Prevalence and the associated factors of burnout among the critical healthcare professionals during the post-pandemic era: a multi-institutional survey in Taiwan with a systematic review of the Asian literatures

Prevalence and the associated factors of burnout among the critical healthcare professionals during the post-pandemic era: a multi-institutional survey in Taiwan with a systematic review of the Asian literatures | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence and the associated factors of burnout among the critical healthcare professionals during the post-pandemic era: a multi-institutional survey in Taiwan with a systematic review of the Asian literatures Yueh-Lin Lee, Jhih-Wei Dai, Xiu-Wei Li, Min-Ying Chiang, Po-Ting Chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4643455/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Dec, 2024 Read the published version in BMC Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background & Aims: Burnout is a global concern, and critical healthcare professionals have been identified as a high-risk population of burnout. Early identification is crucial, but the prevalence of burnout and its risk factors demonstrate significant geographical variations. This study aims to investigate the prevalence of burnout among critical healthcare professionals and explore potential risk factors during the post-pandemic era in Taiwan. Methods: A web-based questionnaire survey was conducted from December 1, 2023, to January 31, 2024, targeting critical healthcare professionals employed in selected medical institutions affiliated with the Chang Gung Memorial Hospital Foundation, one of Taiwan's largest healthcare organizations. Demographic information, the Subjective Happiness Scale (SHS), current work stressors and self-reported general health data were collected. The study utilized the MBI-Human Services Survey for Medical Personnel (MBI-MP). Univariate and multivariate logistic regression were employed to investigate the association between risk factors and each burnout subscales. A systematic review of Asian literature concerning burnout among critical care practitioners was also conducted in accordance with PRISMA guideline. Results: In our study, 254 participants were enrolled, with an overall burnout rate of 35.4%. The prevalence of high emotional exhaustion (EE) was 70.9%, high depersonalization (DP) was 56.3%, and low personal accomplishment (PA) was 60.6%. Young, unmarried populations, individuals with limited work experience, longer working hours, and night shifts are potential vulnerable groups susceptible to burnout. The top three stressors identified were excessive workload, the burden of administrative tasks, and a shortage of vacation time. Our systematic review included 20 Asian studies on the same issue, with variable burnout prevalence ranging from 16.3–82.1%. Conclusion: The prevalence of burnout was high among critical healthcare professionals in post-pandemic Taiwan, particularly affecting younger, unmarried populations and individuals with limited work experience, longer hours, and more night shifts. The influence of pandemic-related factors has decreased. Regional variations in burnout have been observed across Asia, highlighting the need for further research to identify local risk factors and protect the well-being of professionals and healthcare quality. Burnout occupation burnout mental wellbeing critical healthcare professionals post-COVID-19 era Figures Figure 1 Figure 2 Introduction Burnout is a global issue resulted from chronic workplace stress that has not been successfully managed. It’s characterized by dimensions of EE, DP, and low PA 1 – 4 . Burnout led to not only the physical and psychological adverse effects of the suffering individuals but undesirable organizational consequences 5 – 9 . The estimated of overall physician burnout rate is around 14.3–67.0% according to the systematic reviews 10 – 12 . Furthermore, the staffs working in emergency department (ED) and intensive care unit (ICU) are discovered as the high-risk population of suffering burnout and poor wellbeing 13 – 18 . High-quality critical care is not without cost to the clinicians 19 . Early identification and prevention of burnout are thus crucial to reduce negative consequences on critical healthcare professionals, patients, organizations, and healthcare system 20 – 22 . The rising physical and psychological burden of the frontline critical healthcare professionals was discovered as the COVID-19 pandemic raged on 16 , 23 – 30 . Multiple studies during the COVID-19 pandemic demonstrated that the ED and ICU staffs were vulnerable group of suffering from burnout 16 , 24 , 31 – 35 . Possible risk factors of burnout syndrome were reported by S Ramírez-Elvira et al and M.R. Gualano et al through conducting systematic reviews of literatures, the risk factors included the socio-demographic factors (being younger, unmarried, and lower professional experience) and working conditions (workload and working longer hours). The prevalence and the potential risks factors contributing burnout syndrome both varied across nations owing to the geographical heterogeneity, and thus local data was crucial to develop effective interventions both at an individual and organizational level 10 , 33 , 36 . In the post-pandemic era, occupational burnout, and some issues such as understaffing, limited resources, and overcrowding continues to pose a threat to the healthcare system. Recent studies indicate persistent high levels of burnout among frontline healthcare professionals and potential risk factors included being understaffed, female, or inexperienced 37 , 38 . Fortunately, a study in Czech Republic revealed a decreasing tendency of burnout syndrome as specific precautions were adopted 39 . With heightened attention on burnout, the landscape of its prevalence and risk factors is shifting, and we are eager to uncover the answers. At present, there is limited literature on burnout among critical healthcare professionals in Asia during the post-pandemic era. Our study in Taiwan seeks to address this knowledge gap by exploring the prevalence and risk factors of burnout, with the aim of informing and implementing necessary precautions. Methods 2.1 Study design and setting We performed a web-based, structural questionnaire survey from December 1, 2023, to January 31, 2024, to gather self-reported and cross-sectional information. The survey was anonymous, and we guaranteed the survey confidentiality to the participants. The link of the online questionnaire was shared with the emergency department and intensive care unit staff members who were willing to participate in the study after explaining the aim of our research. All the participants were employed at one of the following medical institutions of Chang Gung Memorial Hospital Foundation: Keelung Chang Gung Memorial Hospital (regional hospital), Linkou Chang Gung Memorial Hospital (medical center), Chiayi Chang Gung Memorial Hospital (regional hospital) and Jen-Ai Hospital, Dali Branch (regional hospital). As one of the largest healthcare providers in Taiwan, Chang Gung Memorial Hospital annually handles an average of 8.6 million outpatient visits and around 370,000 admissions. This study was approved by the Jen-Ai Hospital Institutional Review Board, which waived the need for informed consent (IRB Number: 202300085B0). 2.2 Selection of study participants and sample size The critical healthcare professionals including physician and nurse was invited to participant the study if they worked in emergency department or intensive care unit of the selected hospital and department since the outbreak of the pandemic. The selected hospital included Keelung Chang Gung Memorial Hospital, Linkou Chang Gung Memorial Hospital, Chiayi Chang Gung Memorial Hospital and Jen-Ai Hospital, Dali Branch. Staff who did not work in the emergency department or intensive care unit or did not care for COVID patients during the pandemic were excluded. Other medical staffs, such as social workers, secretaries, pharmacists, Hospital porters or radiographers were not included in the study. Using a 95% confidence interval and a 5% margin of error, our estimate suggests a minimum sample size of approximately 218 participants. 2.3 Description of the Survey and Measures. All participants comprehensively grasped the objective of this questionnaire upon reviewing the informed consent form prior to proceeding. The questionnaire comprises five sections with a total of forty-nine questions ( Appendix table 1 ). The sections, in sequence, encompass demographic data, personal health information, COVID-related inquiries, Burnout scale (MBI-MP) and Subjective Happiness Scale (SHS). The MBI-Human Services Survey for Medical Personnel (MBI-MP) stands as one of the most widely used measurement tools for assessing burnout comprising three subscales and a total of 22 items 40 . These subscales include EE section comprises 9 items, DP has 5 items, and PA includes 8 items. Responses to scale items range from "1 = never" to "7 = always." The scores for each of the three subscales are calculated separately and categorized as low, moderate, or high levels of burnout (EE, high: ≥27, moderate: 19 to 26, low: ≤18; DP, high: ≥10, moderate: 6 to 9, low: ≤5; PA, high: ≥40, moderate: 34 to 39, low: ≤33). In this study, the more conservative and widely accepted definition of overall burnout rate was employed. Burnout is defined as having 'high EE,' 'high DP,' and 'low PA. 10 , 41 . The Subjective Happiness Scale (SHS) is a 4-item scale of global subjective happiness 42 . Two items prompt respondents to characterize themselves using both absolute and relative ratings, while the other two items provide brief descriptions of happy and unhappy individuals, asking respondents to gauge how well each description fits them. The answers range from 1 to 7. To score the scale, sum the scores for the four questions and divide the total by four. This result is the "subjective happiness score", typically ranging from about 4.5 to 5.5, with a higher score indicating greater happiness. 2.4 Data collection Upon completion of the questionnaire, non-identifiable data were gathered. Two independent researchers (PTC and MYC) were responsible for assessing the questionnaire's adequacy and performing additional data extraction. Any discrepancies were resolved through discussion with the senior researcher (CHW). 2.5 Systematic reviews of Asian literature A systematic literature search, covering PubMed and Embase, was conducted from the inception of databases until February 1, 2024. Additionally, we manually reviewed pertinent studies highlighted in prior reviews and examined reference lists. Two team members (XWL and CHW), working independently, were responsible for identifying all Asian studies related to the analysis of burnout prevalence or contributing risk factors among critical healthcare professionals. The study selection flowchart, detailed search strategy, and exclusion reasons are documented in Appendix Fig. 1, Appendix Table 2 , and Appendix Table 3 . Table 2 The multivariable analysis of overall burnout and the associated factors of the included participants. Category Covariate Hazzard ratio (95% CI) P-value Age 20–29 1 30–39 1.19(0.48–2.96) 0.706 40–49 0.29(0.04–2.15) 0.223 > 50 0 0.999 Marital status Single 1 Married 0.67(0.24–1.85) 0.443 Number of children 0 1 1 3.39(0.89–12.88) 0.073 2 1.82(0.50–6.50) 0.363 ≥ 3 1.95(0.22–16.96) 0.545 The length of time working 1–5 1 6–10 0.80(0.35–1.86) 0.609 11–15 0.97(0.30–3.11) 0.956 > 15 0.15(0.02–0.98) 0.048 Working hours per week ≤ 30 1 31–40 1.26(0.12–13.8) 0.848 41–50 1.32(0.12–14.49) 0.823 > 50 1.76(0.14–22.14) 0.663 Average night shifts per month ≤ 25% 1 25–50% 2.55(0.98–6.63) 0.055 50–75% 2.93(1.09–7.83) 0.033 ≥ 75% 1.76(0.68–4.56) 0.247 Table 3 Summary of published studies about burnout of critical healthcare professionals in Asian. First author (Publication year) Country Settings Occupation Sample size, n Burnout evaluation tool Burnout Prevalence Potential risk factors Our study (2024) Taiwan ICU/ED physicians, nurses 254 MBI a • Overall burnout: 35.4% EE b : low: 11.4%, average: 17.7%, high: 70.9% DP c : low: 16.1%, average: 27.6%, high: 56.3% PA d : low: 60.6%, average: 28.0%, high: 11.4% Before the outbreak of COVID pandemics Yilmaz F et al (2011) 66 Turkey ICU nurses 85 MBI •Mean score: EE: 14.90 ± 5.53, DP: 3.87 ± 2.77, PA: 11.43 ± 4.63 NA Yunbei Xiao et al (2014) 67 China ED physicians 205 MBI •Mean score: EE: 6.98 ± 5.79, Cynicism: 3.37 ± 4.35, PA: 24.79 ± 10.81 NA Xiao-Chun Zhang et al (2015) 68 China ICU nurses 431 MBI •High degree burnout rate: 16.0% •Mean score: EE: 24.55 ± 12.36, DP: 7.05 ± 6.50, PA: 35.08 ± 9.36 NA Yildiz Denat et al (2016) 69 Turkey ICU nurses 51 MBI •Mean score: EE: 14.68 ± 6.10, DP: 5.31 ± 3.84, PA: 19.19 ± 7.08 NA Motasem Hamdan et al (2017) 20 Palestine ED Nurses, physicians, and administrative personnel 444 MBI EE: low: 14.6%, average: 20.5%, high: 64.8% DP: low: 36.1%, average: 25.8%, high: 38.1% PA: low: 34.6%, average: 21.1%, high: 44.4% •workplace violence, young age ( ≤ 30 years) Wacharasint P et al (2018) 70 Thailand ICU physicians, nurses 171 MBI •Burnout rate: physicians: 65.2%; nurses: 62.6% •Physician: Income, thinking idea to quit their ICU job, need vacation > 2 days/week •Nurse: age > 40 years old, ICU experience > 5 years, patient's ICU length of stay > 5 days, workload and thinking idea to quit their ICU job Kay Choong See et al (2018) 12 Asia ICU physicians, nurses 4092 MBI •High degree burnout rate: 51.6% •Mean score: EE: 25.3 ± 11.2, DP: 8.9 ± 6.2, PA: 32.3 ± 9.0 •Lower risks: religiosity, years of working in the current department, shift work, better work-life balance and number of stay-home night calls •Higher risks: work days per month and having a bachelor's degree Atefeh Soltanifar et al (2018) 71 Iran ED female physicians 77 MBI EE Moderate to high: 84.5% DP Moderate to high :48.1% PA low: 80.5% NA Abdulghani M Alqahtani et al (2019) 72 Saudi Arabia ED physicians, nurses 282 MBI •Burnout rate: 16.3% •Higher risks: male, Smokers and sleep disorders Saravanabavan L et al (2019) 73 India ICU physicians, nurses 204 MBI •High degree burnout rate: 80.0% NA After the outbreak of COVID pandemics Sedigheh Salimi et al (2020) 74 Iran ICU nurses 400 ProQOL Scale e •Average burnout score:36.27 ± 7.45 •low: 8.0%, average: 49.8%, high: 42.3% NA Zakaria MI et al (2021) 75 Malaysia ED physicians, nurses, assistant medical officer 216 Burnout Questionnaire f •Nurses:61.2%, doctors:35.1%, assistant medical officer: 29.6% Frequent exposure to angry public, job overload, lack of clear guidelines, and perception of underpaid Wei Ping Daniel Chor et al (2021) 16 Singapore ED physicians, nurses 337 CBI •Average burnout score: 49.2 ± 18.6 Previously working in the ED or UCC before the COVID-19 pandemic; nurse (compared to physicians) Zihan Hu MS et al (2021) 76 China ICU physicians, nurses 2411 MBI •Burnout rate: 69.7% EE: low: 6.1%, average: 35.1%, high: 58.8% DP: low: 29.8%, average: 36.7%, high: 33.5% PA: low: 64.9%, average: 14.9%, high: 20.2% •Lower risks: exercise every day, more paid vacation •Higher risks: Having Comorbidities, more years of work experience and more night shifts Huan Ma et al (2022) 77 China ED physicians, nurses 342 ProQOL Scale •Average burnout score:27.74 ± 6.19 •low: 19.3%, average: 78.4%, high: 2.3% NA Jing Wang et al (2022) 78 China ICU physicians 1813 MBI •Burnout rate: 82.1% •Mean score: EE: 24.14 ± 10.90, DP: 9.69 ± 5.70, PA: 28.55 ± 9.82 Number of children, income, and difficulties in treatment decisions Artem Kashtanov et al. (2022) 79 Russia ICU physicians, nurses 1259 MBI •Non-COVID-19 ICU EE: low: 14.6%, average: 30.8%, high: 54.6% DP: low: 11.6%, average: 16.5%, high: 71.9% PA: low: 23.5%, average: 40.3%, high: 36.2% •COVID-19 ICU EE: low: 16.5%, average: 31.5%, high: 52.0% DP: low: 7.4%, average: 9.4%, high: 83.1% PA: low: 25.4%, average: 45.4%, high: 29.1% NA Kim C et al (2022) 80 South Korea ED physicians 247 ProQOL Scale •Average burnout score:33.81 ± 6.56 NA Akira Kuriyama et al (2022) 81 Japan ICU All critical care professionals 936 Mini Z 2.0 Survey •Burnout rate: 24.3% •Lower risks: higher resilience scores and perceived support from the hospital or colleagues •Higher risks: having depression or anxiety, experiencing stigma from caring for patients with COVID-19, or having experienced self-quarantine Aylin Arıkan et al (2023) 23 Turkey PED nurses 164 MBI EE: low: 9.1%, average: 40.5%, high: 51.4% DP: low: 14.4%, average: 26.7%, high: 58.9% PA: low: 89.6%, average: 10.4%, high: 0% NA a MBI: Maslach Burnout Inventory ; b EE: Emotional Exhaustion ; c DP: Depersonalization ; d PA: Personal Accomplishment ; e ProQOL Scale: The Professional Quality of Life Scale ; f Burnout Questionnaire was adapted from Michelle Post, Public Welfare, Vol. 39, No. 1, 1981, American Public Welfare Association 2.6 Statistical analysis Baseline demographic categorical variables are depicted as percentages (%), while continuous variables are presented as mean ± SD. One-way ANOVA is used to examine differences between groups. However, if Levene's test for homogeneity of variances fails (indicating significant variance differences between groups with p < 0.05), Welch's ANOVA is used instead to check for differences. If differences are found, Tukey post hoc analysis is used to analyze the differences between groups. Univariate logistic regression was employed to investigate the association between potential risk factors and each burnout subscales (EE, DP, and PA; Table 1 ). Variables that demonstrated significance in the univariate logistic regression (P-values < 0.2) 43 were subsequently analyzed in multivariate logistic regression (Table 2 and Appendix Table 4 ). All analyses were conducted using SPSS Statistics, version 24. 2.7 Ethical considerations Ethics approval was secured from the Institutional Review Board of Jen-Ai Hospital (IRB Number: 202300085B0). All submitted questionnaires were treated with strict confidentiality, accessible only to the researchers involved in this study. License granting the right to utilize and administer the Maslach Burnout Inventory has been secured. There was no external funding source was involved in this research initiative. Results We conducted a self-reported questionnaire survey spanning from December 2023 to January 2024. The study involved 254 critical healthcare professionals across four hospitals in Taiwan: Keelung Chang Gung Memorial Hospital (38 participants), Linkou Chang Gung Memorial Hospital (74 participants), Chiayi Chang Gung Memorial Hospital (83 participants), and Jen-Ai Hospital, Dali Branch (78 participants, Fig. 1 ). The response rate achieved was around 51.0%. Through manual examination, no duplicated or incomplete questionnaires were identified but one participant decided to withdraw after reviewing the participant consent form. 3.1 Demographic characteristics, general health conditions and the Subjective Happiness Scale (SHS) of the included participants Among 254 participants, 46.9% were under 30 years old, with the majority being nurses (81.9%) and unmarried (60.6%). A total of 133 participants worked in the emergency room (52.4%). A significant portion (41.3%) had less than five years of experience in their current emergency department or intensive care unit. Furthermore, 53.2% of participants worked over 40 hours per week, and 45.7% had night shifts for over 50% of the month. Table 1 provides detailed demographic characteristics. Regarding self-assessed general health conditions, detailed results can be found in Appendix Table 5 . Half of the participants rated their health condition as comparable to others. A low percentage relied on medication for sleep (7.8%), used tobacco (2.4%), or consumed alcohol (2.8%). Additionally, the majority (41.7%) did not have regular exercise habits. More than half (58.3%) sometimes experienced stress, while only 15.7% never considered quitting in the past month. About the current workplace stressors, there is differences between physicians and nurses. Nurses reported workload burden (73.6%), additional administrative tasks (63.0%), and a shortage of vacation time (61.5%) as their primary sources of workplace stress, while physicians mentioned workload burden (39.1%), fear of inadequate capabilities (37.0%), and shift work stress (34.8%) as their top stressors. Among the participants, the average subjective happiness score was 4.6, with detailed scores of each subgroup listed in Appendix Table 6 . 3.2 Prevalence of burn-out among critical healthcare professionals and the results of each subscale of the MBI-Human Services Survey for Medical Personnel (MBI-MP) In our study, we found that the overall burnout rate was 35.4% (nurses: 37.5%, physicians: 26%). Specifically, the prevalence of high EE was 70.9%, high DP was 56.3%, and low PA was 60.6%. Regarding the results of each subscale, the average EE score was 35.4 ± 11.6. Nurses experienced higher EE compared to physicians. Additionally, younger individuals, those who were single, worked in the emergency room (ER), had longer average working hours per week, and had more night shifts tended to have higher levels of EE. The mean score for DP was 11.8 ± 6.5. Younger individuals, physicians, those working in the ER, singles, those with no previous critical care experience, and those who had been critical healthcare professionals for a shorter period tended to have higher levels of DP. The average score for PA was 30.6 ± 7.9. Lower levels of PA were observed among younger and single individuals. For a concise overview of burnout components, please refer to the details outlined in Table 1 . 3.3 Associated factors of burn-out syndrome Table 1 summarizes the results of the univariable analysis regarding the potential factors associated with each burnout subscale. Variables that revealed significant univariate associations were subsequently included in the multivariate analyses (Table 2 and Appendix Table 4 ). The results demonstrated that individuals with less experience (6–10 years: 0.80, 95% CI 0.35 to 1.86; 11–15 years: 0.97, 95% CI 0.30 to 3.11; >15 years: 0.15, 95% CI 0.02 to 0.98) and those with more night shifts (25–50%: 2.55, 95% CI 0.98 to 6.63; 50–75%: 2.93, 95% CI 1.09 to 7.83; ≥75%: 1.76, 95% CI 0.68 to 4.56) had an increased risk of overall burnout. Further analysis showed that none of the factors remained significant in increasing the risk of higher EE. However, younger individuals (30–39 years: 2.72, 95% CI 1.05 to 7.07; 40–49 years: 1.20, 95% CI 0.23 to 6.21; >50 years: 0.19, 95% CI 0.01 to 4.62), less experienced individuals (6–10 years: 0.70, 95% CI 0.29 to 1.71; 11–15 years: 1.06, 95% CI 0.31 to 3.59; >15 years: 1.92, 95% CI 0.41 to 9.11), and those with more night shifts (25–50%: 2.85, 95% CI 1.16 to 7.00; 50–75%: 2.54, 95% CI 1.02 to 6.32; ≥75%: 1.80, 95% CI 0.74 to 4.39) had a higher risk of suffering from DP. The risk of experiencing low PA was greater for less experienced individuals (6–10 years: 0.52, 95% CI 0.22 to 1.21; 11–15 years: 0.31, 95% CI 0.09 to 1.00; >15 years: 0.79, 95% CI 0.19 to 3.29). 3.4 The Systematic review of Asian literatures A total of 20 Asian studies related to burnout among critical care practitioners were included, and the summary of conclusions was listed in Table 3 . The Maslach Burnout Inventory (MBI) was the most common tool for burnout evaluation, with variable burnout prevalence ranging from 16.3–82.1%. Factors associated with higher risks of burnout included having comorbidities, job overload, previous experience in ER or ICUs, night shifts, and perception of being underpaid. Discussion Our study identified the high prevalence of burnout rate (overall burnout rate: 35.4%) in critical healthcare practitioners during the post-pandemic era in Taiwan and discovered they were experiencing high levels of EE and DP, coupled with a low level of PA. Lacking experience of critical care, excessive working hours and night shifts were possible key factors damaging the wellbeing of the critical healthcare professionals. Besides, the top three work stressors identified were excessive workload, the burden of administrative tasks, and a shortage of vacation time. Through systematic reviews of Asian literature regarding burnout, we had discovered not only demographic variations in the prevalence of burnout but differences in the associated factors before and after the COVID-19 pandemic. COVID pandemics and its impact on staff wellbeing The COVID-19 pandemic has adversely impacted the wellbeing of the critical healthcare professionals. According to the recent systematic review, the prevalence of overall burnout of critical care staff ranged from 34.6 to 61.5% 12, 33 . Exhausting workload, anxiety and fear of the pandemic, the burden of responsibility and moral distress were previously known possible issues of burnout during the pandemics 33 , 44 . Despite the decrease in stress associated with caring for COVID patients during the post-pandemic era ( Appendix Table 5) , the overall prevalence of burnout didn’t decrease 37 . The pandemic itself was not necessarily the only reason associated with increased burnout 33 , 45 . However, certain issues such as job overload, staff shortages, additional administrative tasks, shift work stress, and economic concerns continue to pose significant stress for critical healthcare practitioners. Prevalence of burnout Before and After the COVID pandemics We summarized the published studies on burnout among critical healthcare professionals in Asian (see Table 3 ) . MBI was the most used assessment instrument. The prevalence of estimates burnout slightly increased from 16.3 to 80.0% before the pandemics to 24.3 to 82.1% after the pandemics. The substantial variability in the prevalence of burnout across studies was attributed not only to the difference in medical systems but to the marked variation in assessment instruments and definitions of burnout 10 . These variations preclude the cross-national comparisons regarding the trends in the prevalence of burnout before and after the COVID pandemics. The importance of developing a consensus definition of burnout, standardizing assessment instruments and obtaining local data were emphasized. Associated factors of burnout during the post-pandemic era Despite the causal relationship between burnout and risk factors may be limited by the cross-sectional design of studies, we can still take a glance at the vulnerable populations. Previous studies discovered that being nurses, job overload, perception of underpaid, experiencing stigma from caring for COVID-19 patients, having personal health condition and more night shift were possible risks factors (see Table 3 ). In our study, several variables were found associated with burnout, including being younger, unmarried, having less working experience, longer working hours and more night shifts. Undoubtedly, being a critical care professional entails a high risk of burnout compared to other specialties due to the nature of the job 12 , 46 – 48 . However, there remains conflicts concerning level of burnout between different occupation. Previous meta-analysis by MM Macaron et al and multinational survey by See KC et al revealed no significant difference in pooled estimate of burnout prevalence between physicians and nurses 11 , 12 . On the contrary, critical care nurses were recognized as high-risk group by Gualano MR et al 32 and the multi-center study by Chor WP et al also discovered slightly higher burnout rate among nurses compared with physicians working in ED (53.3% versus 42.5%) 32 .These variation between studies may reflect the difference in organization-level healthcare systems. In Taiwan, there is the lowest physician or nurse -population ratio, with 2 physicians and 7.6 nurses per 1,000 population, according to the survey by Organization for Economic Cooperation and Development (OECD). However, the number of adult critical care beds leads among Asian countries, with 28.5 beds per 100,000 population, compared to the average of 3.6 beds per 100,000 population 49 . In our study, we found one-third of critical care professionals reported stress related to shift work, and over 70% of nurses experienced a workload burden (Fig. 2 ). Despite no significant difference in each subscale of burnout between physicians and nurses, nurses had higher prevalence of overall burnout compared to physicians (37.5% versus 26%), which may be associated with the critical care nurses were often working understaffed, having additional administrative tasks, and working overtime 33 , 50 . High EE and DP were observed in younger, less experienced individuals, consistent with previous studies 50 – 53 . While burnout is often considered to mainly affect those in their later careers, this may be related to the shortage of critical professionals and the common situation where nurses are forced to handle excessive, unfamiliar clinical tasks before they are fully prepared. Our data reflected that workload burden and staff shortages were reported as the top work stressors ( Fig. 2 ) . According to a survey by the Taiwan Ministry of Health and Welfare, one nurse in Taiwan cares for an average of 9 to 15 patients. Notably, younger individuals comprise most critical healthcare professionals in Taiwan. Therefore, it’s not surprising that the turnover rate for nurses is as high as 14.5% annually, with most nurses leaving within an average of 6.5 years, according to the Taiwan Ministry of Health and Welfare's 2023 survey. Irregular night shifts and longer working hours were associated with higher scores in EE in our study. Night shift stress has been previously linked to burnout, mental health problems, and sleep disturbances 54 , 55 . Furthermore, compared to those with fixed night shifts, participants with irregular night shifts had a higher risk of burnout 56 . Irregular shift schedules can compromise physical and psychological health as well as occupational functionality. Additionally, long working hours, especially working more than 55 hours per week, were associated with greater sleep disturbances and occupational stress compared to working 40 hours a week 57 . Implementing reasonable working hours and regular shift schedules may be effective interventions for preventing burnout and enhancing job performance. Maintaining a work-life balance is crucial for well-being, and marriage appears to be one of the solutions 58 , 59 . According to the theory of work-family enrichment, married individuals tend to experience better job satisfaction by actively engaging in their parental roles 60 . Recent studies conducted during the COVID-19 pandemic have highlighted the significant moderating role of family support in mitigating burnout across various dimensions and enhancing subjective well-being 61 , 62 . Despite the potential stresses of parenthood, the protective effects of marriage can be attributed to lifestyle changes, involvement in parental responsibilities, and simply spending time with family 63 . Consistent with prior research, we found that married individuals exhibited lower EE and PA with higher DP compared to their unmarried counterparts 36 . Individuals with more children also exhibited lower EE and PA with higher DP, a phenomenon not observed in individuals with pets in our study. The relationship between burnout interventions and locally identified workplace stressors and risk factors. Given the demographic variation in burnout, gathering local data, identifying vulnerable populations, and promoting interventions can help reduce the risk of burnout. At the individual level, improving interprofessional communication, participating in face-to-face group programs, fostering a positive mindset, and maintaining a work-family balance have proven beneficial 64 , 65 . Organizational-level interventions include reducing unnecessary administrative tasks and workload, creating a supportive work atmosphere, and regulating the number of workdays and night shifts. Prioritizing the well-being of healthcare staff through these interventions establishes a solid foundation for reducing burnout. Consequently, it leads to improvements in the quality of care, reductions in medical expenditures, and lower turnover rates. Conclusion This multi-institutional study highlights the persistently high prevalence of burnout among critical healthcare professionals in Taiwan, even post-pandemic. Modifiable factors such as age, marital status, work experience, working hours, and night shifts play a role. Key stressors include workload, administrative tasks, limited vacation time, and the stress of shift work. Regional variations in burnout across Asia emphasize the need for tailored interventions. Continued research is essential to monitor and support the well-being of critical professionals and to maintain healthcare quality. Strength and limitations The study exhibits both strengths and potential limitations. Firstly, it authentically captures the psychological well-being of critical care healthcare professionals in Taiwan, despite variations in medical operation modes and disease severity among the included hospitals. However, the applicability of our findings to other countries should be approached with caution. Secondly, due to the lack of consensus definition of burnout, cautious should be taken if comparing our results to other studies, despite the widely accepted definition of burnout rate was used in our study. Thirdly, as a cross-sectional self-report questionnaire survey, drawing causal inferences from the research results requires careful consideration, and the presence of social desirability bias may introduce self-reporting bias. Lastly, participants in this study are voluntary, lacking compulsion, which may lead to a relatively low questionnaire response rate. However, their willingness to participate ensures more sincere responses, thereby enhancing the accuracy of the questionnaire. Moreover, by not mandating participation, the study avoids imposing additional psychological stress on critical healthcare professionals of selected hospitals. Declarations Ethics approval and consent to participate: This research received approval from the Jen-Ai Hospital Institutional Review Board (IRB Number: 202300085B0). All participants fully understood the study's goal and reviewed the informed consent form before proceeding. Human Ethics and Consent to Participate declarations: not applicable. Competing interests: The authors declare that they have no competing interests. Acknowledgments: None. Funding: None. Authors’ contributions YLL, CHW: study design; XWL and CHW: develop research strategy and perform systematic reviews; MYC and PTC: evaluating the questionnaire's adequacy and data extraction; CHW: verified the extracted data; YLL and YCL: performed the statistical analysis; YLL, MYC, JWD and CHW: drift the manuscript; CHW: revised the manuscript; All authors read and approved the final manuscript. Availability of data and materials: All the datasets and the supplementary materials mentioned in the article are available from the corresponding author on reasonable request. References Maslach C, Schaufeli WB, Leiter MP. Job burnout. Annu Rev Psychol 2001;52:397-422. Tanno LK, Chalmers RJ, Calderon MA, et al. Reaching multidisciplinary consensus on classification of anaphylaxis for the eleventh revision of the World Health Organization's (WHO) International Classification of Diseases (ICD-11). Orphanet J Rare Dis 2017;12:53. Maslach C, Jackson SE. The measurement of experienced burnout. 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Simulation Study of Confounder-Selection Strategies. American Journal of Epidemiology 1993;138:923-936. Elliott R, Crowe L, Pollock W, et al. The impact of the COVID-19 pandemic on critical care healthcare professionals' work practices and wellbeing: A qualitative study. Aust Crit Care 2023;36:44-51. Amanullah S, Ramesh Shankar R. The Impact of COVID-19 on Physician Burnout Globally: A Review. Healthcare (Basel) 2020;8. Somville F, Van Bogaert P, Wellens B, et al. Work stress and burnout among emergency physicians: a systematic review of last 10 years of research. Acta Clin Belg 2024;79:52-61. Duarte I, Teixeira A, Castro L, et al. Burnout among Portuguese healthcare workers during the COVID-19 pandemic. BMC Public Health 2020;20:1885. Jun J, Costa DK. Is It Me or You? A Team Approach to Mitigate Burnout in Critical Care. Crit Care Nurs Clin North Am 2020;32:395-406. Phua J, Faruq MO, Kulkarni AP, et al. Critical Care Bed Capacity in Asian Countries and Regions. Crit Care Med 2020;48:654-662. Chuang CH, Tseng PC, Lin CY, et al. Burnout in the intensive care unit professionals: A systematic review. Medicine (Baltimore) 2016;95:e5629. Bogue TL, Bogue RL. Extinguish Burnout in Critical Care Nursing. Crit Care Nurs Clin North Am 2020;32:451-463. Shanafelt TD, Sloan JA, Habermann TM. The well-being of physicians. Am J Med 2003;114:513-9. Kim HS, Yeom HA. The association between spiritual well-being and burnout in intensive care unit nurses: A descriptive study. Intensive Crit Care Nurs 2018;46:92-97. Cheng H, Liu G, Yang J, et al. Shift work disorder, mental health and burnout among nurses: A cross-sectional study. Nurs Open 2023;10:2611-2620. Al-hrinat J, Al-Ansi AM, Hendi A, et al. The impact of night shift stress and sleep disturbance on nurses quality of life: case in Palestine Red Crescent and Al-Ahli Hospital. BMC Nursing 2024;23:24. Gu H, Lee J, Hwang Y, et al. Job burnout among workers with different shift regularity: interactive factors between sleep, depression, and work environment. Frontiers in Public Health 2023;11. Virtanen M, Ferrie JE, Gimeno D, et al. Long working hours and sleep disturbances: the Whitehall II prospective cohort study. Sleep 2009;32:737-45. Yang D, Fang G, Fu D, et al. Impact of work-family support on job burnout among primary health workers and the mediating role of career identity: A cross-sectional study. Front Public Health 2023;11:1115792. Cañadas-De la Fuente GA, Ortega E, Ramirez-Baena L, et al. Gender, Marital Status, and Children as Risk Factors for Burnout in Nurses: A Meta-Analytic Study. Int J Environ Res Public Health 2018;15. Greenhaus J, Powell G. When Work And Family Are Allies: A Theory Of Work-Family Enrichment. The Academy of Management Review 2006;31:72-92. Karagöl A, Törenli Kaya Z. Healthcare workers' burn-out, hopelessness, fear of COVID-19 and perceived social support levels. Eur J Psychiatry 2022;36:200-206. Shin Y, Hur WM, Park K. The Power of Family Support: The Long-Term Effect of Pre-COVID-19 Family Support on Mid-COVID-19 Work Outcomes. Int J Environ Res Public Health 2021;18. Chen Y-H, Lou S-Z, Yang C-w, et al. Effect of Marriage on Burnout among Healthcare Workers during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health 2022;19:15811. Lee M, Cha C. Interventions to reduce burnout among clinical nurses: systematic review and meta-analysis. Scientific Reports 2023;13:10971. Cohen C, Pignata S, Bezak E, et al. Workplace interventions to improve well-being and reduce burnout for nurses, physicians and allied healthcare professionals: a systematic review. BMJ Open 2023;13:e071203. Yilmaz F, Aytekin A, Kuguoglu S. The burnout levels of NICU nurses and its effect on their quality of life in north of Turkey. Intensive Care Medicine 2011;37:S439. Xiao Y, Wang J, Chen S, et al. Psychological distress, burnout level and job satisfaction in emergency medicine: A cross-sectional study of physicians in China. Emerg Med Australas 2014;26:538-42. Wang YS, Huo TR, Wang Y, et al. Constructing mesoporous biochar derived from waste carton: Improving multi-site adsorption of dye wastewater and investigating mechanism. Environmental Research 2024;242. Denat Y, Gokce S, Gungor H, et al. Relationship of anxiety and burnout with extrasystoles in critical care nurses in Turkey. Pak J Med Sci 2016;32:196-200. Wacharasint P, Laopakorn C, Kunakorn P. Prevalence and risk factors for ICU burnout syndrome among Thai intensivists and ICU nurses. Intensive Care Medicine Experimental 2018;6. Soltanifar A, Pishbin E, Attaran Mashhadi N, et al. Burnout among female emergency medicine physicians: A nationwide study. Emerg Med Australas 2018;30:517-522. Alqahtani AM, Awadalla NJ, Alsaleem SA, et al. Burnout Syndrome among Emergency Physicians and Nurses in Abha and Khamis Mushait Cities, Aseer Region, Southwestern Saudi Arabia. ScientificWorldJournal 2019;2019:4515972. Saravanabavan L, Sivakumar MN, Hisham M. Stress and burnout among intensive care unit healthcare professionals in an Indian tertiary care hospital. Indian Journal of Critical Care Medicine 2019;23:462-466. Salimi S, Pakpour V, Rahmani A, et al. Compassion Satisfaction, Burnout, and Secondary Traumatic Stress Among Critical Care Nurses in Iran. J Transcult Nurs 2020;31:59-66. Zakaria MI, Remeli R, Ahmad Shahamir MF, et al. Assessment of burnout among emergency medicine healthcare workers in a teaching hospital in Malaysia during COVID-19 pandemic. Hong Kong Journal of Emergency Medicine 2021;28:254-259. Hu Z, Wang H, Xie J, et al. Burnout in ICU doctors and nurses in mainland China-A national cross-sectional study. J Crit Care 2021;62:265-270. Ma H, Huang SQ, We B, et al. Compassion fatigue, burnout, compassion satisfaction and depression among emergency department physicians and nurses: a cross-sectional study. BMJ Open 2022;12:e055941. Wang J, Hu B, Peng Z, et al. Prevalence of burnout among intensivists in mainland China: a nationwide cross-sectional survey. Crit Care 2021;25:8. Kashtanov A, Molotok E, Yavorovskiy A, et al. A Comparative Cross-Sectional Study Assessing the Psycho-Emotional State of Intensive Care Units' Physicians and Nurses of COVID-19 Hospitals of a Russian Metropolis. Int J Environ Res Public Health 2022;19. Kim C, Park KH, Eo EK, et al. Burnout and Resilience among Emergency Physicians at Korean University Hospitals during the COVID-19 Pandemic: A Cross-Sectional Analysis. Yonsei Med J 2022;63:372-379. Kuriyama A, Sakuraya M, Kinjo M, et al. Burnout and Turnover Intention in Critical Care Professionals During the COVID-19 Pandemic in Japan: A Cross-sectional Survey. Ann Am Thorac Soc 2023;20:262-268. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 18 Dec, 2024 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 23 Aug, 2024 Reviews received at journal 15 Aug, 2024 Reviewers agreed at journal 08 Aug, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviews received at journal 29 Jul, 2024 Reviewers agreed at journal 20 Jul, 2024 Reviewers invited by journal 19 Jul, 2024 Editor assigned by journal 30 Jun, 2024 Submission checks completed at journal 28 Jun, 2024 First submitted to journal 26 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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P: physician ; N: nurse.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4643455/v1/7b1b982fccc97796a47cad8a.png"},{"id":60811924,"identity":"4efa4366-2399-4bf8-a015-99971ec5b1ad","added_by":"auto","created_at":"2024-07-22 11:00:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":225322,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of current work stressors.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4643455/v1/0fd74b47cfb8f4ddff134e74.png"},{"id":72201894,"identity":"299f2687-b3cd-43f2-abf8-c697e4d849d2","added_by":"auto","created_at":"2024-12-23 16:11:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1353837,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4643455/v1/edf0c8eb-9bf3-4ae8-9c9a-b7cc9eea5acd.pdf"},{"id":60812701,"identity":"5fe99937-8dcc-4bdf-a7bb-f05a10b824f1","added_by":"auto","created_at":"2024-07-22 11:08:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":57963,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4643455/v1/44f6b5f663a61da6d99eb0a2.docx"},{"id":60811925,"identity":"3d2196d3-e52f-4a0a-bec4-f509b3ac4b72","added_by":"auto","created_at":"2024-07-22 11:00:20","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":60054,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4643455/v1/19feb798cdfb600ed81ffe82.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrevalence and the associated factors of burnout among the critical healthcare professionals during the post-pandemic era: a multi-institutional survey in Taiwan with a systematic review of the Asian literatures\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBurnout is a global issue resulted from chronic workplace stress that has not been successfully managed. It\u0026rsquo;s characterized by dimensions of EE, DP, and low PA\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Burnout led to not only the physical and psychological adverse effects of the suffering individuals but undesirable organizational consequences\u003csup\u003e\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The estimated of overall physician burnout rate is around 14.3\u0026ndash;67.0% according to the systematic reviews\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Furthermore, the staffs working in emergency department (ED) and intensive care unit (ICU) are discovered as the high-risk population of suffering burnout and poor wellbeing\u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. High-quality critical care is not without cost to the clinicians\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Early identification and prevention of burnout are thus crucial to reduce negative consequences on critical healthcare professionals, patients, organizations, and healthcare system\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe rising physical and psychological burden of the frontline critical healthcare professionals was discovered as the COVID-19 pandemic raged on\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28 CR29\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Multiple studies during the COVID-19 pandemic demonstrated that the ED and ICU staffs were vulnerable group of suffering from burnout\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan additionalcitationids=\"CR32 CR33 CR34\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Possible risk factors of burnout syndrome were reported by S Ram\u0026iacute;rez-Elvira et al and M.R. Gualano et al through conducting systematic reviews of literatures, the risk factors included the socio-demographic factors (being younger, unmarried, and lower professional experience) and working conditions (workload and working longer hours). The prevalence and the potential risks factors contributing burnout syndrome both varied across nations owing to the geographical heterogeneity, and thus local data was crucial to develop effective interventions both at an individual and organizational level\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the post-pandemic era, occupational burnout, and some issues such as understaffing, limited resources, and overcrowding continues to pose a threat to the healthcare system. Recent studies indicate persistent high levels of burnout among frontline healthcare professionals and potential risk factors included being understaffed, female, or inexperienced\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Fortunately, a study in Czech Republic revealed a decreasing tendency of burnout syndrome as specific precautions were adopted\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. With heightened attention on burnout, the landscape of its prevalence and risk factors is shifting, and we are eager to uncover the answers.\u003c/p\u003e \u003cp\u003eAt present, there is limited literature on burnout among critical healthcare professionals in Asia during the post-pandemic era. Our study in Taiwan seeks to address this knowledge gap by exploring the prevalence and risk factors of burnout, with the aim of informing and implementing necessary precautions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Study design and setting\u003c/h2\u003e\n \u003cp\u003eWe performed a web-based, structural questionnaire survey from December 1, 2023, to January 31, 2024, to gather self-reported and cross-sectional information. The survey was anonymous, and we guaranteed the survey confidentiality to the participants. The link of the online questionnaire was shared with the emergency department and intensive care unit staff members who were willing to participate in the study after explaining the aim of our research. All the participants were employed at one of the following medical institutions of Chang Gung Memorial Hospital Foundation: Keelung Chang Gung Memorial Hospital (regional hospital), Linkou Chang Gung Memorial Hospital (medical center), Chiayi Chang Gung Memorial Hospital (regional hospital) and Jen-Ai Hospital, Dali Branch (regional hospital). As one of the largest healthcare providers in Taiwan, Chang Gung Memorial Hospital annually handles an average of 8.6\u0026nbsp;million outpatient visits and around 370,000 admissions. This study was approved by the Jen-Ai Hospital Institutional Review Board, which waived the need for informed consent (IRB Number: 202300085B0).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e2.2 Selection of study participants and sample size\u003c/h3\u003e\n\u003cp\u003eThe critical healthcare professionals including physician and nurse was invited to participant the study if they worked in emergency department or intensive care unit of the selected hospital and department since the outbreak of the pandemic. The selected hospital included Keelung Chang Gung Memorial Hospital, Linkou Chang Gung Memorial Hospital, Chiayi Chang Gung Memorial Hospital and Jen-Ai Hospital, Dali Branch. Staff who did not work in the emergency department or intensive care unit or did not care for COVID patients during the pandemic were excluded. Other medical staffs, such as social workers, secretaries, pharmacists, Hospital porters or radiographers were not included in the study. Using a 95% confidence interval and a 5% margin of error, our estimate suggests a minimum sample size of approximately 218 participants.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3 Description of the Survey and Measures.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll participants comprehensively grasped the objective of this questionnaire upon reviewing the informed consent form prior to proceeding. The questionnaire comprises five sections with a total of forty-nine questions (\u003cstrong\u003eAppendix table 1\u003c/strong\u003e). The sections, in sequence, encompass demographic data, personal health information, COVID-related inquiries, Burnout scale (MBI-MP) and Subjective Happiness Scale (SHS).\u003c/p\u003e\n\u003cdiv\u003e\n\u003c/div\u003e\n\u003cp\u003eThe MBI-Human Services Survey for Medical Personnel (MBI-MP) stands as one of the most widely used measurement tools for assessing burnout comprising three subscales and a total of 22 items\u003csup\u003e\u003cspan\u003e40\u003c/span\u003e\u003c/sup\u003e. These subscales include EE section comprises 9 items, DP has 5 items, and PA includes 8 items. Responses to scale items range from \u0026quot;1\u0026thinsp;=\u0026thinsp;never\u0026quot; to \u0026quot;7\u0026thinsp;=\u0026thinsp;always.\u0026quot; The scores for each of the three subscales are calculated separately and categorized as low, moderate, or high levels of burnout (EE, high: \u0026ge;27, moderate: 19 to 26, low: \u0026le;18; DP, high: \u0026ge;10, moderate: 6 to 9, low: \u0026le;5; PA, high: \u0026ge;40, moderate: 34 to 39, low: \u0026le;33). In this study, the more conservative and widely accepted definition of overall burnout rate was employed. Burnout is defined as having \u0026apos;high EE,\u0026apos; \u0026apos;high DP,\u0026apos; and \u0026apos;low PA.\u003csup\u003e\u003cspan\u003e10\u003c/span\u003e, \u003cspan\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe Subjective Happiness Scale (SHS) is a 4-item scale of global subjective happiness\u003csup\u003e\u003cspan\u003e42\u003c/span\u003e\u003c/sup\u003e. Two items prompt respondents to characterize themselves using both absolute and relative ratings, while the other two items provide brief descriptions of happy and unhappy individuals, asking respondents to gauge how well each description fits them. The answers range from 1 to 7. To score the scale, sum the scores for the four questions and divide the total by four. This result is the \u0026quot;subjective happiness score\u0026quot;, typically ranging from about 4.5 to 5.5, with a higher score indicating greater happiness.\u003c/p\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.4 Data collection\u003c/h2\u003e\n \u003cp\u003eUpon completion of the questionnaire, non-identifiable data were gathered. Two independent researchers (PTC and MYC) were responsible for assessing the questionnaire\u0026apos;s adequacy and performing additional data extraction. Any discrepancies were resolved through discussion with the senior researcher (CHW).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.5 Systematic reviews of Asian literature\u003c/h2\u003e\n \u003cp\u003eA systematic literature search, covering PubMed and Embase, was conducted from the inception of databases until February 1, 2024. Additionally, we manually reviewed pertinent studies highlighted in prior reviews and examined reference lists. Two team members (XWL and CHW), working independently, were responsible for identifying all Asian studies related to the analysis of burnout prevalence or contributing risk factors among critical healthcare professionals. The study selection flowchart, detailed search strategy, and exclusion reasons are documented in \u003cstrong\u003eAppendix Fig.\u0026nbsp;1, Appendix Table\u0026nbsp;2\u003c/strong\u003e, and \u003cstrong\u003eAppendix Table\u0026nbsp;3\u003c/strong\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe multivariable analysis of overall burnout and the associated factors of the included participants.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCovariate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHazzard ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u0026ndash;29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19(0.48\u0026ndash;2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u0026ndash;49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29(0.04\u0026ndash;2.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67(0.24\u0026ndash;1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eNumber of children\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.39(0.89\u0026ndash;12.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82(0.50\u0026ndash;6.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.95(0.22\u0026ndash;16.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eThe length of time working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u0026ndash;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80(0.35\u0026ndash;1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u0026ndash;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97(0.30\u0026ndash;3.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.15(0.02\u0026ndash;0.98)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eWorking hours per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26(0.12\u0026ndash;13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32(0.12\u0026ndash;14.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76(0.14\u0026ndash;22.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eAverage night shifts per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le; 25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u0026ndash;50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.55(0.98\u0026ndash;6.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.93(1.09\u0026ndash;7.83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.76(0.68\u0026ndash;4.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eSummary of published studies about burnout of critical healthcare professionals in Asian.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFirst author\u003c/p\u003e\n \u003cp\u003e(Publication year)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSettings\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOccupation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003cp\u003esize, n\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBurnout evaluation tool\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBurnout Prevalence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePotential risk factors\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOur study\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(2024)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTaiwan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eICU/ED\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ephysicians, nurses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e254\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMBI\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;\u003cstrong\u003eOverall burnout: 35.4%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEE\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e: \u003cstrong\u003elow: 11.4%, average: 17.7%, high: 70.9%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDP\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/sup\u003e: \u003cstrong\u003elow: 16.1%, average: 27.6%, high: 56.3%\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePA\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003c/sup\u003e: \u003cstrong\u003elow: 60.6%, average: 28.0%, high: 11.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eBefore the outbreak of COVID pandemics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYilmaz F\u003c/p\u003e\n \u003cp\u003eet al (2011)\u003csup\u003e\u003cspan\u003e66\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Mean score: EE: 14.90\u0026thinsp;\u0026plusmn;\u0026thinsp;5.53, DP: 3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77, PA: 11.43\u0026thinsp;\u0026plusmn;\u0026thinsp;4.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYunbei Xiao\u003c/p\u003e\n \u003cp\u003eet al (2014)\u003csup\u003e\u003cspan\u003e67\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ephysicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Mean score: EE: 6.98\u0026thinsp;\u0026plusmn;\u0026thinsp;5.79, Cynicism: 3.37\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35, PA: 24.79\u0026thinsp;\u0026plusmn;\u0026thinsp;10.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXiao-Chun Zhang et al (2015)\u003csup\u003e\u003cspan\u003e68\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;High degree burnout rate: 16.0%\u003c/p\u003e\n \u003cp\u003e\u0026bull;Mean score: EE: 24.55\u0026thinsp;\u0026plusmn;\u0026thinsp;12.36, DP: 7.05\u0026thinsp;\u0026plusmn;\u0026thinsp;6.50, PA: 35.08\u0026thinsp;\u0026plusmn;\u0026thinsp;9.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYildiz Denat\u003c/p\u003e\n \u003cp\u003eet al (2016)\u003csup\u003e\u003cspan\u003e69\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Mean score: EE: 14.68\u0026thinsp;\u0026plusmn;\u0026thinsp;6.10, DP: 5.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.84, PA: 19.19\u0026thinsp;\u0026plusmn;\u0026thinsp;7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMotasem Hamdan et al (2017)\u003csup\u003e\u003cspan\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePalestine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNurses, physicians, and administrative personnel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEE: low: 14.6%, average: 20.5%, high: 64.8%\u003c/p\u003e\n \u003cp\u003eDP: low: 36.1%, average: 25.8%, high: 38.1%\u003c/p\u003e\n \u003cp\u003ePA: low: 34.6%, average: 21.1%, high: 44.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;workplace violence, young age ( \u0026le;\u0026nbsp;30\u0026nbsp;years)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWacharasint P\u003c/p\u003e\n \u003cp\u003eet al (2018)\u003csup\u003e\u003cspan\u003e70\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThailand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ephysicians, nurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Burnout rate: physicians: 65.2%; nurses: 62.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Physician: Income, thinking idea to quit their ICU job, need vacation\u0026thinsp;\u0026gt;\u0026thinsp;2 days/week\u003c/p\u003e\n \u003cp\u003e\u0026bull;Nurse: age\u0026thinsp;\u0026gt;\u0026thinsp;40 years old, ICU experience\u0026thinsp;\u0026gt;\u0026thinsp;5 years, patient\u0026apos;s ICU length of stay\u0026thinsp;\u0026gt;\u0026thinsp;5 days, workload and thinking idea to quit their ICU job\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKay Choong See\u003c/p\u003e\n \u003cp\u003eet al (2018)\u003csup\u003e\u003cspan\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAsia\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ephysicians, nurses\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4092\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026bull;High degree burnout rate: 51.6%\u003c/p\u003e\n \u003cp\u003e\u0026bull;Mean score: EE: 25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2, DP: 8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2, PA: 32.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Lower risks: religiosity, years of working in the current department, shift work, better work-life balance and number of stay-home night calls\u003c/p\u003e\n \u003cp\u003e\u0026bull;Higher risks: work days per month and having a bachelor\u0026apos;s degree\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtefeh Soltanifar\u003c/p\u003e\n \u003cp\u003eet al (2018)\u003csup\u003e\u003cspan\u003e71\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efemale physicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEE Moderate to high: 84.5%\u003c/p\u003e\n \u003cp\u003eDP Moderate to high :48.1%\u003c/p\u003e\n \u003cp\u003ePA low: 80.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbdulghani M Alqahtani et al (2019)\u003csup\u003e\u003cspan\u003e72\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSaudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ephysicians, nurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Burnout rate: 16.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Higher risks: male, Smokers and sleep disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSaravanabavan L\u003c/p\u003e\n \u003cp\u003eet al (2019)\u003csup\u003e\u003cspan\u003e73\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ephysicians, nurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;High degree burnout rate: 80.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003eAfter the outbreak of COVID pandemics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSedigheh Salimi\u003c/p\u003e\n \u003cp\u003eet al (2020)\u003csup\u003e\u003cspan\u003e74\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProQOL Scale\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Average burnout score:36.27\u0026thinsp;\u0026plusmn;\u0026thinsp;7.45\u003c/p\u003e\n \u003cp\u003e\u0026bull;low: 8.0%, average: 49.8%, high: 42.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZakaria MI\u003c/p\u003e\n \u003cp\u003eet al (2021)\u003csup\u003e\u003cspan\u003e75\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalaysia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ephysicians, nurses, assistant medical officer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurnout Questionnaire \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Nurses:61.2%, doctors:35.1%, assistant medical officer: 29.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequent exposure to angry public, job overload, lack of clear guidelines, and perception of underpaid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWei Ping Daniel Chor et al (2021)\u003csup\u003e\u003cspan\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingapore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ephysicians, nurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Average burnout score: 49.2\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreviously working in the ED or UCC before the COVID-19 pandemic; nurse (compared to physicians)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZihan Hu MS\u003c/p\u003e\n \u003cp\u003eet al (2021)\u003csup\u003e\u003cspan\u003e76\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ephysicians, nurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Burnout rate: 69.7%\u003c/p\u003e\n \u003cp\u003eEE: low: 6.1%, average: 35.1%, high: 58.8%\u003c/p\u003e\n \u003cp\u003eDP: low: 29.8%, average: 36.7%, high: 33.5%\u003c/p\u003e\n \u003cp\u003ePA: low: 64.9%, average: 14.9%, high: 20.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Lower risks: exercise every day, more paid vacation\u003c/p\u003e\n \u003cp\u003e\u0026bull;Higher risks: Having Comorbidities, more years of work experience and more night shifts\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuan Ma\u003c/p\u003e\n \u003cp\u003eet al (2022)\u003csup\u003e\u003cspan\u003e77\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ephysicians, nurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProQOL Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Average burnout score:27.74\u0026thinsp;\u0026plusmn;\u0026thinsp;6.19\u003c/p\u003e\n \u003cp\u003e\u0026bull;low: 19.3%, average: 78.4%, high: 2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJing Wang\u003c/p\u003e\n \u003cp\u003eet al (2022)\u003csup\u003e\u003cspan\u003e78\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ephysicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Burnout rate: 82.1%\u003c/p\u003e\n \u003cp\u003e\u0026bull;Mean score: EE: 24.14\u0026thinsp;\u0026plusmn;\u0026thinsp;10.90, DP: 9.69\u0026thinsp;\u0026plusmn;\u0026thinsp;5.70, PA: 28.55\u0026thinsp;\u0026plusmn;\u0026thinsp;9.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of children, income, and difficulties in treatment decisions\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArtem Kashtanov\u003c/p\u003e\n \u003cp\u003eet al. (2022)\u003csup\u003e\u003cspan\u003e79\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRussia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ephysicians, nurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Non-COVID-19 ICU\u003c/p\u003e\n \u003cp\u003eEE: low: 14.6%, average: 30.8%, high: 54.6%\u003c/p\u003e\n \u003cp\u003eDP: low: 11.6%, average: 16.5%, high: 71.9%\u003c/p\u003e\n \u003cp\u003ePA: low: 23.5%, average: 40.3%, high: 36.2%\u003c/p\u003e\n \u003cp\u003e\u0026bull;COVID-19 ICU\u003c/p\u003e\n \u003cp\u003eEE: low: 16.5%, average: 31.5%, high: 52.0%\u003c/p\u003e\n \u003cp\u003eDP: low: 7.4%, average: 9.4%, high: 83.1%\u003c/p\u003e\n \u003cp\u003ePA: low: 25.4%, average: 45.4%, high: 29.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKim C\u003c/p\u003e\n \u003cp\u003eet al (2022)\u003csup\u003e\u003cspan\u003e80\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ephysicians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProQOL Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Average burnout score:33.81\u0026thinsp;\u0026plusmn;\u0026thinsp;6.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAkira Kuriyama\u003c/p\u003e\n \u003cp\u003eet al (2022)\u003csup\u003e\u003cspan\u003e81\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAll critical care professionals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMini Z 2.0 Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Burnout rate: 24.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026bull;Lower risks: higher resilience scores and perceived support from the hospital or colleagues\u003c/p\u003e\n \u003cp\u003e\u0026bull;Higher risks: having depression or anxiety, experiencing stigma from caring for patients with COVID-19, or having experienced self-quarantine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAylin Arıkan\u003c/p\u003e\n \u003cp\u003eet al (2023)\u003csup\u003e\u003cspan\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003enurses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEE: low: 9.1%, average: 40.5%, high: 51.4%\u003c/p\u003e\n \u003cp\u003eDP: low: 14.4%, average: 26.7%, high: 58.9%\u003c/p\u003e\n \u003cp\u003ePA: low: 89.6%, average: 10.4%, high: 0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e MBI: Maslach Burnout Inventory ; \u003csup\u003eb\u003c/sup\u003e EE: Emotional Exhaustion ; \u003csup\u003ec\u003c/sup\u003e DP: Depersonalization ; \u003csup\u003ed\u003c/sup\u003e PA: Personal Accomplishment ; \u003csup\u003ee\u003c/sup\u003e ProQOL Scale: The Professional Quality of Life Scale ; \u003csup\u003ef\u003c/sup\u003e Burnout Questionnaire was adapted from Michelle Post, Public Welfare, Vol. 39, No. 1, 1981, American Public Welfare Association\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eBaseline demographic categorical variables are depicted as percentages (%), while continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. One-way ANOVA is used to examine differences between groups. However, if Levene\u0026apos;s test for homogeneity of variances fails (indicating significant variance differences between groups with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), Welch\u0026apos;s ANOVA is used instead to check for differences. If differences are found, Tukey post hoc analysis is used to analyze the differences between groups. Univariate logistic regression was employed to investigate the association between potential risk factors and each burnout subscales (EE, DP, and PA; Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). Variables that demonstrated significance in the univariate logistic regression (P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.2)\u003csup\u003e43\u003c/sup\u003e were subsequently analyzed in multivariate logistic regression (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e and \u003cstrong\u003eAppendix Table\u0026nbsp;4\u003c/strong\u003e). All analyses were conducted using SPSS Statistics, version 24.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.7 Ethical considerations\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ewas secured from the Institutional Review Board of Jen-Ai Hospital (IRB Number: 202300085B0). All submitted questionnaires were treated with strict confidentiality, accessible only to the researchers involved in this study. License granting the right to utilize and administer the Maslach Burnout Inventory has been secured. There was no external funding source was involved in this research initiative.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe conducted a self-reported questionnaire survey spanning from December 2023 to January 2024. The study involved 254 critical healthcare professionals across four hospitals in Taiwan: Keelung Chang Gung Memorial Hospital (38 participants), Linkou Chang Gung Memorial Hospital (74 participants), Chiayi Chang Gung Memorial Hospital (83 participants), and Jen-Ai Hospital, Dali Branch (78 participants, Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). The response rate achieved was around 51.0%. Through manual examination, no duplicated or incomplete questionnaires were identified but one participant decided to withdraw after reviewing the participant consent form.\u003c/p\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e3.1 Demographic characteristics, general health conditions and the Subjective Happiness Scale (SHS) of the included participants\u003c/h2\u003e\n \u003cp\u003eAmong 254 participants, 46.9% were under 30 years old, with the majority being nurses (81.9%) and unmarried (60.6%). A total of 133 participants worked in the emergency room (52.4%). A significant portion (41.3%) had less than five years of experience in their current emergency department or intensive care unit. Furthermore, 53.2% of participants worked over 40 hours per week, and 45.7% had night shifts for over 50% of the month. Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e provides detailed demographic characteristics.\u003c/p\u003e\n \u003cp\u003eRegarding self-assessed general health conditions, detailed results can be found in \u003cstrong\u003eAppendix Table\u0026nbsp;5\u003c/strong\u003e. Half of the participants rated their health condition as comparable to others. A low percentage relied on medication for sleep (7.8%), used tobacco (2.4%), or consumed alcohol (2.8%). Additionally, the majority (41.7%) did not have regular exercise habits. More than half (58.3%) sometimes experienced stress, while only 15.7% never considered quitting in the past month. About the current workplace stressors, there is differences between physicians and nurses. Nurses reported workload burden (73.6%), additional administrative tasks (63.0%), and a shortage of vacation time (61.5%) as their primary sources of workplace stress, while physicians mentioned workload burden (39.1%), fear of inadequate capabilities (37.0%), and shift work stress (34.8%) as their top stressors. Among the participants, the average subjective happiness score was 4.6, with detailed scores of each subgroup listed in \u003cstrong\u003eAppendix Table\u0026nbsp;6\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cspan\u003e\u003cem\u003e3.2 Prevalence of burn-out among critical healthcare professionals and the results of each subscale of the MBI-Human Services Survey for Medical Personnel (MBI-MP)\u003c/em\u003e\u003cbr\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eIn our study, we found that the overall burnout rate was 35.4% (nurses: 37.5%, physicians: 26%). Specifically, the prevalence of high EE was 70.9%, high DP was 56.3%, and low PA was 60.6%.\u003c/p\u003e\n \u003cp\u003eRegarding the results of each subscale, the average EE score was 35.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6. Nurses experienced higher EE compared to physicians. Additionally, younger individuals, those who were single, worked in the emergency room (ER), had longer average working hours per week, and had more night shifts tended to have higher levels of EE. The mean score for DP was 11.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5. Younger individuals, physicians, those working in the ER, singles, those with no previous critical care experience, and those who had been critical healthcare professionals for a shorter period tended to have higher levels of DP. The average score for PA was 30.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9. Lower levels of PA were observed among younger and single individuals. For a concise overview of burnout components, please refer to the details outlined in Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.3 Associated factors of burn-out syndrome\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e summarizes the results of the univariable analysis regarding the potential factors associated with each burnout subscale. Variables that revealed significant univariate associations were subsequently included in the multivariate analyses (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e and \u003cstrong\u003eAppendix Table\u0026nbsp;4\u003c/strong\u003e). The results demonstrated that individuals with less experience (6\u0026ndash;10 years: 0.80, 95% CI 0.35 to 1.86; 11\u0026ndash;15 years: 0.97, 95% CI 0.30 to 3.11; \u0026gt;15 years: 0.15, 95% CI 0.02 to 0.98) and those with more night shifts (25\u0026ndash;50%: 2.55, 95% CI 0.98 to 6.63; 50\u0026ndash;75%: 2.93, 95% CI 1.09 to 7.83; \u0026ge;75%: 1.76, 95% CI 0.68 to 4.56) had an increased risk of overall burnout.\u003c/p\u003e\n \u003cp\u003eFurther analysis showed that none of the factors remained significant in increasing the risk of higher EE. However, younger individuals (30\u0026ndash;39 years: 2.72, 95% CI 1.05 to 7.07; 40\u0026ndash;49 years: 1.20, 95% CI 0.23 to 6.21; \u0026gt;50 years: 0.19, 95% CI 0.01 to 4.62), less experienced individuals (6\u0026ndash;10 years: 0.70, 95% CI 0.29 to 1.71; 11\u0026ndash;15 years: 1.06, 95% CI 0.31 to 3.59; \u0026gt;15 years: 1.92, 95% CI 0.41 to 9.11), and those with more night shifts (25\u0026ndash;50%: 2.85, 95% CI 1.16 to 7.00; 50\u0026ndash;75%: 2.54, 95% CI 1.02 to 6.32; \u0026ge;75%: 1.80, 95% CI 0.74 to 4.39) had a higher risk of suffering from DP. The risk of experiencing low PA was greater for less experienced individuals (6\u0026ndash;10 years: 0.52, 95% CI 0.22 to 1.21; 11\u0026ndash;15 years: 0.31, 95% CI 0.09 to 1.00; \u0026gt;15 years: 0.79, 95% CI 0.19 to 3.29).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.4 The Systematic review of Asian literatures\u003c/h2\u003e\n \u003cp\u003eA total of 20 Asian studies related to burnout among critical care practitioners were included, and the summary of conclusions was listed in Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e. The Maslach Burnout Inventory (MBI) was the most common tool for burnout evaluation, with variable burnout prevalence ranging from 16.3\u0026ndash;82.1%. Factors associated with higher risks of burnout included having comorbidities, job overload, previous experience in ER or ICUs, night shifts, and perception of being underpaid.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study identified the high prevalence of burnout rate (overall burnout rate: 35.4%) in critical healthcare practitioners during the post-pandemic era in Taiwan and discovered they were experiencing high levels of EE and DP, coupled with a low level of PA. Lacking experience of critical care, excessive working hours and night shifts were possible key factors damaging the wellbeing of the critical healthcare professionals. Besides, the top three work stressors identified were excessive workload, the burden of administrative tasks, and a shortage of vacation time. Through systematic reviews of Asian literature regarding burnout, we had discovered not only demographic variations in the prevalence of burnout but differences in the associated factors before and after the COVID-19 pandemic.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCOVID pandemics and its impact on staff wellbeing\u003c/h2\u003e \u003cp\u003eThe COVID-19 pandemic has adversely impacted the wellbeing of the critical healthcare professionals. According to the recent systematic review, the prevalence of overall burnout of critical care staff ranged from 34.6 to 61.5%\u003csup\u003e12, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Exhausting workload, anxiety and fear of the pandemic, the burden of responsibility and moral distress were previously known possible issues of burnout during the pandemics\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Despite the decrease in stress associated with caring for COVID patients during the post-pandemic era (\u003cb\u003eAppendix Table\u0026nbsp;5)\u003c/b\u003e, the overall prevalence of burnout didn\u0026rsquo;t decrease\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The pandemic itself was not necessarily the only reason associated with increased burnout\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. However, certain issues such as job overload, staff shortages, additional administrative tasks, shift work stress, and economic concerns continue to pose significant stress for critical healthcare practitioners.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePrevalence of burnout Before and After the COVID pandemics\u003c/h2\u003e \u003cp\u003eWe summarized the published studies on burnout among critical healthcare professionals in Asian (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. MBI was the most used assessment instrument. The prevalence of estimates burnout slightly increased from 16.3 to 80.0% before the pandemics to 24.3 to 82.1% after the pandemics. The substantial variability in the prevalence of burnout across studies was attributed not only to the difference in medical systems but to the marked variation in assessment instruments and definitions of burnout\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. These variations preclude the cross-national comparisons regarding the trends in the prevalence of burnout before and after the COVID pandemics. The importance of developing a consensus definition of burnout, standardizing assessment instruments and obtaining local data were emphasized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAssociated factors of burnout during the post-pandemic era\u003c/h2\u003e \u003cp\u003eDespite the causal relationship between burnout and risk factors may be limited by the cross-sectional design of studies, we can still take a glance at the vulnerable populations. Previous studies discovered that being nurses, job overload, perception of underpaid, experiencing stigma from caring for COVID-19 patients, having personal health condition and more night shift were possible risks factors (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In our study, several variables were found associated with burnout, including being younger, unmarried, having less working experience, longer working hours and more night shifts.\u003c/p\u003e \u003cp\u003eUndoubtedly, being a critical care professional entails a high risk of burnout compared to other specialties due to the nature of the job\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. However, there remains conflicts concerning level of burnout between different occupation. Previous meta-analysis by MM Macaron et al and multinational survey by See KC et al revealed no significant difference in pooled estimate of burnout prevalence between physicians and nurses\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. On the contrary, critical care nurses were recognized as high-risk group by Gualano MR et al\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e and the multi-center study by Chor WP et al also discovered slightly higher burnout rate among nurses compared with physicians working in ED (53.3% versus 42.5%)\u003csup\u003e32\u003c/sup\u003e.These variation between studies may reflect the difference in organization-level healthcare systems. In Taiwan, there is the lowest physician or nurse -population ratio, with 2 physicians and 7.6 nurses per 1,000 population, according to the survey by Organization for Economic Cooperation and Development (OECD). However, the number of adult critical care beds leads among Asian countries, with 28.5 beds per 100,000 population, compared to the average of 3.6 beds per 100,000 population\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. In our study, we found one-third of critical care professionals reported stress related to shift work, and over 70% of nurses experienced a workload burden (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Despite no significant difference in each subscale of burnout between physicians and nurses, nurses had higher prevalence of overall burnout compared to physicians (37.5% versus 26%), which may be associated with the critical care nurses were often working understaffed, having additional administrative tasks, and working overtime\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHigh EE and DP were observed in younger, less experienced individuals, consistent with previous studies\u003csup\u003e\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. While burnout is often considered to mainly affect those in their later careers, this may be related to the shortage of critical professionals and the common situation where nurses are forced to handle excessive, unfamiliar clinical tasks before they are fully prepared. Our data reflected that workload burden and staff shortages were reported as the top work stressors \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. According to a survey by the Taiwan Ministry of Health and Welfare, one nurse in Taiwan cares for an average of 9 to 15 patients. Notably, younger individuals comprise most critical healthcare professionals in Taiwan. Therefore, it\u0026rsquo;s not surprising that the turnover rate for nurses is as high as 14.5% annually, with most nurses leaving within an average of 6.5 years, according to the Taiwan Ministry of Health and Welfare's 2023 survey.\u003c/p\u003e \u003cp\u003eIrregular night shifts and longer working hours were associated with higher scores in EE in our study. Night shift stress has been previously linked to burnout, mental health problems, and sleep disturbances\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Furthermore, compared to those with fixed night shifts, participants with irregular night shifts had a higher risk of burnout \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Irregular shift schedules can compromise physical and psychological health as well as occupational functionality. Additionally, long working hours, especially working more than 55 hours per week, were associated with greater sleep disturbances and occupational stress compared to working 40 hours a week\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Implementing reasonable working hours and regular shift schedules may be effective interventions for preventing burnout and enhancing job performance.\u003c/p\u003e \u003cp\u003eMaintaining a work-life balance is crucial for well-being, and marriage appears to be one of the solutions\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. According to the theory of work-family enrichment, married individuals tend to experience better job satisfaction by actively engaging in their parental roles\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Recent studies conducted during the COVID-19 pandemic have highlighted the significant moderating role of family support in mitigating burnout across various dimensions and enhancing subjective well-being\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Despite the potential stresses of parenthood, the protective effects of marriage can be attributed to lifestyle changes, involvement in parental responsibilities, and simply spending time with family\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Consistent with prior research, we found that married individuals exhibited lower EE and PA with higher DP compared to their unmarried counterparts\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Individuals with more children also exhibited lower EE and PA with higher DP, a phenomenon not observed in individuals with pets in our study.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe relationship between burnout interventions and locally identified workplace stressors and risk factors.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eGiven the demographic variation in burnout, gathering local data, identifying vulnerable populations, and promoting interventions can help reduce the risk of burnout.\u003c/p\u003e \u003cp\u003eAt the individual level, improving interprofessional communication, participating in face-to-face group programs, fostering a positive mindset, and maintaining a work-family balance have proven beneficial\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Organizational-level interventions include reducing unnecessary administrative tasks and workload, creating a supportive work atmosphere, and regulating the number of workdays and night shifts. Prioritizing the well-being of healthcare staff through these interventions establishes a solid foundation for reducing burnout. Consequently, it leads to improvements in the quality of care, reductions in medical expenditures, and lower turnover rates.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis multi-institutional study highlights the persistently high prevalence of burnout among critical healthcare professionals in Taiwan, even post-pandemic. Modifiable factors such as age, marital status, work experience, working hours, and night shifts play a role. Key stressors include workload, administrative tasks, limited vacation time, and the stress of shift work. Regional variations in burnout across Asia emphasize the need for tailored interventions. Continued research is essential to monitor and support the well-being of critical professionals and to maintain healthcare quality.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStrength and limitations\u003c/h2\u003e \u003cp\u003eThe study exhibits both strengths and potential limitations. Firstly, it authentically captures the psychological well-being of critical care healthcare professionals in Taiwan, despite variations in medical operation modes and disease severity among the included hospitals. However, the applicability of our findings to other countries should be approached with caution. Secondly, due to the lack of consensus definition of burnout, cautious should be taken if comparing our results to other studies, despite the widely accepted definition of burnout rate was used in our study. Thirdly, as a cross-sectional self-report questionnaire survey, drawing causal inferences from the research results requires careful consideration, and the presence of social desirability bias may introduce self-reporting bias. Lastly, participants in this study are voluntary, lacking compulsion, which may lead to a relatively low questionnaire response rate. However, their willingness to participate ensures more sincere responses, thereby enhancing the accuracy of the questionnaire. Moreover, by not mandating participation, the study avoids imposing additional psychological stress on critical healthcare professionals of selected hospitals.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e This research received approval from the Jen-Ai Hospital Institutional Review Board (IRB Number:\u0026nbsp;202300085B0). All participants fully understood the study\u0026apos;s goal and reviewed the informed consent form before proceeding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYLL, CHW: study design; XWL and CHW: develop research strategy and perform systematic reviews; MYC and PTC: evaluating the questionnaire\u0026apos;s adequacy and data extraction; CHW: verified the extracted data; YLL and YCL: performed the statistical analysis; YLL, MYC,\u0026nbsp;JWD\u0026nbsp;and\u0026nbsp;CHW: drift the manuscript; CHW: revised the manuscript; All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e All the datasets and the supplementary materials mentioned in the article are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMaslach C, Schaufeli WB, Leiter MP. 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Sleep 2009;32:737-45.\u003c/li\u003e\n\u003cli\u003eYang D, Fang G, Fu D, et al. Impact of work-family support on job burnout among primary health workers and the mediating role of career identity: A cross-sectional study. Front Public Health 2023;11:1115792.\u003c/li\u003e\n\u003cli\u003eCa\u0026ntilde;adas-De la Fuente GA, Ortega E, Ramirez-Baena L, et al. Gender, Marital Status, and Children as Risk Factors for Burnout in Nurses: A Meta-Analytic Study. Int J Environ Res Public Health 2018;15.\u003c/li\u003e\n\u003cli\u003eGreenhaus J, Powell G. When Work And Family Are Allies: A Theory Of Work-Family Enrichment. The Academy of Management Review 2006;31:72-92.\u003c/li\u003e\n\u003cli\u003eKarag\u0026ouml;l A, T\u0026ouml;renli Kaya Z. Healthcare workers\u0026apos; burn-out, hopelessness, fear of COVID-19 and perceived social support levels. Eur J Psychiatry 2022;36:200-206.\u003c/li\u003e\n\u003cli\u003eShin Y, Hur WM, Park K. 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Assessment of burnout among emergency medicine healthcare workers in a teaching hospital in Malaysia during COVID-19 pandemic. Hong Kong Journal of Emergency Medicine 2021;28:254-259.\u003c/li\u003e\n\u003cli\u003eHu Z, Wang H, Xie J, et al. Burnout in ICU doctors and nurses in mainland China-A national cross-sectional study. J Crit Care 2021;62:265-270.\u003c/li\u003e\n\u003cli\u003eMa H, Huang SQ, We B, et al. Compassion fatigue, burnout, compassion satisfaction and depression among emergency department physicians and nurses: a cross-sectional study. BMJ Open 2022;12:e055941.\u003c/li\u003e\n\u003cli\u003eWang J, Hu B, Peng Z, et al. Prevalence of burnout among intensivists in mainland China: a nationwide cross-sectional survey. Crit Care 2021;25:8.\u003c/li\u003e\n\u003cli\u003eKashtanov A, Molotok E, Yavorovskiy A, et al. A Comparative Cross-Sectional Study Assessing the Psycho-Emotional State of Intensive Care Units\u0026apos; Physicians and Nurses of COVID-19 Hospitals of a Russian Metropolis. Int J Environ Res Public Health 2022;19.\u003c/li\u003e\n\u003cli\u003eKim C, Park KH, Eo EK, et al. Burnout and Resilience among Emergency Physicians at Korean University Hospitals during the COVID-19 Pandemic: A Cross-Sectional Analysis. Yonsei Med J 2022;63:372-379.\u003c/li\u003e\n\u003cli\u003eKuriyama A, Sakuraya M, Kinjo M, et al. Burnout and Turnover Intention in Critical Care Professionals During the COVID-19 Pandemic in Japan: A Cross-sectional Survey. Ann Am Thorac Soc 2023;20:262-268.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Burnout, occupation burnout, mental wellbeing, critical healthcare professionals, post-COVID-19 era","lastPublishedDoi":"10.21203/rs.3.rs-4643455/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4643455/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground \u0026amp; Aims:\u003c/h2\u003e \u003cp\u003eBurnout is a global concern, and critical healthcare professionals have been identified as a high-risk population of burnout. Early identification is crucial, but the prevalence of burnout and its risk factors demonstrate significant geographical variations. This study aims to investigate the prevalence of burnout among critical healthcare professionals and explore potential risk factors during the post-pandemic era in Taiwan.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA web-based questionnaire survey was conducted from December 1, 2023, to January 31, 2024, targeting critical healthcare professionals employed in selected medical institutions affiliated with the Chang Gung Memorial Hospital Foundation, one of Taiwan's largest healthcare organizations. Demographic information, the Subjective Happiness Scale (SHS), current work stressors and self-reported general health data were collected. The study utilized the MBI-Human Services Survey for Medical Personnel (MBI-MP). Univariate and multivariate logistic regression were employed to investigate the association between risk factors and each burnout subscales. A systematic review of Asian literature concerning burnout among critical care practitioners was also conducted in accordance with PRISMA guideline.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eIn our study, 254 participants were enrolled, with an overall burnout rate of 35.4%. The prevalence of high emotional exhaustion (EE) was 70.9%, high depersonalization (DP) was 56.3%, and low personal accomplishment (PA) was 60.6%. Young, unmarried populations, individuals with limited work experience, longer working hours, and night shifts are potential vulnerable groups susceptible to burnout. The top three stressors identified were excessive workload, the burden of administrative tasks, and a shortage of vacation time. Our systematic review included 20 Asian studies on the same issue, with variable burnout prevalence ranging from 16.3\u0026ndash;82.1%.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThe prevalence of burnout was high among critical healthcare professionals in post-pandemic Taiwan, particularly affecting younger, unmarried populations and individuals with limited work experience, longer hours, and more night shifts. The influence of pandemic-related factors has decreased. Regional variations in burnout have been observed across Asia, highlighting the need for further research to identify local risk factors and protect the well-being of professionals and healthcare quality.\u003c/p\u003e","manuscriptTitle":"Prevalence and the associated factors of burnout among the critical healthcare professionals during the post-pandemic era: a multi-institutional survey in Taiwan with a systematic review of the Asian literatures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 11:00:15","doi":"10.21203/rs.3.rs-4643455/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-23T10:59:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-15T19:31:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147083209853159407005486972712172967888","date":"2024-08-09T01:33:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38762182384544781320623199463561523779","date":"2024-08-07T13:26:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-29T21:49:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35342098436154280490764834087555390927","date":"2024-07-20T15:58:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-19T08:27:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-30T06:02:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-29T03:58:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-06-26T14:36:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e84f13d-e42a-433d-af51-d62321eddb76","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:04:08+00:00","versionOfRecord":{"articleIdentity":"rs-4643455","link":"https://doi.org/10.1186/s12889-024-21084-6","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-12-18 15:58:07","publishedOnDateReadable":"December 18th, 2024"},"versionCreatedAt":"2024-07-22 11:00:15","video":"","vorDoi":"10.1186/s12889-024-21084-6","vorDoiUrl":"https://doi.org/10.1186/s12889-024-21084-6","workflowStages":[]},"version":"v1","identity":"rs-4643455","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4643455","identity":"rs-4643455","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}