The impact of health emergencies on nurses’ burnout: a systematic review and meta-analysis

Abstract

Background

Burnout is a prevalent occupational phenomenon among healthcare workers. To evaluate the current evidence on nurses’ burnout and the impact of turnover intention during the pandemic is imperative.

Objective

We aimed to comprehensively synthesize and quantify the impact of health emergencies caused by the COVID-19 pandemic outbreak on nurses’ burnout and identify factors associated with the negative impact.

Methods

Systematic searches were conducted in PubMed, Web of Science, EBSCO (ASP), Cochrane Library, and supplemented by a manual search, for publications from December 2019 to February 2023.

Results

A total of 176 articles involving 110,316 nurses were identified. The overall pooled estimate of the prevalence of burnout was 48% (95% confidence interval [CI] 42–55%). The mean score for overall burnout on the 22-item (7-point) Maslach Burnout Inventory (MBI) was 59.83 (95% CI 49.33 to 70.34). In the work environment, nurses who were exposed to COVID-19 (SMD 0.19, 95% CI 0.04 to 0.33) or worked in emergency departments and ICUs (SMD 0.10, 95% CI 0.06 to 0.14) scored higher for burnout compared to those in general wards. In the presence of increased burnout, overall burnout in nurses was associated with a sevenfold increase in depression (OR 7.40, 95% CI 3.82 to 14.35), a fourfold increase in anxiety (OR 4.14, 95% CI 2.15 to 7.98) and stress (OR 4.60, 95% CI 2.31 to 9.17), and a fourfold increase in low resilience (OR 4.06, 95% CI 2.13 to 7.76) in mental health outcomes. As burnout increased, turnover intention was nearly four times as likely compared with retention (OR 3.55, 95% CI 1.73 to 7.28), and it was related to the quality of care.

Conclusion

The results of this meta-analysis indicate that half of the nurses experienced burnout during the COVID-19. Nurses’ burnout is associated with the sustainability of healthcare organizations. Healthcare organizations and societies should invest more time and effort in implementing evidence-based strategies to mitigate nurses’ burnout across specialties, especially in emergency medicine and for younger nurses in specialized departments, to better prepare for future public health emergencies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-025-24150-9.

Introduction

The concept of burnout, also known as “burnout syndrome,” was first described by Freudenberger [1]. In May 2019, burnout was included in the 11th Revision of the International Classification of Diseases (ICD-11) as an occupational phenomenon [2] and conceptualized as resulting from chronic workplace stress that has not been successfully managed. It was characterized by three key dimensions related to work: emotional exhaustion (EE), which is a diminished sense of feelings of energy depletion or exhaustion; depersonalization (DP), which is a diminished sense of increased mental distance from one’s job, or feelings of negativism or cynicism related to one’s job; and personal accomplishment (PA), which is a diminished sense of reduced professional efficacy. In recent years, the issue of burnout and work-related stress among nurses has become increasingly concerning across various countries [3]. In the United States (US), 35.3% of nurses report the symptom of burnout [4] and in the United Kingdom, four in 10 nurses report at least one symptom of burnout [5]. In a recent review of 94 studies covering over 30 countries, the overall prevalence of burnout ranged from 1 to 78% globally [6].

It is important to thoroughly examine the prevalence and underlying causes of burnout among the nursing workforces. A secondary analysis shows that among 418,769 nurses in the US who reported leaving their jobs in 2017, 31.5% cited burnout as a reason [7]. The World Health Organization (WHO) State of the World’s Nursing Report 2020 predicted a global shortfall of 5.7 million nurses by 2030 if no action is taken [8]. A conservative base-case model estimated that approximately $4.6 billion in costs related to healthcare worker turnover and reduced clinical hours are attributable to burnout each year in the US [9]. Even more unfortunately, the COVID-19 pandemic has created new causes for unsafe working conditions and higher workloads, which have further exacerbated burnout in nurses. This situation has highlighted the global necessity of having sufficient numbers of nurses [10] and shone a light on the catastrophic impacts of nursing shortages throughout health systems in many countries [11]. Nurses play a crucial role and make great contributions during the pandemic. However, this large-scale and ongoing COVID-19 pandemic placed nurses across the world under immense physical and emotional strain [10, 12]. During the COVID-19 pandemic, studies have shown that burnout is not only linked to mental health outcomes [13] which can lead to health problems and even suicidal ideation in nurses [14, 15] but also associated with adverse events, poor quality of care, and productivity loss [16–18].

These significant findings regarding nurses’ burnout and turnover intention are also apparent in previous systematic reviews [19–25]. For example, a systematic review [19] showed that the overall prevalence of emotional exhaustion was 34.1%, of depersonalization was 12.6%, and of lack of personal accomplishment was 15.2%. Another systematic review [20] further assessed the prevalence of various mental health issues experienced by healthcare professionals during the COVID-19 pandemic. A recent mixed-methods systematic review identified a wide range of factors influencing healthcare workers’ turnover intention during the pandemic [21]. Several systematic reviews [22–24] have further suggested that a comprehensive synthesis of the connections between nurses’ burnout, mental health outcomes, support from various sources, and nurse turnover is crucial.These aspects are interrelated and essential for the overall effectiveness of healthcare organizations. These mutually beneficial relationships should be accessible to governments, healthcare institutions, and relevant social organizations to promote financial investments and policies aimed at reducing burnout among international nurses. One narrative review [25] discussed a few of these relationships. However, these previous systematic reviews also have some methodological limitations. For instance, there were no pooled estimates of the overall burnout rate of nurses and the main risk factors due to the limited number of studies reviewed [19]. No previous systematic reviews have taken a quantitative approach to evaluating the relationships between nurse burnout and influencing factors during the COVID-19 [20, 21, 25].

To more comprehensively and quantitatively assess the level of burnout among nurses and identify factors that may reduce burnout can help maintain the vitality and development of healthcare systems, reduce patient hospitalization costs, and minimize physical damage during public health emergencies [16, 18, 26]. Healthcare institutions should address healthcare providers’ burnout and its related consequences. A thorough understanding of the negative effects of epidemics and pandemics on nurses’ burnout is needed to mitigate the impact of burnout and develop appropriate support interventions [27]. In this study, we aimed to comprehensively synthesize and quantify the impact of health emergencies caused by the pandemic on nurses’ burnout and identify factors associated with this adverse impact.

Methods

This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Supplementary Appendix 1). This study was not pre-registered mainly due to the suddenness and rapid spread of the COVID-19 pandemic. The research needed to be quickly designed and implemented to address the urgent situation at that time. Nevertheless, in this study, we have provided a detailed description of the research methods, data sources, screening criteria, data extraction, and analysis process to maximize the transparency and reproducibility of the study.

Search and selection process

We searched PubMed, Web of Science, EBSCO (ASP), and Cochrane Library for English citations from December 2019 to February 2023. The selection process included combinations of key blocks of terms involving Medical Subject Headings terms and free words. The search strategies and related materials were detailed in Supplementary Appendices 2. To identify additional literature, we also manually searched the reference lists of previous reviews.

Inclusion and exclusion criteria

The inclusion criteria were as follows: (1) articles published in English (This study included only English publications due to: <5% non-English eligible records [28]. Excluded records are shown in the flowchart (Fig. 1). This aligns with recent nurse burnout meta-analyses [29]; (2) original articles reporting quantitative data; (3) participants being nurses; (4) research conducted during the COVID-19 outbreak; and (5) studies reporting the prevalence of overall burnout among nurses and the means (and standard deviation) of overall or subscale burnout. Exclusion criteria included: (1) conference abstracts or proceedings, clinical case reports, study protocols, letters, and commentaries; (2) dissertation reviews or purely theoretical discussion papers; (3) articles involving fewer than 70% of responses from nurses; (4) articles with no quantitative data; or (5) articles with obvious data errors. This selection process identified a total of 176 studies that met the criteria for the current meta-analysis.

Fig. 1.

Flow chart of the study selection procedure

Quality assessment

For this meta-analysis, the quality assessment was conducted using the Joanna Briggs Institute Checklist [30] quality assessment tool to evaluate the quality of both observational studies (e.g., cross-sectional, cohort studies) and experimental studies (e.g., randomized controlled trials). Two reviewers (HP and YL) independently evaluated the risk of bias and the quality of the studies, rating them according to the tool’s criteria and guidelines. Any potential disagreement was resolved through discussion and/or by involving a third appraiser (XL). The developers of the JBI Checklist did not assign a numerical scoring system to help determine study quality. Based on previously published systematic reviews using the JBI checklist, the risk of bias was assessed according to percentage [31, 32]. For this systematic review, the overall quality score was calculated as the percentage of ‘yes’ responses across all checklist items. According to Ancheta’s report [33] the percentage of studies scoring below 49% of the maximum score is considered high risk, 50%−70% is considered moderate risk, and above 70% is considered low risk. Detailed information about the quality of the included articles is listed in Supplementary Appendices 3 and 4.

Data collection and extraction

Two researchers (YL, HP) independently screened the titles and abstracts of all articles. The searches that met the inclusion criteria were exported into EndNote, and duplicate studies were removed. Then, articles were titled by three coders for further review of their full texts. In case of disagreement, we resolved the issue through discussion. A standardized Excel data extraction spreadsheet (Microsoft Inc.) was developed to facilitate the extraction of the following study information: (1) study author; (2) year of publication; (3) country or region; (4) study design; (5) study participants; (6) sample size; (7) participants’ demographic characteristics; (8) measurement tools; (9) prevalence of overall burnout; and (10) mean and standard deviation of overall burnout and subscales of burnout. In addition to overall burnout, participants were also assessed for the following three burnout subscales: emotional exhaustion, depersonalization, and personal accomplishment. We converted the extracted quantitative data into uniform log odds ratios and standardized mean differences using the statistical software Comprehensive Meta-Analysis [34]. The formulas for these transformations are provided in Supplementary Appendix 5. All three reviewers (YL, HP, and XL) completed data extractions independently and cross-checked them, resolving any disagreements through consensus.

Outcome measures

The main outcomes were the prevalence rates and the mean scores (including standard deviations) of burnout symptoms among nurses during the COVID-19 pandemic. For the mean scores (and standard deviations) of burnout symptoms, we reported the means of both overall burnout and its subscales. Research indicates that there is high sensitivity among different versions of measurement tools in assessing burnout prevalence. Moreover, the aggregation of these scales is supported by prior meta-analyses on burnout [34] as the core subscales remain fairly consistent despite slight variations in wording. Secondarily, indicators associated with nurse burnout mentioned in the original literature during the pandemic were also examined. We summarized these related indicators into the following aspects: demographic characteristics, work environment, mental health outcomes, organizational strategies, job satisfaction, social support, family support, and turnover intention. The definitions for each of the outcomes and indicators are provided in Supplementary Appendix 6.

Statistical analysis

In this review, for the main outcomes, we calculated the overall prevalence of burnout with 95% confidence intervals (CIs) of burnout and the mean scores (including standard deviations) of burnout symptoms among nurses, providing 95% CIs for overall burnout and its subscales, respectively. For the burnout indicators, we utilized odds ratios (ORs) and standardized mean differences (SMDs) to summarize and analyze the associations between burnout and other indicators. Specifically, ORs were used to describe the associations of burnout (overall burnout, emotional exhaustion, depersonalization, and personal accomplishment) with mental health outcomes (depression, anxiety, stress, resilience), organizational strategies, job satisfaction, social support, family support, and turnover intention. SMDs were used to analyze the associations between burnout (overall burnout emotional exhaustion, depersonalization, and personal accomplishment) and various factors including sociodemographic characteristics (gender, marital status, having children, age, education level, years of experience) and work environment (exposure to the COVID-19 patients, type of hospital, work department). If a study consisted of multiple groups of nurses varying in age, years of experience, and other factors, we combined them into a single group following the guidelines outlined in the Cochrane Handbook for Systematic Reviews of Interventions [35].

The heterogeneity of study effect sizes was assessed using the I² and Q statistics. A random-effects model was used for the meta-analysis when significant heterogeneity was observed (i.e., I² > 50% or P < 0.05 for Q). Otherwise (i.e., I² ≤ 50% and P ≥ 0.05 for Q), a fixed-effects model would be selected [36]. Publication bias was assessed using funnel plots and the Egger test [37] to examine the evidence of bias and the sensitivity of the conclusions of the meta-analysis to the bias [38]. All meta-analyses were conducted using the statistical software R version 4.0.2 with the “meta” package.

Results

We initially identified 3,907 records plus 1 article through other sources. After removing 1,144 duplicates, we screened 2,764 titles and abstracts for eligibility in this review. After screening, 176 studies met our inclusion criteria (Supplementary Appendix 7). The flowchart of the study selection process is presented in Fig. 1.

Study characteristics

Descriptive details of the eligible studies are presented in Supplementary Appendix 8. The 176 identified studies involved 110,316 nurses, including 163 cross-sectional studies with 106,632 nurses, 10 cohort studies with 3,437 nurses, and 3 randomized controlled studies with 247 nurses. It is worth noting that 8 out of 176 studies included a mixed sample of nurses (at least 70% of the participants were nurses) along with other health professionals.

Fifty-three out of 176 studies, involving 45,064 nurses, reported the prevalence of burnout using GIS (Geographic Information System), and 51 of these studies were analyzed for time trends. Out of 176 studies, one hundred thirty-nine reported the burnout scores of nurses, which were measured using various scales and involved 83,584 nurses. Among the 176 studies, 73 studies used various versions of the Maslach Burnout Inventory (MBI). We combined the various MBI scores in the meta-analysis of the overall burnout because the different various versions of the MBI are based on the same theoretical framework [39]. Twenty studies assessed burnout using the Copenhagen Burnout Inventory (CBI). Twenty-two studies assessed burnout using the Professional Quality of Life Scale. Eight studies assessed burnout using the Oldenburg Burnout Inventory (OBLI). Sixteen studies assessed burnout using different scales. The burnout measurement instruments used in the included studies are presented in Supplementary Appendix 7. Out of the 176 articles, 83 of them, involving 70,166 nurses, also examined the correlations of burnout with sociodemographics, work environment, mental health outcomes, external supports, turnover intention, and quality of patient care.

Quality assessment

According to the quality percentage scoring system for the JBI Checklist. Twenty out of 163 (12.26%) cross-sectional studies reported a medium risk of bias, while 143 out of 163 (87.73%) cross-sectional studies reported a low risk of bias. Among the cohort studies, three out of ten (30%) reported a high risk of bias, and six out of ten (60%) reported a high risk of bias. One out of three (33.33%) cohort studies reported a high risk of bias, while two out of three 66.67%) cohort studies reported a medium risk of bias. Overall, 144 (81.81%) of the studies reported a low risk of bias, 4 (2.27%) reported a high risk of bias, and 28 (15.90%) studies reported a medium risk of bias. The full results of the quality assessment are presented in Supplementary Appendices 4 and 5. These studies were rated as moderate and high risk primarily due to the lack of identification of confounding factors, which resulted in the absence of strategies to address confounding.

Prevalence and level of nurses’ burnout

The meta-analysis results on the prevalence and score level of nurse burnout are presented in eTable 1 in Supplementary Appendix 10. The overall burnout rate is 48% (95% CI 42–55%, I² = 99%), as illustrated in eFigure 1 in Supplementary Appendix 10. Egger’s regression test showed no publication bias (p > 0.05) (eFigure 1 in Supplemental Appendix 13). Sensitivity analysis showed that no single effect size influenced the overall result (eFigure 29 in Supplemental Appendix 13). Due to the significant heterogeneity among studies, we conducted subgroup analyses based on regions and time, which can be found in eFigures 2–4 in Supplementary Appendix 10.

The subsequent subgroup analysis revealed various burnout rates across regions, including Asia, Europe, Africa, North America, South America, and Oceania. Europe exhibited the highest burnout rate with a prevalence estimate of 54% (95% CI 40–68%, I² = 99%), while Oceania displayed the lowest rate at 22% (95% CI 1–89%, I² = 99%). At the country level, Ireland had the highest burnout rate at 77% (95% CI 60–90%), while Argentina showed the lowest rate at 20%. Further details of these analyses can be found in Fig. 2. To elucidate the temporal relationship between the pandemic and nurse burnout, we examined the timeframes of the included studies: 2020, 2021, and 2022. The highest burnout rate was identified in 2022, with a prevalence estimate of 52% (95% CI 35–69%, I² = 99%), while the lowest rate was observed in 2020, with a prevalence estimate of 47% (95% CI 38–57%, I² = 99%) (Fig. 3). Detailed information regarding these temporal analyses can be found in eTable 1 in Supplementary Appendix 10.

Fig. 2.

Prevalence of nurse burnout across continents

Fig. 3.

The changes in nurses’ burnout with the COVID-19 pandemic over time

This review also reported pooled random-effects mean estimates based on continuous data derived from burnout scores obtained from various burnout scales and their subscales. The mean scores for the full 22-item (7-point) MBI were collected fromr 29 studies. The overall score for burnout was 59.83 ([95% CI, 49.33 to 70.34], I² = 99.90%), 25.79 ([95% CI, 22.48 to 29.10], I² = 100.00%) for emotional exhaustion, 9.69 ([95% CI, 7.47 to 11.91]; I² = 100.00%) for depersonalization, and 28.98 ([95% CI, 26.30 to 31.65]; I² = 99.90%) for personal accomplishment. These results are pesented in Table 1 and eFigures 5–8 in Supplementary Appendix 10. Egger’s regression test indicated the presence of publication bias (p < 0.05) (see eFigures 3–5 in Supplemental Appendix 13), while sensitivity analysis revealed that no individual effect size significantly influenced the overall results for EE, DP, and PA (eFigures 31–33 in Supplemental Appendix 13). Five studies reported data on the 22-item (5-point) MBI mean scores. The mean score for EE was 26.09 ([95% CI, 20.40 to 30.77], I² = 99.51%), the mean DP score was 11.10 ([95% CI, 7.31 to 14.90], I² = 99.63%), and for PA it was 23.02 ([95% CI, 14.51 to 31.53], I² = 99.96%) (eFigure 9 in the Supplementary Appendix 10). Six studies provided data on the 15-item (5-point) MBI-GS. The mean score in the EE subscale was 11.73 ([95% CI, 10.90 to 12.57], I² = 86.84%); for DP, it was 7.78 ([95% CI, 5.89 to 9.66], I² = 98.85%), and for PA, it was 18.79 ([95% CI, 15.06 to 22.53, I² = 99.47%). Please refer to eFigure 9 in Supplementary Appendix 10 for details. The meta-analysis results of other scales (CBI, ProQoL, OBLI) are shown in Table 1.

Table 1.

Correlation coefficients for the SC thickness values calculated by the five different methods

Meta-analysis of nurses’ burnout score
Measurement and submeasure	No. of studies (No. of nurses)	Mean (95%CI)	Cut-off value				Publication bias	I2
MBI-22 (7-point)	9 (6852)	59.83 (49.33 to 70.34)		Low	Moderate	High	NA	99.75%
Emotional exhaustion	23 (24669)	25.79 (22.48 to 29.10)	EE	0–16	17–26	> 27	P = 0.02	100%
Depersonalization	21 (23973)	9.69 (7.47 to 11.91)	DP	0–6	7–12	> 13	P = 0.01	100%
Personal accomplishments	22 (24318)	28.98 (26.30 to 31.65)	PA	> 39	32–38	0–31	P = 0.0002	99.90%
MBI-GS-15 (5-point)	2 (1345)	34.22 (33.35 to 35.09		Low	Moderate	High	NA	0.00%
Emotional exhaustion	5 (2663)	11.73 (10.90 to 12.57)	EE	0–16	17–24	≥ 25	NA	86.84%
Cynicism	5 (2663)	7.78 (5.89 to 9.66)	DP	0–7	8–12	≥ 12	NA	98.85%
Personal accomplishments	5 (2663)	18.79 (15.06 to 22.53)	PA	≥ 16	12–15	0–11	NA	99.47%
MBI-22 (5-point)	2 (465)	64.28 (43.49 to 85.08)		Low	Moderate	High	NA	99.65%
Emotional exhaustion	5 (2347)	26.09 (20.40 to 30.77)	EE	0–16	17–26	≥ 27	NA	99.51%
Depersonalization	5 (2347)	11.10 (7.31 to 14.90)	DP	0–6	7–12	≥ 13	NA	99.63%
Personal accomplishments	5 (2347)	23.02 (14.51 to 31.53)	PA	≥ 39	32–38	0–31	NA	99.96%
CBI	5 (1072)	51.87 (45.64 to 58.10)	No/Low: <50				NA	89.64%
Personal Burnout	14 (3499)	52.28 (45.11 to 59.45)	Moderate: 50–74				P = 0.04	99.90%
Work Related Burnout	15 (4798)	45.76 (38.38 to 53.13)	High:75–99				P = 0.09	99.90%
Patient-Related Burnout	14 (3499)	46.41 (38.64 to 54.19	Severe: 100				P = 0.49	99.90%
ProQoL (5-point)	10 (1990)	25.53 (23.68 to 27.39)	Low: <18				NA	98.76%
			Moderate: 19–26
			High: >27
ProQoL (6-point)	5 (2175)	23.86 (20.84 to 26.87)	Low: <18				NA	98.76%
			Moderate: 19–26
			High: >27
OLBI	3 (660)	32.41 (17.53 to 47.28)	NR				NA	99.90%
Disengagement	3 (1843)	2.55 (2.22 to 2.88)	≥ 2.25				NA	97.67%
Exhaustion	5 (2257)	2.72 (2.49 to 2.96)	≥ 2.10				NA	99.36%

MBI Maslach Burnout Inventory, CBI Copenhagen Burnout Inventory, ProQoL Professional Quality of Life Scale, OLBI Oldenburg Burnout Inventory

The association between nurses’ burnout and demographics

The results of meta-analyses for secondary outcomes are presented in Figs. 4 and 5. In this review, 19 studies were identified regarding the ages of nurses. Among them, 16 articles reported a significant difference in overall burnout between nurses older than 30 years and their younger counterparts. Specifically, younger nurses scored higher on the burnout scale compared to older nurses (SMD 0.23, 95% CI 0.05 to 0.41) in the overall mean effect size using a random-effects model. Egger’s regression test showed no publication bias (p > 0.05) (eFigure 15 in Supplemental Appendix 13). Twenty-three studies were identified in this search. Higher educational levels were associated with burnout (SMD − 0.17, 95% CI −0.22 to −0.12) based on 17 studies (eFigure 6 in Supplementary Appendix 11). Egger’s regression test showed evidence of publication bias (p < 0.05) (eFigure 17 in Supplemental Appendix 13). Moreover, 14 studies indicated that nurses with Bachelor’s degrees had higher burnout scores compared to those with Master’s degrees or higher (SMD − 0.16, 95% CI −0.34 to −0.01) as illustrated in eFigure 7 in Supplementary Appendix 11. Egger’s regression test indicated the absence of publication bias (> 0.05) (eFigure 18 in Supplemental Appendix 13). This result further suggests that higher degrees are correlated with increased levels of burnout. The meta-analysis did not reveal statistically significant differences in terms of gender, marital status, having children, or years of experience.

Fig. 4.

Meta-analysis of the correlation between burnout and demographic characteristics, work environment

Fig. 5.

Meta-analysis of the correlation between burnout and Support, mental health outcomes, turnover intention

The association between nurses’ burnout and work environment

In this study, work environment factors included exposure to COVID-19 patients, type of hospital, and work department. Burnout was positively associated with exposure to COVID-19 patients in 24 studies, involving a total sample of 33,702 participants. Nineteen articles reported that overall burnout in nurses who were exposed to COVID-19 scored higher than non-exposed nurses (SMD 0.19, 95% CI 0.04 to 0.33), as depicted in Fig. 4. Egger’s regression test indicated there was a publication bias (> 0.05) (eFigure 20 in Supplemental Appendix 13). Additionally, eFigure 11 in the Supplementary Appendix 11 demonstrates significant differences in burnout scores between special units and general sections (SMD 0.10, 95% CI 0.06 to 0.14), based on 13 articles with a total sample of 14,787 participants. Egger’s regression test showed no evidence of publication bias (p > 0.05). However, the meta-analyses did not reveal statistically significant differences between government and private hospitals.

The association between burnout and support

We identified six studies related to job satisfaction in this search. All six articles reported that overall burnout in nurses was associated with job dissatisfaction (OR 5.14, 95% CI 2.39 to 11.07) (eFigure 6 in Supplementary Appendix 12). For burnout subscales, Heidari’s research revealed a statistically significant negative correlation between job satisfaction and emotional exhaustion (OR 0.21, 95% CI 0.15 to 0.30); and job satisfaction was positively correlated with personal accomplishment (OR 14.17, 95% CI 9.6 to 20.93). However, no statistically significant correlation was found between job satisfaction and depersonalization. We found seven studies in this review related to organizational strategies. Overall burnout in nurses was associated with a threefold increase in low organizational strategy support (OR 3.29, 95% CI 1.75 to 6.18). Moreover, low organizational strategies support is negatively correlated with personal accomplishment (OR 0.16, 95% CI 0.09 to 0.27), as illustrated in eFigure 7 in Supplementary Appendix 12. Five articles reported that nurses with high social support, compared to those with low social support, have a more significant influence on the increase in overall burnout (OR 2.65, 95% CI 1.47 to 4.75). (eFigure 8 in Supplementary Appendix 12).

The association between burnout and mental health outcomes

A total of 11 studies were identified in this search for depression, with an overall sample of 14,958 participants. Among them, nine articles reported that overall burnout in nurses was associated with a sevenfold increase in depression (OR 7.40, 95% CI 3.82 to 14.35). Nine articles reported that anxiety was related to burnout with a total sample of 14,147 participants. Additionally, seven articles indicated that nurses experiencing burnout were up to five times more likely to be anxious (OR 4.14, 95% CI 2.15 to 7.98) (eFigure 2 in Supplementary Appendix 12). Eight studies indicated that nurses’ burnout was associated with a fourfold risk of stress, involving a total sample of 10,352 participants (OR 4.60, 95% CI 2.31 to 9.17) (eFigure 3 in Supplementary Appendix 12). Ten studies revealed that overall burnout in nurses was associated with four times lower resilience compared to those with higher resilience (OR 4.06, 95% CI 2.13 to 7.76). Particularly, six studies showed that EE was associated three times with reporting low resilience (OR 3.09, 95% CI 2.23 to 4.29), twofold for DP (OR 2.18, 95% CI 1.64 to 2.90), and PA was associated three times with reporting low resilience (OR 0.30, 95% CI 0.14 to 0.62) (eFigure 5 in Supplementary Appendix 12). Egger’s regression test showed no publication bias (p > 0.05) (eFigure 25 Supplemental Appendix 13).

The association between burnout and turnover intention

We identified 7 studies in this review related to turnover intention. Burnout was associated with up to a threefold increase in turnover intention compared with retention, based on measures of overall burnout (OR 3.55, 95% CI 1.73 to 7.28), as shown in eFigure 10 in Supplementary Appendix 12. Three articles reported the differences in poor-quality patient care among nurses. All three studies on this topic were included in narrative synthesis [16–18]. Overall, a higher degree of burnout is correlated with a higher number of adverse events and poor quality of patient care. While thematic trends emerged, caution is warranted in interpreting these findings due to the limited evidence base.

Discussion

The COVID-19 pandemic, which persisted for three years (2020–2023), triggered multiple waves of infection across the globe. As novel coronavirus virulent strains continue to evolve and mutate, the number of people infected with COVID-19 is gradually increasing. Our study found that the incidence of burnout varies in different countries and regions. The rate of burnout among nurses increased gradually over time, The observed increase in nurse burnout prevalence (47–52% over 2020–2022) suggests cumulative pandemic impacts, including chronic occupational stress, escalating staff shortages, and Omicron-variant workload pressures [40, 41]. While substantial heterogeneity and overlapping confidence intervals preclude definitive conclusions, this temporal pattern corresponds with theoretical models of prolonged resource [42]. This is likely related to the insufficient medical equipment and ineffective response protocols in many countries worldwide, along with the high rates of disease and death experienced in regions severely affected by the COVID-19 pandemic. Furthermore, the prolonged nature of the pandemic, with repeated waves of infection, and the global scale of its impact, have likely exacerbated the burnout crisis among nurses who have been on the frontlines throughout this challenging period [43, 44]. Our results also suggest that the prevalence of burnout among nurses was 48% during the COVID-19 pandemic. Europe exhibited the highest burnout rate with a prevalence estimate of 54%, while Oceania displayed the lowest rate at 22%. At the country level, Ireland had the highest burnout rate at 77%, while Argentina showed the lowest rate at 20%. Societal perceptions of nursing vary considerably across regions. Europe’s high burnout rates may be linked to workplace stress and cultural expectations within its healthcare systems [45]. In contrast, Oceania’s relatively lower burnout rates may reflect stronger institutional support. The heightened moral expectations placed on the nursing profession in Chinese society confer professional respect while simultaneously imposing additional ethical burdens [46] potentially explaining moderate burnout levels in Asia. Staffing shortages directly increase workload pressures [7] particularly evident in high-income European countries. Shift work, especially night shifts, disrupts circadian rhythms. The pandemic exacerbated burnout due to low level of preparedness, increased perceived threat of COVID-19 among nurses, insufficient professional training on COVID-19, and inadequate preparation of materials and medical resources [19].

This systematic review and meta-analysis provide compelling evidence that nurses’ burnout is strongly associated with age and education levels. We found that nurses with higher levels of education are more likely to experience higher levels of burnout compared to those with lower levels of education [15]. Nurses working in emergency medicine and intensive care units, who are exposed to COVID-19 patients, are more likely to experience burnout. We found that nurses with burnout were up to eight times more likely to be depressed compared to those without burnout and up to four times more likely to be anxious and stressed. By contrast, nurses with burnout are only 1.7 times more likely to fear COVID-19 than those without burnout. Therefore, mental health outcomes such as depression, anxiety, and stress are more strongly linked to nurses’ burnout than to the fear of COVID-19. These findings suggest that nurses’ burnout may continue to impact psychological health during and after the COVID-19 pandemic. This hypothesis was supported by numerous reviews that found a positive association between mental health outcomes and burnout [47, 48]. Therefore, it is critical that hospitals regularly screen nurses for mental health issues. Particularly, young nurses who work in an emergency department or ICU, or provide care for COVID-19 patients [49]. Although resilience has been widely recognized as an important coping resource, there is an urgent need for high-quality evidence supporting resilience-building interventions to help frontline nurses cope with major crises such as the COVID-19 pandemic [50].

The findings of this study suggest that organizational strategies could help alleviate burnout. Nurses experiencing burnout were up to four times more likely to perceive a lack of organizational support compared to those who perceived organizational support. Prior research has suggested that implementing appropriate organizational strategies (such as structural support, effective communication, ensuring a safe working environment, and providing COVID-19-related education and training) is essential for supporting nurses who are directly encountering challenges as a result of the COVID-19 crisis [14, 51]. Studies have also shown that providing compensation to nurses, such as extra pay or additional paid leave, mitigates the impact of redeployment on burnout [52]. The provision of protective equipment provision was also linked to a reduced risk of burnout, indicating the importance of ensuring resource allocation through national and organizational policies during the COVID-19 pandemic [53, 54].

The development and clear communication of pandemic planning roadmaps by organizational leadership during the COVID-19 crisis may have significantly contributed to maintaining positive mental health outcomes among healthcare workers [55]. These findings provide valuable insights for addressing psychological challenges in medical personnel. During the initial phases of public health emergencies, governments and healthcare organizations must implement rapid response measures, including comprehensive occupational health surveillance systems and workplace mental health promotion programs, to facilitate early identification of burnout and other adverse psychological outcomes [56]. Concurrently, long-term evidence-based psychological interventions should be established to enhance individual resilience and support stress recovery [57]. Strategic resource allocation planning - encompassing both personal protective equipment and human resource management - requires meticulous organizational attention [58]. These collective findings highlight the critical importance of three key protective measures during pandemics: (1) targeted mental health training programs, (2) systematic workload management protocols, and (3) implementation of multidisciplinary psychological support initiatives, all of which are essential for preserving the mental wellbeing of frontline healthcare staff, particularly nurses [15].

Additionally, nurses experiencing burnout were up to five times more likely to be dissatisfied with their jobs compared to being satisfied with their jobs, as indicated in our review. Nursing managers should implement special measures, such as increasing the nurse-to-patient ratio and reducing nurses’ workload, to enhance job satisfaction among nurses working in COVID-19 wards to an acceptable level [59]. Moreover, variances in working conditions across different medical centers (such as leadership styles, communication, specialization, and other factors) can influence nurses’ perspectives and job satisfaction [59]. The research findings indicate that burnout can diminish nurses’ work enjoyment, enthusiasm, and satisfaction [54]. Similarly, a study has shown that nurses’ job satisfaction is linked to innovative organizational initiatives in the workplace [60].

Notably, we found that nurses with burnout were up to twice as likely to lack family support compared to nurses with family support. Nowadays, the relationship between work and family has always been a concern. With the development of society, the concept of “work-family facilitation” has gradually become a trend [61]. Research indicates that family support plays a positive mediating role between doctors’ emotional exhaustion and subjective well-being [62]. On the other side of the coin, Jin’s research suggested that when situations at work deteriorate,,family support can lead to undesirable outcomes that will exacerbate job burnout [48]. When individuals are unable to fulfill their family duties and obligations in the family due to their job, they may experience feelings of guilt. This guilt can escalate into negative emotions towards work, ultimately leading to job burnout [61]. Overall, medical institutions can establish organizations such as a “hospital-family alliance” through which they can engage in listening and communication with the families of employees, enabling them to understand their needs [61]. The program was developed through a systematic process involving comprehensive literature reviews, rigorous quality assessments, and qualitative studies engaging both patients and healthcare providers [63]. These foundational elements were further refined via expert panel consultations. The initiative specifically aims to strengthen interdisciplinary collaboration across healthcare systems, medical professionals, patients, and their families [63]. For example, under the framework of the “Hospital-Family Alliance”, we can establish an interdisciplinary team composed of doctors, nurses, social workers, psychological counselors, and representatives of patients’ families to jointly formulate personalized care plans for patients. This team can hold regular meetings to discuss the progress of patients’ conditions and adjust the care plans according to the actual situation of patients. In addition, an Internet-based collaborative platform can be developed to facilitate information sharing and communication among team members and to promptly answer the questions of patients and their families [64].

Social support is a crucial interpersonal resource that contributes to improved mental health outcomes following a disaster [65]. In addition, social support has been shown to reduce the negative consequences of burnout [57]. In this review, we found that nurses experiencing burnout were up to two times more likely to lack social support compared to nurses who had social support. Previous research has shown that “not being appreciated by patients or colleagues” was associated with high DP among healthcare workers [66] and experiences of stigma among healthcare providers are correlated with symptoms of burnout [67]. Therefore, the public, colleagues, and patients should provide more support and understanding to nursing staff. At the same time, it is essential to raise public awareness and understanding of nurses’ experiences to reduce discrimination. Governmental and community actions to control the spread of infection, prevent misinformation through fact-checking, and provide public education about sharing false information help healthcare workers from being stigmatized and further aggravating psychological damage [68, 69].

COVID-19 has imposed a heavy burden on the healthcare sector across the globe [70]. It has also led to a new social phenomenon where a growing number of healthcare workers are considering early retirement [71] a trend referred to as the “Great Resignation"by economists. Burnout is the factor that is most strongly related to healthcare workers’ plans to withdraw from the clinical workforce [72]. Burnout and turnover have become significant global concerns due to the heightened stress caused by COVID-19 in the work environment [73]. In this review, we found that nurses experiencing burnout were up to threefold more likely to be turnover compared to retention. This gloomy result prompted calls for healthcare organizations to invest in a range of evidence-based interventions to tackle burnout. These interventions should include individual-focused and organization-directed workplace initiatives that can address both acute and long-term mental healthconsequences for nurses, during and beyond the pandemic [71].

Furthermore, ensuring high-quality services should remain a top priority for healthcare systems. High-level burnout may impair nurses’ ability to provide safe and high-quality care, especially during disasters such as pandemics [74]. A poor work environment can lead to facility errors that may result in adverse conditions unsafe for patients and costly for the organization [18]. Moreover, some of our outcomes, such as low support, low job satisfaction, and poor mental health outcomes, are precursors of safety risks with the potential to lead to active patient safety incidents and have serious career implications for the nurses [75]. This balanced approach, which aims to be comprehensive in terms of outcomes but specific to nurses, was agreed upon by our core research team, which includes nurses and patients. Therefore, in order to minimize work burnout, nursing managers should create a favorable working environment.

Strengths and limitations

The limitations of this study are primarily reflected in the significant heterogeneity observed in the assessment of outcome measures. First, this heterogeneity may stem from the diversity of tools or questionnaires used to evaluate nurse burnout, as well as differences in the definitions of certain outcome measures, such as organizational strategies, job satisfaction, family support, social support, and turnover intention. For instance, the outcome measure of organizational strategies encompasses multiple aspects, including the provision of protective equipment, infection control measures, education and training, compensation, and support. Although these definitions were selected based on theory and stakeholder consultation, their diversity may lead to an overestimation of the association strength of nurse burnout, as indicated by the prediction intervals (a convenient representation of heterogeneity). Therefore, this potential overestimation should be taken into account when interpreting the results. Similarly, the inclusion of geographical distribution in the study, the diversity of burnout measurement tools (such as different versions of the MBI scale). The differences among the studied countries/regions in terms of culture, economy, disease burden, as well as political, educational, and healthcare systems, have led to disparities in workload, resource availability, and training, which may result in significant variations in the prevalence of nurse burnout during COVID-19 across different countries/regions. Moreover, the predominance of cross-sectional design in the original studies inherently limits our ability to establish causal relationships between nurse burnout and its associated factors [76] (e.g., mental health status, support systems, and turnover intention). Furthermore, with only ten prospective cohort or longitudinal studies available in our analysis, robust causal inference remains methodologically unfeasible. Thirdly, the majority of the included studies recruited participants on a voluntary basis, employed observational study designs, or utilized self-reported questionnaires. Consequently, certain biases (e.g., self-selection bias, social desirability bias) may have influenced the outcomes, thereby affecting the findings of our research. Additionally, we only included English-language publications, which may have introduced publication bias, and the possibility that significant references may have been overlooked cannot be ruled out.

Conclusions

The level of burnout among nurses worldwide is high, particularly among young, highly educated nurses working in specialized departments and providing services to COVID-19 patients. Moreover, it may lead to the turnover of nurses, exacerbate the shortage of nurses in the global healthcare system, and indirectly or directly increase the cost of healthcare systems. However, systematic organizational strategies, public understanding and support, and appropriate family support may help nurses to avoid these adverse outcomes. This study also has some limitations. To address these shortcomings, future research could delve into the following aspects: First, a longitudinal study design could be adopted to track the dynamic changes in burnout levels among nurses; second, intervention studies could be conducted to evaluate the effectiveness of specific interventions; finally, qualitative investigations could be carried out to explore the underlying causes and experiences of nurse burnout. By implementing effective interventions and conducting further research to address the gaps identified in this meta-analysis, we can strive to support the well-being of nurses and ensure the provision of high-quality patient care during ongoing global health crises. Moving forward, relevant government departments and social organizations should make more efforts to monitor and improve nurses’ burnout in order to sustain the vitality of health systems. And, healthcare system capacity and financing need to be more flexible to account for exceptional emergencies and the recruitment of human resources for a planned and financed long-term vision [77].

Authors’ contributions

YL, HP, and XL contributed equally to this paper and are joint first authors. WP and CN are joint last authors. YL, HP, XL, and CN conceived and designed the study. YL, HP, and XL conducted the systematic search, screened articles, and read the full texts for eligibility. YL and HP extracted data from the original studies. YL and HP evaluated the studies for risk of bias. YL and HP performed the analyses. YL, HP, and XL wrote the first draft of the manuscript. WP and CN contributed to the interpretation of the results and critically revised the manuscript as well as monitored the review process. All authors provided advice at different stages. All authors approved the final version of the manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding

This study received support from the Outstanding Youth Fund of The Natural Science Foundation of Guangxi in China (Grant No. 2023GXNSFFA026007), Guangxi Key Laboratory of Major Infectious Disease Prevention and Control and Biosafety Emergency Response (GKLKP-KF-202202).

Data availability

All data analyzed in this meta-analysis were extracted from previously published studies referenced in this article. The processed data (e.g., effect sizes, pooled estimates) are presented in the manuscript tables and figures. The PRISMA flowchart, search strategy, and data extraction templates are provided as supplementary files. The datasets used and/or analyzed during the study available from the corresponding author (Chuanyi Ning: ningchuanyi@126.com) on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.