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Emotional exhaustion as a predictor for burnout among nurses

Whittington, Kelli D. PhD, RN, CNE; Shaw, Thomas PhD, MBA; McKinnies, Richard C. PhD, RTR, RTT, CMD; Collins, Sandra K. PhD, MBA

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Nursing Management (Springhouse): January 2021 - Volume 52 - Issue 1 - p 22-28
doi: 10.1097/01.NUMA.0000724928.71008.47
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In Brief


In an effort to maximize patient outcomes, the Institute for Healthcare Improvement introduced a fourth prong to the Triple Aim, effectively drawing attention to the need for healthcare delivery systems to focus on the health of their employees and create cultures of employee wellness.1 With this additional focus, the aim to develop joy in the workplace has emerged as necessary to the improvement of patient outcomes. The American Nurses Association and the National Academy of Medicine weighed in to promote workplace wellness.2 Healthcare delivery systems quickly realized that the importance of investing in the well-being of their staff members had far-reaching effects, from shortening patient stays to retaining employees, both of which have a positive impact on the fiscal stability of the institution.3

We can't easily examine joy in the workplace without a thorough review of the opposite—those negative aspects that stem from the absence of joy and wreak deleterious effects on employees. Burnout, if not identified and addressed, does much to hinder full functionality within the workplace, negatively impacting the quality of care and patient satisfaction.4 Decreasing patient satisfaction scores have a significant role in the fiscal well-being of the institution, so ways to enhance patient outcomes and satisfaction via the mechanics of care delivery by caring and competent employees is integral.5

When developing competent and caring employees, specifically nurses, it's essential to recognize, minimize and, when possible, diminish burnout. There's a dearth of literature that supports one simple theory examining the relationship between nurse burnout and negative patient ramifications; however, there's no argument that the relationship exists.6–8 It's widely established that certain environments lend themselves to higher levels of burnout. Examples of high-stress environments include care delivery in high-intensity settings with changing patient dynamics, challenging multidisciplinary relationships, and the presence of uncontrollable circumstances such as those inherent in a global pandemic.9–12

The Maslach Burnout Inventory (MBI) has been widely used in a variety of studies to demonstrate the presence and extent of burnout, which encompasses three different feelings: emotional exhaustion (EE), depersonalization, and low personal accomplishment.13–17 By measuring individuals across these three themes, it may be possible to identify the presence of burnout and, going further, examine the contributing factors.

Once these factors are identified, it's possible for cultural shifts to occur, acknowledging the impact of burnout, fostering ways to avoid it, and developing strategies to minimize the impact.18–20 Although our research was initially intended to examine facets of burnout among RNs, due to the timing of the survey deployment and completion, the focus shifted slightly to examine the impact of burnout during high-stress situations, as afforded by the presence of COVID-19. During the crises of a pandemic, myriad factors combine to actuate burnout. Some of these factors include the novel treatment modalities associated with caring for patients with a communicable disease, loneliness, lack of needed equipment/supplies, and employer-provided training/support.11,21,22 Additional stress was felt by healthcare workers related to the fear of contracting the virus personally or functioning as the transmission host to their friends and family.23


Study design. A quantitative methodology was used to explore the experiences of burnout and facets of work life as measured by the MBI and the Areas of Worklife Survey (AWS) among RNs in the US. For this population, the MBI for Medical Personnel was employed because this inventory uses the term “patients” when relevant.16 To evaluate burnout, the feelings of EE, depersonalization, and personal accomplishment were assessed. The AWS examines six areas of work life separated into the domains of workload, control, reward, community, fairness, and values.17 This was appropriate for our study because it provided quantitative findings for the examination of descriptive statistics among variables, as well as the ability to examine correlations between variables and linear regression.

This study's participants were solicited via email addresses obtained from a national data distribution firm. The use of emails was a logical choice that allowed participants to easily navigate to the instruments via a hyperlink to an online survey tool. The use of distribution via email enabled the study to include individuals across a large geographic region rather than limiting the sample to known RNs within a smaller geographic area.

Sample and setting. Emails were sent to 11,000 individuals noted as being RNs who willingly allowed a marketing company to distribute their email addresses for research purposes during Spring 2020, which coincided with the early days of COVID-19 within the US. Of the sent emails, 114 individuals completed the survey, with 11 respondents being deleted due to failure to complete all aspects of the survey. The eligibility criterion was self-acknowledging being an RN; retired nurses and individuals who left the profession were allowed to complete the survey.

Data collection. Approval for this study was obtained by the institution's human subjects committee before the mass emailing. The email contained a cover letter introducing the study, with a statement that indicated the survey was voluntary and completion and submission provided consent. All responses were obtained via the password-protected online survey tool account of one researcher who then disseminated the anonymous and confidential data to all four researchers. Confidentiality was maintained by identifying participants with identification numbers; no personal identifying information was obtained.

Data analysis. All data were entered into a spreadsheet program. Once organized, data were further entered into statistical software for descriptive and inferential analysis. One researcher examined the descriptive nature of the data, providing a platform to describe the findings. A second researcher examined the correlation between variables, manipulating several variables to identify correlation. A third researcher, after verifying appropriateness, applied linear regression to examine the relationship between the independent variable of the six areas of work life and the dependent variable of EE. The fourth researcher verified all statistical analysis.


Of the 93 participants in the study, 89% reported themselves as female, with an average age of 50.63 years. The majority (62.4%) were employed in the nursing profession for 16 years or longer. For employment status, 15.1% of the respondents reported working part time, 74.2% reported working full time, and 10.8% reported that they had retired or left the nursing profession. Most participants (72%) were frontline staff, with 3.2% indicating that they were supervisors and 13% in either first-level, intermediate, or senior-level management roles. Regarding organization stability, 40.9% reported being at their current organization for 5 years or less, 13.5% for 6 to 10 years, 16.1% for 11 to 15 years, 11.2% for 16 to 20 years, and 10.1% for 21+ years; 5.6% reported that they had retired or left the nursing profession. (See Table 1.)

Table 1: - Participant demographics (n = 93)
Variables n %
Reported gender
Female 83 89.2
Male 8 8.6
Prefer not to answer 2 8.6
Age range
25–34 7 7.9
35–44 27 30.3
45–54 17 19.1
55–64 27 30.3
65+ 11 12.4
Level of education
Certified nurse assistant 2 1.1
LPN 2 1.1
RN 72 77.4
NP 12 12.9
Nurse anesthetist 3 3.2
Employed in nursing profession
1–5 years 10 10.8
6–10 years 10 10.8
11–15 years 15 16.1
16–20 years 13 14
21+ years 45 48.4
Employment status
Full time 69 74.2
Part time 14 15.1
Retired or left nursing profession 10 10.8
Position within current organization
Frontline staff 67 72
Supervisor 3 3.2
Management (first level) 2 2.2
Management (intermediate level) 6 6.5
Management (senior level) 4 4.3
Time at current organization
0–5 years 38 40.9
6–10 years 12 13.5
11–15 years 15 16.1
16–20 years 10 11.2
21+ years 9 10.1
Retired or left nursing profession 5 5.6

EE. Although all components of the MBI are relevant in the study of burnout, this study focused on the theme of EE. Participants reported experiencing EE between once a month to a few times a month on average. Based on the MBI scoring, burnout is indicated when the EE score is equal to or exceeds 27. The average EE score was 25.13, with a minimum of 3 and a maximum of 51. Almost half (47.3%) of the participants demonstrated a total EE score of 27 or higher. (See Table 2.)

Table 2: - Participant scores on EE subset of the MBI
Variable n %
Frequency of experiencing EE
Never 0 0
A few times a year 11 11.83
Once a month or less 19 20.43
A few times a month 19 20.43
Once a week 23 24.73
A few times a week 17 18.28
Every day 4 4.3
EE subset score
27 or higher 44 47.31

Areas of work life. The AWS consists of six subsets that can indicate congruence between individuals and their work role: workload (five questions), control (four questions), reward (four questions), community (five questions), fairness (six questions), and values (four questions).24 The AWS doesn't offer a definitive score, rather a view of the six subsets. When examined together, the researcher can extrapolate findings based on the results from each of the subsets.

After examining the descriptive statistics to get a full picture of the sample responses, inferential statistics were used to determine correlations and statistical significance with analysis of variance (ANOVA), as well as an exploration of linear regression. There was no significance noted between demographic variables and EE. Within the subsets of the AWS, workload, control, and community were noted to be statistically significant with EE. When examining the relationship between workload and EE, a positive correlation was noted, indicating that an increase in workload is associated with an increase in EE. When examining the relationship between control and EE, a negative correlation was noted, indicating that as control is perceived to decrease, there's an increase in EE. The relationship between community and EE was also negatively correlated, indicating that a loss of community affiliation results in increased EE. (See Table 3 and Table 4.)

Table 3: - Correlation of workload, control, and community with EE
Workload EE
Workload Pearson correlation 1 .397
Sig. (two-tailed) .000
N 93 93
EE Pearson correlation .397 1
Sig. (two-tailed) .000
N 93 93
Control EE
Control Pearson correlation 1 -.367
Sig. (two-tailed) .000
N 93 93
EE Pearson correlation -.367 1
Sig. (two-tailed) .000
N 93 93
Community EE
Community Pearson correlation 1 -.343
Sig. (two-tailed) .001
N 93 93
EE Pearson correlation -.343 1
Sig. (two-tailed) .001
N 93 93
Correlation is significant at the 0.01 level (two-tailed).

Table 4: - ANOVA of workload, control, and community with EE
Sum of squares df Mean square F Sig.
EE between groups 42.210 4 10.553 6.810 .000
Within groups 136.369 88 1.550
Total 178.579 92
EE between groups 47.731 8 5.966 3.830 .001
Within groups 130.848 84 1.558
Total 178.579 92
EE between groups 40.289 4 10.072 6.409 .000
Within groups 138.290 88 1.571
Total 178.579 92

Using EE as the dependent variable, additional statistical analysis of linear regression was employed to examine the role of the AWS subsets as independent variables. Linear regression analysis was appropriate because all assumptions were met, there was statistical significance, and the model works as a predictor when examining workload in relation to EE. Although some statistical significance did present, the remaining subsets of control, reward, fairness, and community didn't work as predictors when examining the relationship with EE.

Discussion and recommendations

When examining the data, there's an abundance of information that describes the relationship between EE and the subsets within the AWS; however, a few areas consistently emerge as significant, both statistically and clinically: workload, control, and community. Employers should consider developing a culture that promotes the health and wellness of employees to positively impact joy in the workplace, retain employees, and improve patient satisfaction and outcomes. Understanding the data obtained in this study provides the employer with a starting point for building and developing such a culture.

The relationship between workload and EE is apparent. Although workload may not always be flexible, fostering employees' perception of control over their workload can mitigate the impact of a stressful workload. Based on the questions in this subset, it's recommended that employees feel their workload is manageable and not all-encompassing, even encroaching into their personal lives.25 Fostering ways to prioritize workload, allow for breaks in the workday, and ensure that the workload is equitably distributed among employees allows nurses to feel a sense of input into their workload, enabling them to complete those tasks that are most important and leave their workplace behind them at the end of a shift.

The second platform employers can build on is that of control. Within this subset, individuals experience congruence with their job if they perceive a sense of control over how they complete their workload and an ability to demonstrate autonomy and influence decisions that impact their work. For the employer to enhance this congruence, they can, within reason, allow nurses to practice to the fullest extent of their licensure, prioritizing assignments and providing input about workflow. Fostering a sense of mindfulness within the context of control and workload may also be beneficial.26 Purposefully including discussions on mindfulness, as well as suggestions for how to incorporate mindfulness into daily practice, can improve well-being and enhance a sense of contentment.27 Encouraging the use of mindfulness mobile applications, such as those available for smartphone use, demonstrates a reduction in anxiety and stress, positively impacting a sense of control.28 Formal and informal group discussions are also a fiscally prudent resource that can positively impact the sense of control.29

The third subset providing opportunities for development includes cultivating a sense of community. Within this subset, individuals reported on their perception of trusting fellow coworkers to complete obligations within their roles, feeling supported, working cooperatively, experiencing open dialogue, and feeling close to colleagues. Developing and cultivating a sense of pride and belonging not only to the organization, but also to more personal subsets within the overall organization, can build this sense of community. Offering peer support develops human capital that nurtures a sense of community.30 Building opportunities for multidisciplinary collaboration grounds individuals together to work toward common themes.

A positive workplace culture

Regardless of how an organization elects to intentionally develop its workforce, the relationships between workload, control, and community and EE must be explored. Failure to do this inhibits the productive workplace, not only in work completed, but also in the human capital component. Using data from this research and other studies provides a platform for initiating a desirable organizational culture. Understanding the impact of having this culture in place before a crisis such as the current pandemic can mitigate some of its impact. Growing organizational culture can positively impact joy in the workplace, employee retention, and patient satisfaction and outcomes—all of which play an important role in the fiscal bottom line and the future viability of the organization.


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