Background

Critical care nurses have a burnout rate among the highest of any nursing field. Nurse burnout may impact care quality. Few studies have considered how temporal patterns may influence outcomes.

Objective

To test a longitudinal model of burnout clusters and associations with patient and clinician outcomes.

Methods

An observational study analyzed data from annual employee surveys and administrative data on patient outcomes at 111 Veterans Health Administration intensive care units from 2013 through 2017. Site-level burnout rates among critical care nurses were calculated from survey responses about emotional exhaustion and depersonalization. Latent trajectory analysis was applied to identify clusters of facilities with similar burnout patterns over 5 years. Regression analysis was used to analyze patient and employee outcomes by burnout cluster and organizational context measures. Outcomes of interest included patient outcomes (30-day standardized mortality rate and observed minus expected length of stay) for 2016 and 2017 and clinician outcomes (intention to leave and employee satisfaction) from 2013 through 2017.

Results

Longitudinal analysis revealed 3 burnout clusters among the 111 sites: low (n = 37), medium (n = 68), and high (n = 6) burnout. Compared with sites in the low-burnout cluster, those in the high-burnout cluster had longer patient stays, higher employee turnover intention, and lower employee satisfaction in bivariate models but not in multivariate models.

Conclusions

In this multiyear, multisite study, critical care nurse burnout was associated with key clinician and patient outcomes. Efforts to address burnout among nurses may improve patient and employee outcomes.

Notice to CE enrollees

This article has been designated for CE contact hour(s). The evaluation demonstrates your knowledge of the following objectives:

  1. Identify trends in burnout among critical care nurses across medical centers.

  2. Describe characteristics associated with different burnout trends over time.

  3. Describe how burnout trends over time relate to clinical and employee outcomes.

To complete the evaluation for CE contact hour(s) for this article #A21802, visit www.ajcconline.org and click the “CE Articles” button. No CE evaluation fee for AACN members. This expires on November 1, 2023.

The American Association of Critical-Care Nurses is accredited as a provider of nursing continuing professional development by the American Nurses Credentialing Center’s Commission on Accreditation, ANCC Provider Number 0012. AACN has been approved as a provider of continuing education in nursing by the California Board of Registered Nursing (CA BRN), CA Provider Number CEP1036, for 1.0 contact hour.

Burnout is a maladaptive response to persistent work-related stress that is characterized by feelings of emotional exhaustion, depersonalization or cynicism, and lack of personal achievement.1  In recent years, a growing body of literature has drawn attention to the high prevalence of burnout among health care workers and the pervasive health system factors that perpetuate the burnout crisis.2,3 

Increasingly, clinicians and clinical leaders are advocating for organizational changes to mitigate burnout and promote clinician well-being.4  A key step in this advocacy is being able to identify and communicate the impact of burnout on patients, clinicians, and health systems.5  Studies that examine longitudinal data and include multiple medical centers would support these efforts.

Nurses working in the intensive care unit (ICU) can be particularly susceptible to burnout because they deal with many complex patient care challenges and need to navigate multidisciplinary relationships at work. Nurses in the ICU must cope with dying patients, moral distress, and perceived delivery of inappropriate care.6,7  Approximately a third to half of nurses have symptoms of burnout.810  Because of their essential role on the front lines of delivering care, burnout in ICU nurses may be a major driver of poor patient outcomes. A meta-analytic review identified burnout as a risk to patient satisfaction and patient safety.11  In a cross-sectional study, nurse burnout was associated with higher ICU mortality and longer patient stays.12  In addition to its impact on patients, burnout can have devastating consequences for clinicians themselves, causing higher rates of depression, substance abuse, and suicide.1315  Burnout also contributes to substantial health system costs because of burnout-related turnover and recruitment challenges.16,17  The Veterans Health Administration (VA) is one of the largest integrated health care systems in the United States. Nursing staff shortages are common across the VA partly because of high turnover and difficulties with recruitment,18  concerns likely relevant to other organizations as well. Our previous work confirmed high rates of burnout among VA critical care nurses.19 

Nurses working in intensive care units can be susceptible to burnout.

A major gap in the literature is that most studies have focused on burnout at the individual level, with less attention directed to the site-level impact of burnout across multiple facilities. Additionally, few studies have accounted for variation in burnout in relation to outcomes over time.20  This information is critical for establishing the impact of burnout on important outcomes and revealing areas in which actions to reverse negative trends can be taken. Understanding the relationships between levels of burnout in the workforce and patient outcomes could provide valuable evidence that directs ICU improvement efforts, which may be particularly salient during the COVID-19 pandemic.21,22 

Our research aims were to perform a site-level analysis of longitudinal burnout among critical care nurses and to determine the association of burnout with key outcomes over multiple years. We hypothesized that sites with higher levels of burnout over time would have worse performance on patient outcomes such as length of stay and mortality and on the clinician outcomes of intention to leave and employee satisfaction.

Study Design

We conducted a facility-level longitudinal study of self-reported survey data combined with internally collected risk-adjusted performance measures to identify relationships between burnout patterns and patient and employee outcomes. Our study population included ICU registered nurses in the VA from 2013 through 2017. The institutional review board at VA Boston Healthcare System approved the study protocol.

Participants

Data from nurses were obtained from the All-Employee Survey (AES). The AES is an anonymous and confidential census survey conducted annually in the VA since 2006. The survey offers employees’ perspectives about working conditions.23  Data from the AES were collected primarily (>95%) through online surveys in June of each calendar year from 2013 through 2017. The response rate ranged from 56.1% to 60.3% each year. To identify survey responses from ICU nurses for our study sample, we examined self-reported responses about occupation and responses to the question “What is the main type of service provided?” wherein we included respondents selecting “intensive care unit—critical care.” We aggregated data to the site level for each year with at least 5 respondents.

Study Measures

Our independent variable of interest was derived from self-reported measures of burnout on the AES. We identified burnout on the basis of 2 single-item statements about depersonalization (“I worry that this job is hardening me emotionally”) and/or emotional exhaustion (“I feel burned out from my work”) from the Maslach Burnout Inventory.24  Responses were reported on a 7-level frequency scale ranging from “never” to “every day.” A respondent who reported having either of the 2 experiences once a week or more frequently was considered to be experiencing burnout.25  This coding approach has been evaluated and validated against the full set of items in the depersonalization and emotional exhaustion domains from the complete Maslach Burnout Inventory.26,27 

Patient Outcomes

Patient outcomes included facility-level observed minus expected length of stay and the standardized 30-day mortality rate for ICUs; for these outcomes we used data from 2016 and 2017. The standardized 30-day mortality rate is computed by dividing the number of observed deaths within 30 days of hospital admission by the number of deaths predicted to occur. The predicted value is risk adjusted in a multivariate model by using individual patient characteristics, diagnoses, physiological variables, and length of stay.28,29  Values of less than 1 indicate that there were fewer deaths than predicted (ie, preferred values), and values greater than 1 indicate that there were more deaths than predicted.

The observed minus expected length of stay is exactly what it says. The expected value is obtained via a method similar to that used for the predicted 30-day mortality rate and then subtracted from the observed (ie, actual) length of stay. Values of less than 0 indicate that the overall observed length of stay in a unit was less than expected (ie, preferred values), and values greater than 0 indicate that the length of stay was greater than expected. Data for both measures were provided only at the summative level by the Inpatient Evaluation Center within the VA Office of Analytics and Performance Integration under the Office of Quality and Patient Safety.

Outcomes included employee satisfaction and intent to leave and adjusted patient length of stay and 30-day mortality.

Clinician Outcomes

Clinician outcomes included turnover intention and the best places to work index, which were reported in the VA AES from 2013 through 2017. To identify turnover intention, we used responses to the statement “I plan to leave my job within the next 6 months.” Responses were reported on a 5-point scale; lower scores indicated less agreement. The best places to work index is a weighted score ranging from 0 to 1, computed for each employee and averaged for each site on the basis of 3 AES items that ask about overall satisfaction, organizational satisfaction, and organizational commitment.30 

Covariates

We included covariates to account for variation in site-level characteristics. Measures included the number of respondents per site and whether a site had Magnet recognition, which reflects a focus on developing and retaining nurses.31  We used the VA system approach for classifying ICUs as basic, moderate, or complex. The site-level classification system is adapted from the private sector and takes into account the availability of subspecialists, pharmacy, diagnostic and therapeutic radiologic procedures, and laboratory services.32  We included academic teaching affiliation and US census region. We adjusted for time-based changes in outcomes by including fixed effects by year.

Statistical Analysis

We characterized burnout clusters by using a site-level analysis of burnout rates for 5 years according to data from the VA AES. Clusters are used to help represent a latent class reflecting common patterns among measured variables. We used latent growth curve models estimated in Mplus software (Muthén & Muthén) to create facility burnout clusters that remained consistent over time.33  Change in burnout was modeled as a function of time through specification of a latent variable, providing an estimate of the average trajectory and variation over time.34  Full-information maximum likelihood was applied for missing data. We first tested a 1-class solution, then gradually increased to 5 classes to assess model fit. We evaluated and selected the best-fitting solution based on model comparison tools for a variety of fit statistics: Bayesian information criterion, bootstrapped χ2 test, entropy, Lo-Mendell-Rubin likelihood ratio test, and Vuong-Lo-Mendell-Rubin likelihood ratio test.3537 

We used χ2 tests and analysis of variance to characterize ICU nurse burnout clusters by site-level variables. We then conducted multivariate generalized linear regression, while controlling for covariates, to assess the relationship between burnout clusters and patient and clinician outcomes. We also examined whether patient outcomes were associated with burnout. Statistical analyses were performed with a general least-squares random-effects model with robust standard errors using Stata software, release 15 (StataCorp LLC).

Three longitudinal profiles emerged for low-, medium-, and high-burnout medical centers.

We included a mean of 23.44 survey respondents from the 111 VA medical centers for each year of the study. After testing several variations for classes and examining model fit statistics across all sites for 5 years, we selected a final model with 3 latent variable classes to describe site-level ICU registered nurse burnout trajectories on the basis of a variety of fit statistics. Given the observed patterns, we categorized the 111 medical centers into 3 burnout clusters: low burnout (n = 37), medium burnout (n = 68), and high burnout (n = 6). We computed the mean (SD) burnout rates among sites for each year (Table 1). The mean percentages of nurses reporting burnout across 5 years were 19.2%, 37.0%, and 59.3% in the low-burnout, medium-burnout, and high-burnout clusters, respectively.

In bivariate analysis, burnout clusters were associated with several site-level characteristics (Table 2). We found a significant association between burnout cluster and geographic location (P = .02), with more sites in the high-burnout cluster located in the western United States. We also found ordered differences in best places to work index (P < .001) and turnover intention (P < .001); sites in the low-burnout cluster had more favorable scores.

In a multivariate model, neither complexity, standardized 30-day mortality rate, nor the observed minus expected length of stay was associated with burnout. Magnet status was negatively associated with burnout (−0.12; P = .01). Employee satisfaction was lowest at sites in the high-burnout (−0.39 percentage points) and medium-burnout (−0.14 percentage points) clusters (Table 3). Turnover intention also showed an ordered effect, with significantly higher intention-to-leave scores in the high-burnout (0.58 percentage points, P < .01) and medium-burnout (0.18 percentage points, P < .01) clusters than in the low-burnout cluster. Compared with sites in the low-burnout cluster, sites in the high-burnout cluster had a higher observed minus expected length of stay (0.45 standardized units, P < .01).

In this nationwide study of burnout among ICU registered nurses over time, sites in the high-burnout cluster had longer than expected risk-adjusted lengths of stay, unlike sites in the low-burnout cluster. Burnout may affect length of stay through several potential mechanisms leading to worse quality and safety, which can delay recovery. Specifically, burnout may contribute to inefficiency that delays discharge planning.38  Burnout has been reported to contribute to greater use of workarounds39  and care left undone,40  which could lead to higher infection rates41  and prolonged stays.42  Further, burned-out clinicians may experience problems engaging with families of critically ill patients, delaying important discussions on care planning for patients.43  Perceived use of non-beneficial treatments, unnecessary diagnostic tests, or delayed end-of life decisions may increase burnout44  and lead to longer stays. Although our analysis did not find that site-level burnout was associated with mortality, length of stay, or ICU complexity, further research is warranted to investigate these potential causal mechanisms that could link burnout to longer stays.

Our finding that burnout cluster was associated with lower employee satisfaction and intention to leave at the site level is consistent with the results of individual-level research.45  Further work could consider the role of mediating factors such as organizational climate and leadership. Organizational climate is an important risk factor for burnout and may influence job satisfaction and intention to leave.4648  Additional work, such as qualitative investigations, to understand organizational climate and leadership style exhibited among units with high and low levels of burnout may yield insights that could guide future interventional studies.

To address potential detrimental effects of burnout on care quality and clinician outcomes, health systems may invest in solutions to promote clinician well-being.49  Emerging evidence suggests that organizational solutions (eg, schedule management, clinician autonomy)50  may be more effective than individual solutions (eg, gardens, mindfulness interventions, stress management)5153  and could guide health system responses. Organizational and leadership support for such practices will be important for success.54,55 

Our findings highlight 3 patterns of burnout that tend to be stable over time: low, medium, and high. The sites in the high-burnout cluster are particularly important given that more than half of the nurses at these sites may be experiencing burnout. Understanding local field conditions at these sites would be an important starting point for learning about unique and shared-site issues of high-burnout sites. It is important for health systems to monitor burnout because of the negative impacts on outcomes analyzed in this study. The high-stress situations in the ICU that have materialized during the COVID-19 pandemic underscore the importance of monitoring burnout.56,57 

Limitations

We used a dichotomous definition of burnout with 2 single-item questions from the Maslach Burnout Inventory. Although this approach has been validated against the comprehensive Maslach Burnout Inventory, it may not describe the full spectrum of clinician experiences.25  Because of the anonymous and confidential nature of the survey, we were unable to track respondents over time and instead relied on site-level estimates. Although our overall response rate was relatively large, the number of respondents at some of the ICUs was smaller than preferred, which could reflect the size and complexity of the unit as well as interest in responding to the survey.

It is important to recognize the ever-present risk of burnout in health care workers and to work to mitigate the strain on clinicians.

Without interview and observational data, we also were unable to evaluate differences in contextual factors, such as staffing and specialization, between the ICUs. Intensive care unit factors such as end-of-life care, ethical issues, and bearing witness to trauma may also play a significant role in the development of burnout but were not addressed in this investigation. Although we had 5 years of data for burnout and organizational characteristics, our ICU mortality and length of stay outcomes data were limited to 2 years. Our outcome measures were based on an observed-to-expected model with aggregate values reported. Whether certain patient types may affect burnout more than others is unclear; for example, patients receiving mechanical ventilation may be more challenging to care for and may lead to greater burnout. Although we partially accounted for patient type by including complexity as a covariate, we lacked individual patient measures for refined analysis. We used summative measures to represent clinical outcomes; our data sources did not include the number of events, eligible patients, or unadjusted prevalence ratios. These data could provide a focus for future research to examine patient-level analyses. We did not account for the balance between supply and demand characteristics, which may be important to understand ICU capacity strain.58,59  Thus, unmeasured factors, such as workload, may be important confounders. Our study also focused specifically on registered nurses to minimize the variation in professional roles observed in other studies,60  but examining other occupations may offer valuable insights. Although we assessed turnover intention, ascertaining the reasons for wanting to leave would be important for understanding whether work-related factors, personal factors, or a combination of factors contributed to nurse ratings. The study was conducted in the VA, so the results may not be generalizable outside this setting.

Although our study examined clinical data that predated the COVID-19 pandemic, we believe that our findings may have additional relevance to current care delivery. At the time of writing the manuscript, research on the negative effects of COVID-19 on burnout was just being reported and newer research also seems to corroborate those early findings. Our study suggests that greater levels of burnout in ICUs may negatively impact employee outcomes, including turnover. This information could be particularly salient during the COVID-19 pandemic as health systems have struggled to maintain adequate staffing.

The results of our multiyear study emphasize the importance of nurse burnout clusters on employee morale and ICU length of stay, findings that may impact health care delivery in multiple ways. Organizational leaders should work closely with staff members to improve the work environment and mitigate the strain on frontline clinicians. It is important to recognize the ever-present risk of burnout in health care workers, given the intricate nature and demands of their work. Emotionally and physically exhausted clinicians can be hampered in delivering optimal care.

We are grateful for the support of the VA National Center for Organization Development for data access to the AES. We are also grateful to the Inpatient Evaluation Center within the VA Office of Analytics and Performance Integration under the Office of Quality and Patient Safety (formerly known as Office of Reporting, Analytics, Performance, Improvement & Deployment) for access to quality measures.

1
Maslach
C
,
Schaufeli
WB
,
Leiter
MP
.
Job burnout
.
Annu Rev Psychol
.
2001
;
52
:
397
422
.
2
Salvagioni
DAJ
,
Melanda
FN
,
Mesas
AE
,
González
AD
,
Gabani
FL
,
Andrade
SM
.
Physical, psychological and occupational consequences of job burnout: a systematic review of prospective studies
.
PLoS One
.
2017
;
12
(
10
):
e0185781
. doi:
3
Sikka
R
,
Morath
JM
,
Leape
L
.
The quadruple aim: care, health, cost and meaning in work
.
BMJ Qual Saf
.
2015
;
24
(
10
):
608
610
.
4
Shanafelt
TD
,
Noseworthy
JH
.
Executive leadership and physician well-being: nine organizational strategies to promote engagement and reduce burnout
.
Mayo Clin Proc
.
2017
;
92
(
1
):
129
146
.
5
Shanafelt
T
,
Goh
J
,
Sinsky
C
.
The business case for investing in physician well-being
.
JAMA Intern Med
.
2017
;
177
(
12
):
1826
1832
.
6
Poncet
MC
,
Toullic
P
,
Papazian
L
, et al
.
Burnout syndrome in critical care nursing staff
.
Am J Respir Crit Care Med
.
2007
;
175
(
7
):
698
704
.
7
Rushton
CH
,
Batcheller
J
,
Schroeder
K
,
Donohue
P
.
Burnout and resilience among nurses practicing in high-intensity settings
.
Am J Crit Care
.
2015
;
24
(
5
):
412
420
.
8
Epp
K
.
Burnout in critical care nurses: a literature review
.
Dynamics
.
2012
;
23
(
4
):
25
31
.
9
See
KC
,
Zhao
MY
,
Nakataki
E
, et al;
SABA Study Investigators and the Asian Critical Care Clinical Trials Group
.
Professional burnout among physicians and nurses in Asian intensive care units: a multinational survey
.
Intensive Care Med
.
2018
;
44
(
12
):
2079
2090
.
10
Filho
FA
,
Rodrigues
MCS
,
Cimiotti
JP
.
Burnout in Brazilian intensive care units: a comparison of nurses and nurse technicians
.
AACN Adv Crit Care
.
2019
;
30
(
1
):
16
21
.
11
Tawfik
DS
,
Scheid
A
,
Profit
J
, et al
.
Evidence relating health care provider burnout and quality of care: a systematic review and meta-analysis
.
Ann Intern Med
.
2019
;
171
(
8
):
555
567
.
12
Welp
A
,
Meier
LL
,
Manser
T
.
Emotional exhaustion and workload predict clinician-rated and objective patient safety
.
Front Psychol
.
2015
;
5
:
1573
.
13
Stehman
CR
,
Testo
Z
,
Gershaw
RS
,
Kellogg
AR
.
Burnout, drop out, suicide: physician loss in emergency medicine, part I
.
West J Emerg Med
.
2019
;
20
(
3
):
485
494
.
14
Hyman
SA
,
Shotwell
MS
,
Michaels
DR
, et al
.
A survey evaluating burnout, health status, depression, reported alcohol and substance use, and social support of anesthesiologists
.
Anesth Analg
.
2017
;
125
(
6
):
2009
2018
.
15
Molodynski
A
,
Lewis
T
,
Kadhum
M
, et al
.
Cultural variations in wellbeing, burnout and substance use amongst medical students in twelve countries
.
Int Rev Psychiatry
.
2021
;
33
(
1–2
):
37
42
.
16
Hamidi
MS
,
Bohman
B
,
Sandborg
C
, et al
.
Estimating institutional physician turnover attributable to self-reported burnout and associated financial burden: a case study
.
BMC Health Serv Res
.
2018
;
18
(
1
):
851
.
17
2020 NSI national health care retention & RN staffing report
.
NSI Nursing Solutions, Inc
.
2020
. Accessed August 28, 2020.
18
Department of Veterans Affairs Office of Inspector General
.
OIG Determination of Veterans Health Administration ’s Occupational Staffing Shortages
.
Department of Veterans Affairs
;
2019
.
Report #19-00346-241
.
19
Swamy
L
,
Mohr
D
,
Blok
A
, et al
.
Impact of workplace climate on burnout among critical care nurses working in the Veterans Health Administration
.
Am J Crit Care
.
2020
;
29
(
5
):
380
389
.
20
Shanafelt
TD
,
Mungo
M
,
Schmitgen
J
, et al
.
Longitudinal study evaluating the association between physician burnout and changes in professional work effort
.
Mayo Clin Proc
.
2016
;
91
(
4
):
422
431
.
21
Sasangohar
F
,
Jones
SL
,
Masud
FN
,
Vahidy
FS
,
Kash
BA
.
Provider burnout and fatigue during the COVID-19 pandemic: lessons learned from a high-volume intensive care unit
.
Anesth Analg
.
2020
;
131
(
1
):
106
111
.
22
Babamiri
M
,
Alipour
N
,
Heidarimoghadam
R
.
Research on reducing burnout in health care workers in critical situations such as COVID-19 outbreak
.
Work
.
2020
;
66
(
2
):
379
380
.
23
Osatuke
K
,
Draime
J
,
Moore
SC
, et al
.
Organization development in the Department of Veterans Affairs
. In:
Miller
TW
, ed.
The Praeger Handbook of Veterans Health: History, Challenges, Issues. and Developments, Volume 4: Future Directions in Veterans’ Healthcare
.
Praeger
;
2012
:
21
76
.
24
Maslach
C
,
Jackson
SE
.
The measurement of experienced burnout
.
J Organiz Behav
.
1981
;
2
:
99
113
.
25
West
CP
,
Dyrbye
LN
,
Satele
DV
,
Sloan
JA
,
Shanafelt
TD
.
Concurrent validity of single-item measures of emotional exhaustion and depersonalization in burnout assessment
.
J Gen Intern Med
.
2012
;
27
(
11
):
1445
1452
.
26
Waddimba
AC
,
Scribani
M
,
Nieves
MA
,
Krupa
N
,
May
JJ
,
Jenkins
P
.
Validation of single-item screening measures for provider burnout in a rural health care network
.
Eval Health Prof
.
2016
;
39
(
2
):
215
225
.
27
Dolan
ED
,
Mohr
D
,
Lempa
M
, et al
.
Using a single item to measure burnout in primary care staff: a psychometric evaluation
.
J Gen Intern Med
.
2015
;
30
(
5
):
582
587
.
28
Render
ML
,
Kim
HM
,
Deddens
J
, et al
.
Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure
.
Crit Care Med
.
2005
;
33
(
5
):
930
939
.
29
Render
ML
,
Almenoff
P
.
The Veterans Health Affairs experience in measuring and reporting inpatient mortality
. Accessed August 4, 2021.
30
Best places to work in the federal government
.
Partnership for Public Service
. Accessed June 4, 2020.
31
Find a Magnet hospital or facility
.
American Nurses Credentialing Center
. Accessed March 2, 2020.
32
Almenoff
P
,
Sales
A
,
Rounds
S
, et al
.
Intensive care services in the Veterans Health Administration
.
Chest
.
2007
;
132
(
5
):
1455
1462
.
33
Muthén
B
,
Muthén
LK
.
Integrating person-centered and variable-centered analyses: growth mixture modeling with latent trajectory classes
.
Alcohol Clin Exp Res
.
2000
;
24
(
6
):
882
891
.
34
Felt
JM
,
Depaoli
S
,
Tiemensma
J
.
Latent growth curve models for biomarkers of the stress response
.
Front Neurosci
.
2017
;
11
:
315
.
35
Schwarz
G
.
Estimating the dimension of a model
.
Ann Statistics
.
1978
;
6
(
2
):
461
464
.
36
Kim
SY
.
Determining the number of latent classes in single-and multi-phase growth mixture models
.
Struct Equ Modeling
.
2014
;
21
(
2
):
263
279
.
37
Nylund
KL
,
Asparouhov
T
,
Muthén
BO
.
Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study
.
Struct Equ Modeling
.
2007
;
14
(
4
):
535
569
.
38
Carpenter
A
,
Vora
SM
,
Kestenbaum
S
, et al
.
Afternoon ward rounds: bad for patients, bad for doctors?
Future Healthc J
.
2019
;
6
(
2
):
118
122
.
39
Halbesleben
JR
,
Rathert
C
,
Williams
ES
.
Emotional exhaustion and medication administration work-arounds: the moderating role of nurse satisfaction with medication administration
.
Health Care Manage Rev
.
2013
;
38
(
2
):
95
104
.
40
Liu
X
,
Zheng
J
,
Liu
K
, et al
.
Hospital nursing organizational factors, nursing care left undone, and nurse burnout as predictors of patient safety: a structural equation modeling analysis
.
Int J Nurs Stud
.
2018
;
86
:
82
89
.
41
Cimiotti
JP
,
Aiken
LH
,
Sloane
DM
,
Wu
ES
.
Nurse staffing, burnout, and health care-associated infection
.
Am J Infect Control
.
2012
;
40
(
6
):
486
490
.
42
Rathert
C
,
Williams
ES
,
Lawrence
ER
,
Halbesleben
JR
.
Emotional exhaustion and workarounds in acute care: cross sectional tests of a theoretical framework
.
Int J Nurs Stud
.
2012
;
49
(
8
):
969
977
.
43
Kirchhoff
KT
,
Beckstrand
RL
.
Critical care nurses’ perceptions of obstacles and helpful behaviors in providing end-of-life care to dying patients
.
Am J Crit Care
.
2000
;
9
(
2
):
96
105
.
44
Schwarzkopf
D
,
Rüddel
H
,
Thomas-Rüddel
DO
, et al
.
Perceived nonbeneficial treatment of patients, burnout, and intention to leave the job among ICU nurses and junior and senior physicians
.
Crit Care Med
.
2017
;
45
(
3
):
e265
e273
. doi:
45
Nantsupawat
A
,
Kunaviktikul
W
,
Nantsupawat
R
,
Wichaikhum
OA
,
Thienthong
H
,
Poghosyan
L
.
Effects of nurse work environment on job dissatisfaction, burnout, intention to leave
.
Int Nurs Rev
.
2017
;
64
(
1
):
91
98
.
46
Zhang
Y
,
Wu
X
,
Wan
X
, et al
.
Relationship between burnout and intention to leave amongst clinical nurses: the role of spiritual climate
.
J Nurs Manag
.
2019
;
27
(
6
):
1285
1293
.
47
Labrague
LJ
,
McEnroe-Petitte
DM
,
Gloe
D
,
Tsaras
K
,
Arteche
DL
,
Maldia
F
.
Organizational politics, nurses’ stress, burnout levels, turnover intention and job satisfaction
.
Int Nurs Rev
.
2017
;
64
(
1
):
109
116
.
48
Wagner
A
,
Rieger
MA
,
Manser
T
, et al;
WorkSafeMed Consortium
.
Healthcare professionals’ perspectives on working conditions, leadership, and safety climate: a cross-sectional study
.
BMC Health Serv Res
.
2019
;
19
(
1
):
53
.
49
West
CP
,
Dyrbye
LN
,
Erwin
PJ
,
Shanafelt
TD
.
Interventions to prevent and reduce physician burnout: a systematic review and meta-analysis
.
Lancet
.
2016
;
388
(
10057
):
2272
2281
.
50
Kleinpell
R
,
Moss
M
,
Good
VS
,
Gozal
D
,
Sessler
CN
.
The critical nature of addressing burnout prevention: results from the Critical Care Societies Collaborative’s National Summit and Survey on Prevention and Management of Burnout in the ICU
.
Crit Care Med
.
2020
;
48
(
2
):
249
253
.
51
Cordoza
M
,
Ulrich
RS
,
Manulik
BJ
, et al
.
Impact of nurses taking daily work breaks in a hospital garden on burnout
.
Am J Crit Care
.
2018
;
27
(
6
):
508
512
.
52
Steinberg
BA
,
Klatt
M
,
Duchemin
AM
.
Feasibility of a mindfulness-based intervention for surgical intensive care unit personnel
.
Am J Crit Care
.
2016
;
26
(
1
):
10
18
.
53
Magtibay
DL
,
Chesak
SS
,
Coughlin
K
,
Sood
A
.
Decreasing stress and burnout in nurses: efficacy of blended learning with stress management and resilience training program
.
J Nurs Adm
.
2017
;
47
(
7–8
):
391
395
.
54
Mealer
M
,
Hodapp
R
,
Conrad
D
,
Dimidjian
S
,
Rothbaum
BO
,
Moss
M
.
Designing a resilience program for critical care nurses
.
AACN Adv Crit Care
.
2017
;
28
(
4
):
359
365
.
55
Kuehnl
A
,
Seubert
C
,
Rehfuess
E
,
von Elm
E
,
Nowak
D
,
Glaser
J
.
Human resource management training of supervisors for improving health and well-being of employees
.
Cochrane Database Syst Rev
.
2019
;
9
(
9
):
CD010905
.
56
Shah
K
,
Chaudhari
G
,
Kamrai
D
,
Lail
A
,
Patel
RS
.
How essential is to focus on physician’s health and burnout in coronavirus (COVID-19) pandemic?
Cureus
.
2020
;
12
(
4
):
e7538
. doi:
57
CDC COVID-19 Response Team
.
Characteristics of health care personnel with COVID-19-United States, February 12-April 9, 2020
.
MMWR Morb Mortal Wkly Rep
.
2020
;
69
(
15
):
477
481
.
58
Bagshaw
SM
,
Opgenorth
D
,
Potestio
M
, et al
.
Healthcare provider perceptions of causes and consequences of ICU capacity strain in a large publicly funded integrated health region: a qualitative study
.
Crit Care Med
.
2017
;
45
(
4
):
e347
e356
. doi:
59
Opgenorth
D
,
Stelfox
HT
,
Gilfoyle
E
, et al
.
Perspectives on strained intensive care unit capacity: a survey of critical care professionals
.
PLoS One
.
2018
;
13
(
8
):
e0201524
. doi:
60
Johnson-Coyle
L
,
Opgenorth
D
,
Bellows
M
,
Dhaliwal
J
,
Richardson-Carr
S
,
Bagshaw
SM
.
Moral distress and burnout among cardiovascular surgery intensive care unit health-care professionals: a prospective cross-sectional survey
.
Can J Crit Care Nurs
.
2016
;
27
(
4
):
27
36
.

Footnotes

FINANCIAL DISCLOSURES

This study was supported in part by an American Thoracic Society Foundation unrestricted grant. Dr Rinne was supported by a VA Health Services Research and Development Career Development Award (1IK2HX002248). This work was supported in part by resources from the VA Bedford Healthcare System and the VA Boston Health-care System. The views expressed here are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs.

 

SEE ALSO

For more about burnout of critical care nurses, visit the AACN Advanced Critical Care website, www.aacnac-conline.org, and read the article by Howell, “Battling Burnout at the Frontlines of Health Care Amid COVID-19” (Summer 2021).

 

To purchase electronic or print reprints, contact American Association of Critical-Care Nurses, 27071 Aliso Creek Road, Aliso Viejo, CA 92656. Phone, (800) 899-1712 or (949) 362-2050 (ext 532); fax, (949) 362-2049; email, reprints@aacn.org.