is a moderator within the workload—burnout relationship. Although the moderator analyses revealed low buffering effect values, social resources were identified once more as important resources. This is consistent with the results of a study conducted in the field of specialised palliative care in Germany, where a good working team and workplace commitment moderated the impact of quantitative demands on nurses burnout [ 52 ]. A recently published review also describes social support from co-workers and supervisors as a fundamental resource in preventing burnout in nurses [ 53 ]. Workplace commitment was not only reported as a moderator between workload and health in the nurse setting [ 37 ], but also as a moderator between work stress and burnout [ 54 ] and between work stress and other health related aspects outside the nurse setting [ 55 ]. In the present study, the effect of high workload on burnout was reduced with increasing workplace commitment. Nurses reporting a high work commitment may experience workload as less threatening and disruptive because workplace commitment gives them a feeling of belonging, security and stability. However, there are also some correlation studies which observed no direct relationship between workplace commitment and burnout for occupations in the health sector [ 56 ]. A study from Serbia assessed workplace commitment by nurses and medical technicians as a protective factor against patient-related burnout, but not against personal and work-related burnout [ 57 ]. Furthermore, a study conducted in Estonia reported no relationship between workplace commitment and burnout amongst nurses [ 58 ]. As there are indications that workplace commitment is correlated with patient safety [ 59 ], the development and improving of workplace commitment needs further scientific investigation.

This study observed slightly higher burnout rates among nurses who reported a ‘good working team’ for low workload. This fact is not decisive for the interpretation of the moderation effect of this resource because moderation is present. When workload increased, nurses who confirmed that they worked in a good working team stated a lower burnout level. However, the result of the current study showed that a good working team is particularly important when workload increases, in the most extreme cases team work in palliative care is necessary to save a person’s life. Because team work in today’s health care system is essential, health care organisations should foster team work in order to enhance their clinical outcomes [ 60 ], improve the quality of patient care as well as health [ 61 ] and satisfaction of nurses [ 62 ].

The bivariate analysis revealed that nurses who reported getting recognition from colleagues, through the social context, salary and gratitude from relatives of patients stated a lower value on the burnout scale. This is in accordance with the results of a qualitative study, which indicated that the feeling of recognition, and that one’s work is useful and worthwhile, is very important for nurses and a source of satisfaction [ 63 ]. Furthermore, self-care, self-reflection [ 64 ] and professional attitude/dissociation seem to play an important role in preventing burnout. The bivariate analysis also revealed a relationship between resilience and burnout. Nurses with high resilience reported lower values on the burnout scale, but a buffering role of resilience on burnout was not assessed. The present paper focuses solely on quantitative demands and burnout. In future studies, the different fields of nursing demands, like organisational or emotional demands, should be assessed in relation to burnout, job satisfaction and health.

Finally, we observed whether the consideration of palliative care aspects is associated with burnout. The bivariate analysis revealed a relationship between the extent of palliative care, number of patient deaths within the last month and burnout. Using regression analyses, only the extent of palliative care was associated with burnout. Since, to the best of our knowledge, the present study is the first study to consider palliative care aspects within general palliative care in Germany, these variables need further scientific investigation, not only within the demand—burnout relationship but also between the demand—health and the demand—job satisfaction relationship. Furthermore, palliative care experts from around the world considered the education and training of all members of staff in the fundamentals of palliative care to be essential [ 9 ]. One-fourth of the respondents in the present study had an additional qualification in palliative care, which was not obligatory. We assessed a relationship between quantitative demands and burnout but no relationship between an additional qualification and quantitative demands nor burnout. Nevertheless, we assessed a protective effect of the additional qualification within the pilot study in specialised palliative care, in relation both to organisational demands and demands regarding the care of relatives [ 6 ]. This suggests that the additional qualification is a resource, but one which depends on the field of demand. Further analyses would be required to review benefits achieved by additional qualifications in general palliative care.

The variable extent of palliative care is the one with the most missing values in the survey, thus future analyses should not only study larger samples but also reconsider the question on extent of palliative care.

Finally, it can be said that the main contribution of the present study is to make palliative care aspects in non-specialised palliative care settings a subject of discussion.

Limitations

The following potential limitations need to be stated: although a random sample was drawn, the sample is not representative for general palliative care in Germany due to a low participation rate of the health facilities, a low response rate of the nurses, the different responses of the health facilities and the exclusion of hospitals. One possible explanation for the low participation rate of the health facilities is the sampling procedure and data protection rules, which did not allowed the study team to contact the institutions in the sample. Due to the low participation rate, the results of the present study may be labelled as preliminary. Further, the data are based on a detailed and anonymous survey, and therefore the potential for selection bias has to be considered. It is possible that the institutions and nurses with the highest burden had no time for or interest in answering the questionnaire. It is also possible that the institutions which care for a high number of palliative patients may have taken particular interest in the survey. Additionally, some items of the questionnaire were self-developed and not validated but were considered valuable for our study as they answered certain questions that standardized questionnaires could not. The moderator analyses revealed low effect values and the variance explained by the interaction terms is rather low. However, moderator effects are difficult to detect, therefore, even those explaining as little as one percent of the total variance should be considered [ 65 ]. Consequently, the additional amount of variance explained by the interaction in the current study (2% for workplace commitment and recognition of supervisor and 1% for good working team) is not only statistically significant but also practically and theoretically relevant. When considering the results of the current study, it must be taken into account that the present paper focuses solely on quantitative demands and burnout. In future studies, the different fields of nursing demands have to be carried out on the role of resources. This not only pertains for burnout, but also for other outcomes such as job satisfaction and health. Finally, the cross-sectional design does not allow for casual inferences. Longitudinal and interventional studies are needed to support causality in the relationships examined.

Conclusions

The present study provides support to a buffering role of workplace commitment, good working teams and recognition from supervisors on the relationship between workload and burnout. Initiatives to develop or improve workplace commitment and strengthen collaboration with colleagues and supervisors should be implemented in order to reduce burnout levels. Furthermore, the results of the study provides first insights that palliative care aspects in general palliative care may have an impact on nurse burnout, and therefore they have gone unrecognised for too long in the scientific literature. They have to be considered in future studies, in order to improve the working conditions, health and satisfaction of nurses. As our study was exploratory, the results should be confirmed in future studies.

Supporting information

S1 table. number of questionnaires sent out to facilites and response rate..

https://doi.org/10.1371/journal.pone.0245798.s001

Acknowledgments

We thank the nurses and the health care institutions for taking part in the study. We thank D. Wendeler, O. Kleinmüller, E. Muth, R. Amma and C. Kohring who were helpful in the recruitment of the participants and data collection.

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CDC: Burnout Keeps Rising for Nurses and Other Healthcare Workers

• According to recent federal data, healthcare workers experienced more mental health declines between 2018 and 2022 than workers in other sectors.
• Health workers reported fewer mental health problems when they worked in supportive environments.
• The CDC has designed a new intervention to create healthier healthcare workplaces called Impact Wellbeing.

A new study from the Centers for Disease Control and Prevention (CDC) found that nurse burnout is on the rise, with significantly more nurses and other healthcare workers reporting burnout in 2022 compared with 2018.

At the same time, a new federal campaign, Impact Wellbeing , is designed to help foster healthier healthcare workplaces. While the COVID-19 pandemic undoubtedly increased nurse burnout , experts noted that the problem persists even as the pandemic subsides. Interventions are needed to improve working conditions in healthcare.

Nurse Burnout and Mental Decline During COVID-19

The report, published in the CDC’s Morbidity and Mortality Weekly Report, was based on survey data collected in 2018 and 2022. Investigators found that health workers experienced a more significant mental decline during these four years than non-healthcare workers included in the survey. Of the 551 healthcare workers who responded to the surveys, 27% were nurses.

The study began before the 2020 onset of the COVID-19 pandemic that overwhelmed many facets of the healthcare system with a massive influx of highly contagious and seriously ill patients. Health workers were forced to adapt quickly, without proper training or adequate supplies, and care for patients for extended periods in isolation.

In 2022, the study looked at healthcare workers, non-healthcare essential workers, and other workers four years after the initial survey began and two years after the start of the pandemic.

Over those four years, nurses and other healthcare workers reported worsening outcomes in various mental health domains — including symptoms of burnout — than non-healthcare workers.

Key Findings From the CDC Study on Burnout

Some key findings from the CDC research include:

• Nearly half of health workers reported often feeling burned out in 2022, up from 32% in 2018.
• 1 in 4 U.S. essential workers (including healthcare professionals) received a mental health diagnosis since the pandemic’s onset.
• Favorable working conditions, such as trust in management and supervisor support, were associated with lower odds of burnout and overall poor mental health.
• Healthcare workers reported less burnout when they had enough time to complete their work and be productive.
• The number of health workers who reported harassment (e.g., bullying, verbal abuse) more than doubled from 2018 to 2022.
• Feelings of harassment at work increased feelings of anxiety, depression, and burnout.
• In 2022, almost half of the surveyed health workers planned to look for a new job.
• From 2018 to 2022, health workers reported an increase of 1.2 days of poor mental health during the previous 30 days (from 3.3 to 4.5 days)
• The percentage of health workers who reported feeling burnout very often increased from 11.6% to 19.0%.

Nurse Burnout: What Experts Recommend

“Being creative and working together with the workers to come up with solutions to help them better manage the work environment and their reaction is a good approach,” Nigam told NurseJournal. “Certainly, we should not be telling workers to buck up and be more resilient.”

Prevention is vital to nurse burnout, Nigam said. NIOSH has developed prevention training for supervisors of public health workers to improve their work environment. In 2022, NIOSH investigated health workers’ anxiety, depression, and burnout to create potential prevention strategies.

“NIOSH is very committed to helping hospitals in the U.S. tackle the root causes of health care worker mental health and burnout because it’s supposed to address the root cause, with intentions to make changes,” Nigam said.

NIOSH’s Impact Wellbeing toolkit offers multiple ways for employers to improve workplace wellness, including:

• Administer the NIOSH Worker Well-Being Questionnaire (WellBQ) to assess the whole person, including life outside work.
• Use the Dr. Lorna Breens Heroes’ foundation toolkit to help remove intrusive questions from hospital credentialing applications, making it safe for staff to seek out and receive mental health care.
• Use the Fundamentals of ‘Total Worker Health’ approaches workbook to guide advancing worker safety, health, and worker wellbeing.

Primary prevention strategies include having workers participate in the department’s decisions. Additional strategies endorsed include supportive supervision and increased psychological safety (being able to speak up without fear of consequences) when seeking help.

Frequently Asked Questions About Nurse Burnout

What causes nurse burnout.

The World Health Organization defines burnout as an occupational phenomenon created from chronic workplace stress that has not been managed effectively.

Burned-out nurses may feel physical, mental, and emotional exhaustion from job stress.

Risk factors for increased nurse burnout include:

• Younger age
• Decreased social support
• Limited family and colleagues’ ability to cope with a pandemic
• Increased perceived threat of COVID-19
• Longer working time in quarantine or isolation rooms
• Working in a high-risk environment
• Working in hospitals with inadequate and lack of supplies and resources

Is Nurse burnout the same as compassion fatigue?

Many nurses also experience compassion fatigue . Compassion fatigue is different from burnout: It is the diminished ability to provide empathetic and compassionate care due to the mental and emotional strain of caring for suffering patients.

COVID-19 left many patients and family feeling frightened and fearing death. Nurses exposed to frequent human suffering can become detached from their patients and take on a tasks-only role.

Why Did the COVID-19 Pandemic Make Nurse Burnout Worse?

The pandemic pushed extraordinary challenges and fears on healthcare workers that couldn’t be disguised.

A comprehensive systematic review and meta‐analysis found that in the U.S., within the first year of the pandemic, 143 out of 448 hospital healthcare workers who died were nurses.

Nurses who were exposed or in contact with suspected or verified positive COVID‐19 patients were more often distressed. Many were worried about taking the virus home to their families.

Nurses also experienced other adverse effects, such as:

• Psychological distress
• Sleep disturbances
• Post‐traumatic stress disorder (PTSD)

How Do We Fix Nurse Burnout?

Psychologically safe workplaces can help to promote well-being. Health workers reported fewer mental health issues when they said they work in supportive environments.

Impact Wellbeing is one example of a workplace intervention. Some other clinician-ranked burnout interventions include:

• Having uninterrupted breaks
• Increased scheduling control
• Additional resources for new-to-practice clinicians
• Less required documentation
• Creating a quiet space designated for meditation or reflection
• Electronic health records systems improvement
• Team communication improvement

Burn-out an “occupational phenomenon”: International Classification of Diseases . (2019). WHO

Galanis P, et al. Nurses’ burnout and associated risk factors during the COVID‐19 pandemic: A systematic review and meta‐analysis . NIH

Nigam J, et al. (2023). Vital Signs: Health Worker–Perceived Working Conditions and Symptoms of Poor Mental Health — Quality of Worklife Survey, United States, 2018–2022 . CDC

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