The Buffering Role of Resilience on Burnout, Depression, Anxiety, and Stress among Healthcare Workers in Sri Lanka

The Buffering Role of Resilience on Burnout, Depression, Anxiety, and Stress among Healthcare Workers in Sri Lanka | 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 Short Report The Buffering Role of Resilience on Burnout, Depression, Anxiety, and Stress among Healthcare Workers in Sri Lanka Anuradha Baminiwatta, Roshan Fernando, Thanabalasingam Gadambanathan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5091541/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This study aimed to explore the relationship between burnout, depression, anxiety, stress, and resilience among healthcare workers (HCWs), and to investigate the moderating role of resilience against the mental health correlates of burnout. For this purpose, using a cross-sectional design, 318 HCWs from various categories (nurses, midwives, doctors, etc.) recruited from hospital and community settings in two Districts of Sri Lanka during the COVID-19 pandemic were surveyed using the Copenhagen Burnout Inventory (CBI), Depression, Anxiety and Stress Scale (DASS-21), and the Brief Resilience Scale. CBI assessed three domains: personal burnout (PB), work-related burnout (WRB) and client-related burnout (CRB). The prevalence of PB, WRB and CRB were 26.4%, 12.9%, and 7.9%, respectively. The prevalence of low, normal, and high resilience was 25.6%, 66.2%, and 8.6%, respectively. All three burnout domains correlated positively with depression, anxiety, and stress. Resilience correlated inversely with all CBI and DASS-21 scores. In moderator analysis, higher levels of resilience dampened the effects of CRB on depression and anxiety, and WRB on anxiety, but resilience moderated the psychological consequences of PB in the opposite direction. Our findings indicate notable rates of burnout among Sri Lankan HCWs, and suggest a possible buffering role of resilience against the psychological consequences of burnout. burnout depression healthcare worker resilience anxiety work stress Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Burnout has been defined as a psychological syndrome characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment (Maslach et al., 1997). It has diverse consequences on individuals, societies, and health systems (Salvagioni et al., 2017). Psychological consequences of burnout include poor concentration and memory, anxiety, depression, suicidal behaviour, insomnia, and irritability (Salvagioni et al., 2017). Positive psychological attributes such as resilience, optimism and self-efficacy may protect individuals against such negative mental health sequelae(Edú-valsania et al., 2022). Understanding burnout among healthcare workers (HCWs) became important during the COVID-19 pandemic. A meta-analysis reported a pooled prevalence of 37% for burnout, and 33%, 42% and 40% respectively for depressive, anxiety, and stress, among nurses during the pandemic (Aymerich et al., 2022). In Sri Lanka, depression, anxiety, and stress were observed to be notably high among HCWs during that time (Baminiwatta et al., 2021). While some authors suggest that burnout and depression are two distinct constructs, others argue it is merely a form of work-related depression (Bianchi et al., 2015; Schonfeld, 1991). Some researchers have suggested that burnout is a causal antecedent of depression and not vice versa, based on longitudinal observations (Hakanen & Schaufeli, 2012). Other authors contend that the relationship is bi-directional (Bianchi et al., 2015). Conceptual overlaps of burnout with anxiety and stress have also been described(Golonka et al., 2019; Parker & Tavella, 2022). Resilience is the capacity for adaptation and “bouncing back” in the face of adversity(Windle, 2011). It offers protection against mental health problems as well as burnout (Guo et al., 2018; Hu et al., 2015; McCain et al., 2018). Resilience is currently understood as a modifiable trait, and interventions enhancing resilience may alleviate HCW burnout (Cohen et al., 2023). Whether resilience plays a buffering role against the negative psychological effects of burnout such as depression, anxiety and stress has not been widely studied. This study thus aimed to explore the relationship between burnout, depression, anxiety, stress, and resilience among HCWs in Sri Lanka. We also hypothesize that burnout is an antecedent of depression, anxiety and stress among HCWs in the context of a pandemic, and that resilience moderates these relationships. Methods Study Design, Setting and Participants This was a cross-sectional study conducted among HCWs belonging to any professional category (nurses, midwives, doctors, public health inspectors, allied health professionals and healthcare assistants) employed in government hospitals and Medical Officer of Health (MOH) offices in two districts - Gampaha and Batticaloa. The study was conducted in October-December 2021, i.e., during the third wave of the COVID-19 pandemic. Convenience sampling was used. Measures Copenhagen Burnout Inventory (CBI) CBI is a 19-item survey with positively and negatively framed items that cover 3 domains of burnout: personal burnout (PB; 6 items), work-related burnout (WRB; 7 items) and client-related burnout (CRB; 6 items) (Kristensen et al., 2005). The responses to all items are rescaled to a 0-100 metric. Scores are calculated for each domain as the mean of the respective items. Scores of less than 50 are considered as ‘no/low’ burnout, 50 to 74.9 as ‘moderate’, 75 to 99.9 as ‘high’, and a score of 100 is considered ‘severe’ burnout (Creedy et al., 2017). CBI has been previously translated and validated into Sinhala among public health midwives (PHMs) (Pathiraja, 2011). The scale was translated into Tamil using standard procedures prior to this study. Confirmatory factor analysis affirmed the three-factor structure of the Tamil CBI in the present sample with excellent model fit [CFI = 0.996, TLI = 0.996, RMSEA = 0.051, SRMR = 0.069), and the subscales demonstrated good internal consistency (alpha = 0.83–0.89). Brief Resilience Scale (BRS) The BRS is a 6-item instrument developed for measuring resilience (Smith et al., 2008). It intends to assess resilience in its most basic meaning—the ability to “bounce back.” BRS was translated and validated in Sinhala and Tamil languages prior to the present study (Baminiwatta et al., 2023). Resilience score is the average of the six item scores, and it can be interpreted as low (1–2.99), normal (3–4.30), and high resilience (4.31–5)(Windle et al., 2022). Depression, Anxiety and Stress Scale (DASS)-21 This 21-item self-administered scale includes three subscales assessing depression, anxiety, and stress separately, as experienced during the past week (Lovibond & Lovibond, 1995). Subscale scores are generated by summing the scores of items in each subscale, and multiplying by two. DASS-21 has been culturally adapted and validated into Sinhala (Rekha, 2012). Data Analysis Data analysis was performed using IBM SPSS Version 26. Pearson correlation among continuous parameters were tested. Simple moderation models were tested using the PROCESS Macro on SPSS. As a post-hoc analysis, cluster analysis was performed using hierarchical agglomerative cluster analysis followed by K-means clustering to identify profiles of burnout, resilience, and DASS-21 scores. Dendrograms were inspected to determine the number of clusters. Ethical Considerations Ethics approval was obtained from the Ethics Review Committee of [Anonymized for Peer Review]. Informed written consent was obtained from each participant. Any participant who actively reported mental health problems to the researchers were offered a referral to mental health services. Results Characteristics of the Sample A total of 318 participants responded (208 from Gampaha and 110 from Batticaloa district). Respondents were predominantly female (83.3%). The sample consisted of HCWs employed in both the public health (56.9%) and hospital-based (43.1%) health services. Public health midwives (37.1%) and hospital nurses (26.7%) predominated in the sample. The majority (89.6%) had been performing COVID-19-related duties. The sample characteristics are summarized in Table 1 . Table 1 Characteristics of the sample (n = 318) Characteristics n % Gender Male 53 16.7 Female 265 83.3 Age (years) 21–30 31–40 41–50 51–60 > 60 62 89 95 66 6 19.5 28 29.9 20.8 1.9 Marital status Single 51 16 Married 263 82.7 Separated/widowed 4 1.3 Profession Hospital Nurse Public Health Nurse Public Health Midwife Medical officer (hospital-based) Medical officer (public health) Public Health Inspector Allied Health Professional Supportive staff 85 9 118 24 9 28 29 16 26.7 2.8 37.1 7.5 2.8 8.8 9.1 5 Monthly income Rs. Rs. 100000 Missing 25 1 7.9 0.3 Duration of service Less than 1 year 20 6.3 (years) 1–5 58 18.3 6–10 53 16.7 More than 10 years 186 58.7 Having a chronic medical condition Yes No Missing 40 277 1 12.6 87.1 0.3 Performing COVID-19-related duties Yes No 285 33 89.6 10.4 Tested positive for COVID-19 Yes No 37 281 11.6 82.4 Wearing Personal Protective Equipment at work Yes No Missing 229 87 2 72 27.4 0.6 Burnout, Mental Health Problems and Resilience Based on the CBI domains, moderate or higher degrees of PB, WRB and CRB were reported by 26.4% [95% CI = 21.6%, 31.2%], 12.9% [95% CI = 9.22%, 16.6%] and 7.9% [95% CI = 4.9%, 10.9%], of the participants, respectively. In the sample, 68 (21.4%) had moderate or higher burnout in only one domain; 23 (7.2%) in two domains; and 12 (3.8%) in all three domains; 103 participants (32.4%) had moderate or higher burnout on at least one domain. The respective mean (SD) scores on the PB, WRB and CRB subscales were 36.4 (20.9), 26.9 (17.5) and 26.1 (14.7). The prevalence of depression, anxiety, and stress according to the DASS-21 cut-off values (including cases with mild, moderate, severe and extremely severe states) were 18.2%, 23.2% and 14.3%, respectively. According to the BRS, the majority (66.2%) had ‘normal resilience’, whereas 25.6% had ‘low resilience’; the mean resilience score (on a scale of 1 to 5) was 3.35 (SD = 0.76). The findings based on the CBI, DASS-21 and BRS are displayed in Table 2 . Table 2 Prevalence of different levels of burnout, depression, anxiety, stress, and resilience in the study population Outcome variable Severity or level (score range) n % Personal burnout No/Low (0–50) 234 73.6 Moderate (50-74.9) 68 21.4 High (75-99.9) 9 2.8 Severe (100) 7 2.2 Work-related burnout No/Low (0–50) 277 87.1 Moderate (50-74.9) 38 11.9 High (75-99.9) 3 0.9 Client-related burnout No/Low (0–50) 292 92.1 Moderate (50-74.9) 23 7.3 High (75-99.9) 1 0.3 Severe (100) 1 0.3 Depression Normal (0–9) 257 81.8 Mild (10–13) 35 11.1 Moderate (14–20) 16 5.1 Severe (21–27) 3 1 Extremely severe (28+) 3 1 Anxiety Normal (0–3) 241 76.8 Mild (4–5) 11 3.5 Moderate (6–7) 45 14.3 Severe (8–9) 9 2.9 Extremely severe (10+) 8 2.5 Stress Normal (0–7) 269 85.7 Mild (8–9) 27 8.6 Moderate (10–12) 12 3.8 Severe (13–16) 6 1.9 Resilience Low (1-2.99) 81 25.6 Normal (3-4.3) 210 66.2 High (4.31-5) 26 8.2 Correlations between CBI, DASS-21 and BRS Scores Correlations between the CBI, DASS-21 and BRS scores are shown in Fig. 1 . Each burnout domain (PB, WRB and CRB) correlated positively with depression (r = 0.25 to 0.44, p < 0.001), anxiety (r = 0.31 to 0.4, p < 0.001) and stress (r = 0.27 to 0.37, p < 0.001). Burnout domains negatively correlated with resilience, with the strongest correlation observed for PB (r=-0.53, p < 0.001). Resilience correlated negatively with depression, anxiety and stress (r=-0.24 to -0.3). Prevalences of Depression, Anxiety, Stress and Burnout Domains across Resilience Groups Table 3 shows the prevalence of depression, anxiety, stress and burnout domains in the three resilience groups: low, normal and high (based on the BRS cut-off values). For all outcomes except CRB, there was a statistically significant difference in the outcome across the resilience groups with the highest prevalence observed in the low resilience group. Among those with low resilience, a remarkably high prevalence of PB (53.1%) was observed, as compared to other outcomes. Table 3 Rates of depression, anxiety, and stress across resilience groups Outcomes Resilience groups Significance a Low N (%) Normal N (%) High N (%) PB None/Low 37 (46.9) 170 (81) 25 (96.2) P < 0.001 Moderate or higher 43 (53.1) 40 (19) 1 (3.8) WRB None/Low 64 (79) 188 (89.5) 24 (92.3) P = 0.046 Moderate or higher 17 (21) 22 (10.5) 2 (7.7) CRB None/Low 72 (88.9) 196 (93.3) 24 (92.3) P = 0.466 Moderate or higher 9 (11.1) 14 (6.7) 2 (7.7) Depression Normal 55 (68.7) 178 (85.6) 23 (92) P = 0.002 Mild or higher 25 (31.3) 30 (14.4) 2 (8) Anxiety Normal 50 (62.5) 69 (81.2) 22 (88) P = 0.002 Mild or higher 30 (37.5) 39 (18.8) 3 (12) Stress Normal 59 (74.7) 187 (89.9) 22 (88) P = 0.003 Mild or higher 21 (26.3) 21 (10.1) 3 (12) Note: PB = personal burnout; WRB = work-related burnout; CRB = client-related burnout. a P-value is based on Fisher’s exact test Moderating Role of Resilience on the Effects of Burnout on Depression, Anxiety, and Stress Moderation analyses explored resilience as a potential moderator of the relationship between each burnout domain and depression, anxiety, and stress. Resilience (BRS score) significantly moderated the relationship between CRB and depression (B=-0.51, SE = 0.026, t=-1.98, p = 0.0488), and between CRB and anxiety (B=-0.676, SE = 0.0246, t=-2.75, p = 0.0064); higher levels of resilience dampened the effects of CRB on depression and anxiety. Resilience similarly moderated the effect of WRB on anxiety (B=-0.455, SE = 0.0215, t=-2.11, p = 0.0353), with higher levels of resilience dampening the effect of WRB on anxiety. The effects of CRB on depression and anxiety, and the effect of WRB on anxiety were significant at low, medium and high levels of resilience. These moderating effects are illustrated in Figs. 2 , 3 and 4 showing the slopes for the three resilience groups. Contrary to expectation, resilience was observed to accentuate the effect of PB on depression (B = 0.0662, SE = 0.0445, t = 4.56, p < 0.001), anxiety (B = 0.057, SE = 0.0136, t = 4.18, p < 0.001) and stress (B = 0.0725, SE = 0.0173, t = 4.2, p < 0.001). The effects of PB on depression and stress were found to be non-significant at the lower end of resilience, whereas the effect of PB on anxiety was significant at each level of resilience. Discussion In this sample of HCWs in Sri Lanka, more than a quarter (26.4%) had moderate or higher levels of PB during the third wave of the COVID-19 pandemic. The prevalence of WRB (12.9%) and CRB (7.9%) were relatively lower. All three burnout domains correlated positively with depression, anxiety, and stress. Resilience correlated inversely with all CBI and DASS-21 scores. A moderator analysis showed that higher levels of resilience dampened the effects of CRB on depression and anxiety, and WRB on anxiety. Higher rates of PB, WRB and CRB than observed in our sample have been reported among HCWs elsewhere. A meta-analysis of 14 studies on burnout among midwives revealed rates of 50% for PB, 40% for WRB, and 10% for CRB(Suleiman-Martos et al., 2020). One reason for the relatively lower rates in the present study could be because this was conducted during the third wave of the pandemic, and by then, HCWs may have adjusted to the pandemic-related changes. However, burnout rates lower than reported here have also been observed in India (Yella & Dmello, 2022). Although significant correlations of burnout with depression, anxiety and stress were observed similar to previous studies (Creedy et al., 2017), the strength of these correlations was modest. This supports the view that, although burnout overlaps with depression, anxiety and stress, they are distinct constructs. The direction of the association between burnout and depression is debatable, but it is plausible that in the context of the COVID-19 pandemic, burnout would have been an important contributor to depression among HCWs. The inverse correlation between resilience and burnout in our study suggests that higher resilience may have protected HCWs against burnout. Significant negative correlations between resilience and burnout have been previously reported(Guo et al., 2018; McCain et al., 2018). In our study, the proportion of participants with low and high resilience, respectively, were 25.6% and 8.2%. For comparison, among HCWs in Malaysia during the pandemic, the corresponding rates were 23.9% and 1.5%, respectively (Marzo et al., 2022). Our findings partly supported the hypothesis that resilience buffers against the psychological sequelae of burnout. This was suggested by the moderating effect of resilience on the relationship between CRB and depression/anxiety, and between WRB and anxiety. Comparable literature is limited, but one previous study showed a buffering role of resilience against the psychological effects of burnout(García-Izquierdo et al., 2018). Conversely, we found that the effects of PB on depression, anxiety and stress were not dampened by resilience, but instead, increased. This means that’s the PB-outcome relationship is stronger in the high resilience group, and vice versa. The reasons for this are unclear. It may be because those with low resilience became depressed, anxious, and stressed due to reasons other than job-related emotional exhaustion, making the PB-outcome correlation weaker in this group. As resilience is a buffer against burnout itself, it may have distorted the moderation model that was studied. There may have been many other moderators and meditators operating on the relationship between burnout and mental health sequelae. Future research should attempt to unravel these complex interactions, preferably using longitudinal study designs. There is a need to implement strategies to tackle burnout and enhance mental wellbeing. Interventions should include practices that enhance resilience. Currently, most healthcare institutions in Sri Lanka do not routinely offer programs to mitigate burnout or promote mental wellbeing among the staff. Policy-makers should, therefore, make it a priority to introduce culturally acceptable workplace interventions such as mindfulness-based interventions (Cohen et al., 2023) to staff of hospitals and MOH offices. Limitations The findings are cross-sectional in nature, and therefore, no causal inferences can be made regarding the associations observed here. Assessments are based on self-report scales, which may be biased. The sample consists of a heterogeneous group of professionals but the composition of the sample probably does not follow the composition of HCWs in Sri Lanka. There was a preponderance of females in the sample, again limiting the generalizability of results. The convenience sampling method may have introduced a selection bias. The relatively small sample size limited the statistical power of the analyses conducted. Declarations Acknowledgements: We thank all the healthcare workers who participated in the study. Funding: This research was conducted under a grant received from the University of Kelaniya. Competing interests: The authors declare that they have no competing interests. Authors' contributions: The study was conceptualized by AH, RF, AB, RW and LK. AH contributed to funding acquisition and project administration. LK and RW provided supervision. RF, IP, KHM, FJ, and TG contributed to the investigation process. Data curation was done by RF. Formal analysis was performed by AB and checked by RW. AB wrote the original draft. All authors were involved in reviewing and editing the manuscript. Ethics approval: Ethics approval was obtained from the Ethics Review Committee of Faculty of Medicine, University of Kelaniya. Consent to participate: Informed written consent was obtained from each participant. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 21 Nov, 2024 Reviews received at journal 18 Nov, 2024 Reviewers agreed at journal 08 Nov, 2024 Reviews received at journal 31 Oct, 2024 Reviewers agreed at journal 31 Oct, 2024 Reviewers invited by journal 22 Oct, 2024 Editor assigned by journal 07 Oct, 2024 Submission checks completed at journal 06 Oct, 2024 First submitted to journal 15 Sep, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5091541","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":380861352,"identity":"31d0f6ed-7928-41f7-a870-c90de0385400","order_by":0,"name":"Anuradha Baminiwatta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACAyA+ACYZGBgfNhRAGAcbiNHCw8bAbNgA0ctAUAsYALWwSRKlxZz9dOLhgoLD8vbyzc8qZxjcYeBvP8BwcAYeLZY9uRsOzzA4bNjDxmZ2c4PBMwaJMwkMBzfgc9gBoBYeg9uMPWwMZjcfGBxmYLgBdNgDfFrOvwVrse9hY/9WCNIiT1DLDYgtiT1sPGaMG4BaDEBa8DnMcgbYlv/JPcdyiiWBnuIxPJPYgNf75vy5mz/z/EmzbW8+vvFjT8VhObnjhw8+7MGjBQPwACO/gRQNo2AUjIJRMAqwAAA8IlbPob+GvAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Kelaniya","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Anuradha","middleName":"","lastName":"Baminiwatta","suffix":""},{"id":380861353,"identity":"7bb923d1-d8cd-4370-992d-d4632df6dc6d","order_by":1,"name":"Roshan Fernando","email":"","orcid":"","institution":"University of Kelaniya","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Roshan","middleName":"","lastName":"Fernando","suffix":""},{"id":380861354,"identity":"18dc1792-b1d1-41fd-ae2f-c9ec57d6be1b","order_by":2,"name":"Thanabalasingam Gadambanathan","email":"","orcid":"","institution":"Teaching Hospital Batticaloa","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Thanabalasingam","middleName":"","lastName":"Gadambanathan","suffix":""},{"id":380861355,"identity":"fbc8795d-2155-4022-82e1-ada3bda37ea0","order_by":3,"name":"Fathima Jiyatha","email":"","orcid":"","institution":"Eastern University of Sri Lanka","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fathima","middleName":"","lastName":"Jiyatha","suffix":""},{"id":380861356,"identity":"1e447fc1-c081-4369-9a57-e8337f19a3e6","order_by":4,"name":"Kadheeja Haniya Maryam","email":"","orcid":"","institution":"University of Kelaniya","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kadheeja","middleName":"Haniya","lastName":"Maryam","suffix":""},{"id":380861357,"identity":"2f6b4f18-21f9-4f71-a3e2-de9025bb5b44","order_by":5,"name":"Imalsha Premaratne","email":"","orcid":"","institution":"University of Kelaniya","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Imalsha","middleName":"","lastName":"Premaratne","suffix":""},{"id":380861358,"identity":"17771298-cc54-49aa-be3c-707fb86f1efc","order_by":6,"name":"Lalith Kuruppuarachchi","email":"","orcid":"","institution":"University of Kelaniya","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lalith","middleName":"","lastName":"Kuruppuarachchi","suffix":""},{"id":380861359,"identity":"0b8307ae-4152-46fc-a5ad-a6f83de29caf","order_by":7,"name":"Rajitha Wickremasinghe","email":"","orcid":"","institution":"University of Kelaniya","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rajitha","middleName":"","lastName":"Wickremasinghe","suffix":""},{"id":380861360,"identity":"9fb3c907-ffdc-4839-91b4-cb7b5ab8d02f","order_by":8,"name":"Aruni Hapangama","email":"","orcid":"","institution":"University of Kelaniya","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aruni","middleName":"","lastName":"Hapangama","suffix":""}],"badges":[],"createdAt":"2024-09-15 06:51:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5091541/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5091541/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71804117,"identity":"9e0a21cf-9b8d-4911-88fc-dd010325317c","added_by":"auto","created_at":"2024-12-18 17:15:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21478,"visible":true,"origin":"","legend":"\u003cp\u003ePearson correlation coefficient matrix of the study variables\u003c/p\u003e","description":"","filename":"Onlinefloatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-5091541/v1/d4491400e58f2b5307a673c9.png"},{"id":71804922,"identity":"91e8f08c-2f1d-444b-9b15-8f53be4e9a8d","added_by":"auto","created_at":"2024-12-18 17:23:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":12995,"visible":true,"origin":"","legend":"\u003cp\u003eModerating role of resilience on the relationship between client-related burnout (CRB) and depression\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5091541/v1/6426364d85eda9eae1396f0e.png"},{"id":71804118,"identity":"fb153db9-117b-40e1-9cc4-e590967f39b3","added_by":"auto","created_at":"2024-12-18 17:15:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13957,"visible":true,"origin":"","legend":"\u003cp\u003eModerating role of resilience on the relationship between client-related burnout (CRB) and anxiety\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5091541/v1/c0bd389d819a1b60d37e74f2.png"},{"id":71804121,"identity":"de22b4a2-b270-4dd6-b27b-a0bb3394f834","added_by":"auto","created_at":"2024-12-18 17:15:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14111,"visible":true,"origin":"","legend":"\u003cp\u003eModerating role of resilience on the relationship between work-related burnout (WRB) and anxiety\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5091541/v1/c5f148d492f6bba570f2657e.png"},{"id":71805787,"identity":"2594b7b9-90b3-4af2-8ef2-b38a1ffae5c5","added_by":"auto","created_at":"2024-12-18 17:31:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":747412,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5091541/v1/6ac25cf9-cf35-40f8-8d23-5c9e59a4823f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Buffering Role of Resilience on Burnout, Depression, Anxiety, and Stress among Healthcare Workers in Sri Lanka","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBurnout has been defined as a psychological syndrome characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment (Maslach et al., 1997). It has diverse consequences on individuals, societies, and health systems (Salvagioni et al., 2017). Psychological consequences of burnout include poor concentration and memory, anxiety, depression, suicidal behaviour, insomnia, and irritability (Salvagioni et al., 2017). Positive psychological attributes such as resilience, optimism and self-efficacy may protect individuals against such negative mental health sequelae(Ed\u0026uacute;-valsania et al., 2022).\u003c/p\u003e \u003cp\u003eUnderstanding burnout among healthcare workers (HCWs) became important during the COVID-19 pandemic. A meta-analysis reported a pooled prevalence of 37% for burnout, and 33%, 42% and 40% respectively for depressive, anxiety, and stress, among nurses during the pandemic (Aymerich et al., 2022). In Sri Lanka, depression, anxiety, and stress were observed to be notably high among HCWs during that time (Baminiwatta et al., 2021).\u003c/p\u003e \u003cp\u003eWhile some authors suggest that burnout and depression are two distinct constructs, others argue it is merely a form of work-related depression (Bianchi et al., 2015; Schonfeld, 1991). Some researchers have suggested that burnout is a causal antecedent of depression and not vice versa, based on longitudinal observations (Hakanen \u0026amp; Schaufeli, 2012). Other authors contend that the relationship is bi-directional (Bianchi et al., 2015). Conceptual overlaps of burnout with anxiety and stress have also been described(Golonka et al., 2019; Parker \u0026amp; Tavella, 2022).\u003c/p\u003e \u003cp\u003eResilience is the capacity for adaptation and \u0026ldquo;bouncing back\u0026rdquo; in the face of adversity(Windle, 2011). It offers protection against mental health problems as well as burnout (Guo et al., 2018; Hu et al., 2015; McCain et al., 2018). Resilience is currently understood as a modifiable trait, and interventions enhancing resilience may alleviate HCW burnout (Cohen et al., 2023). Whether resilience plays a buffering role against the negative psychological effects of burnout such as depression, anxiety and stress has not been widely studied.\u003c/p\u003e \u003cp\u003eThis study thus aimed to explore the relationship between burnout, depression, anxiety, stress, and resilience among HCWs in Sri Lanka. We also hypothesize that burnout is an antecedent of depression, anxiety and stress among HCWs in the context of a pandemic, and that resilience moderates these relationships.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design, Setting and Participants\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional study conducted among HCWs belonging to any professional category (nurses, midwives, doctors, public health inspectors, allied health professionals and healthcare assistants) employed in government hospitals and Medical Officer of Health (MOH) offices in two districts - Gampaha and Batticaloa. The study was conducted in October-December 2021, i.e., during the third wave of the COVID-19 pandemic. Convenience sampling was used.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eCopenhagen Burnout Inventory (CBI)\u003c/p\u003e \u003cp\u003eCBI is a 19-item survey with positively and negatively framed items that cover 3 domains of burnout: personal burnout (PB; 6 items), work-related burnout (WRB; 7 items) and client-related burnout (CRB; 6 items) (Kristensen et al., 2005). The responses to all items are rescaled to a 0-100 metric. Scores are calculated for each domain as the mean of the respective items. Scores of less than 50 are considered as \u0026lsquo;no/low\u0026rsquo; burnout, 50 to 74.9 as \u0026lsquo;moderate\u0026rsquo;, 75 to 99.9 as \u0026lsquo;high\u0026rsquo;, and a score of 100 is considered \u0026lsquo;severe\u0026rsquo; burnout (Creedy et al., 2017). CBI has been previously translated and validated into Sinhala among public health midwives (PHMs) (Pathiraja, 2011). The scale was translated into Tamil using standard procedures prior to this study. Confirmatory factor analysis affirmed the three-factor structure of the Tamil CBI in the present sample with excellent model fit [CFI\u0026thinsp;=\u0026thinsp;0.996, TLI\u0026thinsp;=\u0026thinsp;0.996, RMSEA\u0026thinsp;=\u0026thinsp;0.051, SRMR\u0026thinsp;=\u0026thinsp;0.069), and the subscales demonstrated good internal consistency (alpha\u0026thinsp;=\u0026thinsp;0.83\u0026ndash;0.89).\u003c/p\u003e \u003cp\u003eBrief Resilience Scale (BRS)\u003c/p\u003e \u003cp\u003eThe BRS is a 6-item instrument developed for measuring resilience (Smith et al., 2008). It intends to assess resilience in its most basic meaning\u0026mdash;the ability to \u0026ldquo;bounce back.\u0026rdquo; BRS was translated and validated in Sinhala and Tamil languages prior to the present study (Baminiwatta et al., 2023). Resilience score is the average of the six item scores, and it can be interpreted as low (1\u0026ndash;2.99), normal (3\u0026ndash;4.30), and high resilience (4.31\u0026ndash;5)(Windle et al., 2022).\u003c/p\u003e \u003cp\u003eDepression, Anxiety and Stress Scale (DASS)-21\u003c/p\u003e \u003cp\u003eThis 21-item self-administered scale includes three subscales assessing depression, anxiety, and stress separately, as experienced during the past week (Lovibond \u0026amp; Lovibond, 1995). Subscale scores are generated by summing the scores of items in each subscale, and multiplying by two. DASS-21 has been culturally adapted and validated into Sinhala (Rekha, 2012).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData analysis was performed using IBM SPSS Version 26. Pearson correlation among continuous parameters were tested. Simple moderation models were tested using the PROCESS Macro on SPSS. As a post-hoc analysis, cluster analysis was performed using hierarchical agglomerative cluster analysis followed by K-means clustering to identify profiles of burnout, resilience, and DASS-21 scores. Dendrograms were inspected to determine the number of clusters.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e Ethics approval\u003c/strong\u003e was obtained from the Ethics Review Committee of [Anonymized for Peer Review]. Informed written consent was obtained from each participant. Any participant who actively reported mental health problems to the researchers were offered a referral to mental health services.\u003c/p\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the Sample\u003c/h2\u003e \u003cp\u003eA total of 318 participants responded (208 from Gampaha and 110 from Batticaloa district). Respondents were predominantly female (83.3%). The sample consisted of HCWs employed in both the public health (56.9%) and hospital-based (43.1%) health services. Public health midwives (37.1%) and hospital nurses (26.7%) predominated in the sample. The majority (89.6%) had been performing COVID-19-related duties. The sample characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the sample (n\u0026thinsp;=\u0026thinsp;318)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;30\u003c/p\u003e \u003cp\u003e31\u0026ndash;40\u003c/p\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003cp\u003e51\u0026ndash;60\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003cp\u003e89\u003c/p\u003e \u003cp\u003e95\u003c/p\u003e \u003cp\u003e66\u003c/p\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003cp\u003e28\u003c/p\u003e \u003cp\u003e29.9\u003c/p\u003e \u003cp\u003e20.8\u003c/p\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeparated/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfession\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHospital Nurse\u003c/p\u003e \u003cp\u003ePublic Health Nurse\u003c/p\u003e \u003cp\u003ePublic Health Midwife\u003c/p\u003e \u003cp\u003eMedical officer (hospital-based)\u003c/p\u003e \u003cp\u003eMedical officer (public health)\u003c/p\u003e \u003cp\u003ePublic Health Inspector\u003c/p\u003e \u003cp\u003eAllied Health Professional\u003c/p\u003e \u003cp\u003eSupportive staff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e118\u003c/p\u003e \u003cp\u003e24\u003c/p\u003e \u003cp\u003e9\u003c/p\u003e \u003cp\u003e28\u003c/p\u003e \u003cp\u003e29\u003c/p\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003cp\u003e2.8\u003c/p\u003e \u003cp\u003e37.1\u003c/p\u003e \u003cp\u003e7.5\u003c/p\u003e \u003cp\u003e2.8\u003c/p\u003e \u003cp\u003e8.8\u003c/p\u003e \u003cp\u003e9.1\u003c/p\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRs. \u0026lt;50000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRs. 50000\u0026ndash;100000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt; Rs. 100000\u003c/p\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of service\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than 1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMore than 10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaving a chronic medical condition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003cp\u003e277\u003c/p\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.6\u003c/p\u003e \u003cp\u003e87.1\u003c/p\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerforming COVID-19-related duties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e285\u003c/p\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89.6\u003c/p\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTested positive for COVID-19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003cp\u003e82.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWearing Personal Protective Equipment at work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003cp\u003eNo\u003c/p\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e229\u003c/p\u003e \u003cp\u003e87\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003cp\u003e27.4\u003c/p\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBurnout, Mental Health Problems and Resilience\u003c/h3\u003e\n\u003cp\u003eBased on the CBI domains, moderate or higher degrees of PB, WRB and CRB were reported by 26.4% [95% CI\u0026thinsp;=\u0026thinsp;21.6%, 31.2%], 12.9% [95% CI\u0026thinsp;=\u0026thinsp;9.22%, 16.6%] and 7.9% [95% CI\u0026thinsp;=\u0026thinsp;4.9%, 10.9%], of the participants, respectively. In the sample, 68 (21.4%) had moderate or higher burnout in only one domain; 23 (7.2%) in two domains; and 12 (3.8%) in all three domains; 103 participants (32.4%) had moderate or higher burnout on at least one domain. The respective mean (SD) scores on the PB, WRB and CRB subscales were 36.4 (20.9), 26.9 (17.5) and 26.1 (14.7).\u003c/p\u003e \u003cp\u003eThe prevalence of depression, anxiety, and stress according to the DASS-21 cut-off values (including cases with mild, moderate, severe and extremely severe states) were 18.2%, 23.2% and 14.3%, respectively. According to the BRS, the majority (66.2%) had \u0026lsquo;normal resilience\u0026rsquo;, whereas 25.6% had \u0026lsquo;low resilience\u0026rsquo;; the mean resilience score (on a scale of 1 to 5) was 3.35 (SD\u0026thinsp;=\u0026thinsp;0.76). The findings based on the CBI, DASS-21 and BRS are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of different levels of burnout, depression, anxiety, stress, and resilience in the study population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeverity or level (score range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersonal burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo/Low (0\u0026ndash;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (50-74.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (75-99.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork-related burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo/Low (0\u0026ndash;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (50-74.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (75-99.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClient-related burnout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo/Low (0\u0026ndash;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (50-74.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (75-99.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal (0\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild (10\u0026ndash;13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (14\u0026ndash;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere (21\u0026ndash;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtremely severe (28+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal (0\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild (4\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (6\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere (8\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtremely severe (10+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal (0\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild (8\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate (10\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere (13\u0026ndash;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResilience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (1-2.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal (3-4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (4.31-5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eCorrelations between CBI, DASS-21 and BRS Scores\u003c/h3\u003e\n\u003cp\u003eCorrelations between the CBI, DASS-21 and BRS scores are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Each burnout domain (PB, WRB and CRB) correlated positively with depression (r\u0026thinsp;=\u0026thinsp;0.25 to 0.44, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), anxiety (r\u0026thinsp;=\u0026thinsp;0.31 to 0.4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and stress (r\u0026thinsp;=\u0026thinsp;0.27 to 0.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Burnout domains negatively correlated with resilience, with the strongest correlation observed for PB (r=-0.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Resilience correlated negatively with depression, anxiety and stress (r=-0.24 to -0.3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrevalences of Depression, Anxiety, Stress and Burnout Domains across Resilience Groups\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the prevalence of depression, anxiety, stress and burnout domains in the three resilience groups: low, normal and high (based on the BRS cut-off values). For all outcomes except CRB, there was a statistically significant difference in the outcome across the resilience groups with the highest prevalence observed in the low resilience group. Among those with low resilience, a remarkably high prevalence of PB (53.1%) was observed, as compared to other outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRates of depression, anxiety, and stress across resilience groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eResilience groups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificance\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (96.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWRB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e188 (89.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone/Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196 (93.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.466\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (68.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178 (85.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69 (81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (74.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187 (89.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMild or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: PB\u0026thinsp;=\u0026thinsp;personal burnout; WRB\u0026thinsp;=\u0026thinsp;work-related burnout; CRB\u0026thinsp;=\u0026thinsp;client-related burnout.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003eP-value is based on Fisher\u0026rsquo;s exact test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModerating Role of Resilience on the Effects of Burnout on Depression, Anxiety, and Stress\u003c/h2\u003e \u003cp\u003eModeration analyses explored resilience as a potential moderator of the relationship between each burnout domain and depression, anxiety, and stress. Resilience (BRS score) significantly moderated the relationship between CRB and depression (B=-0.51, SE\u0026thinsp;=\u0026thinsp;0.026, t=-1.98, p\u0026thinsp;=\u0026thinsp;0.0488), and between CRB and anxiety (B=-0.676, SE\u0026thinsp;=\u0026thinsp;0.0246, t=-2.75, p\u0026thinsp;=\u0026thinsp;0.0064); higher levels of resilience dampened the effects of CRB on depression and anxiety. Resilience similarly moderated the effect of WRB on anxiety (B=-0.455, SE\u0026thinsp;=\u0026thinsp;0.0215, t=-2.11, p\u0026thinsp;=\u0026thinsp;0.0353), with higher levels of resilience dampening the effect of WRB on anxiety. The effects of CRB on depression and anxiety, and the effect of WRB on anxiety were significant at low, medium and high levels of resilience. These moderating effects are illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, 3 and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e showing the slopes for the three resilience groups. Contrary to expectation, resilience was observed to accentuate the effect of PB on depression (B\u0026thinsp;=\u0026thinsp;0.0662, SE\u0026thinsp;=\u0026thinsp;0.0445, t\u0026thinsp;=\u0026thinsp;4.56, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), anxiety (B\u0026thinsp;=\u0026thinsp;0.057, SE\u0026thinsp;=\u0026thinsp;0.0136, t\u0026thinsp;=\u0026thinsp;4.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and stress (B\u0026thinsp;=\u0026thinsp;0.0725, SE\u0026thinsp;=\u0026thinsp;0.0173, t\u0026thinsp;=\u0026thinsp;4.2, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The effects of PB on depression and stress were found to be non-significant at the lower end of resilience, whereas the effect of PB on anxiety was significant at each level of resilience.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this sample of HCWs in Sri Lanka, more than a quarter (26.4%) had moderate or higher levels of PB during the third wave of the COVID-19 pandemic. The prevalence of WRB (12.9%) and CRB (7.9%) were relatively lower. All three burnout domains correlated positively with depression, anxiety, and stress. Resilience correlated inversely with all CBI and DASS-21 scores. A moderator analysis showed that higher levels of resilience dampened the effects of CRB on depression and anxiety, and WRB on anxiety.\u003c/p\u003e \u003cp\u003eHigher rates of PB, WRB and CRB than observed in our sample have been reported among HCWs elsewhere. A meta-analysis of 14 studies on burnout among midwives revealed rates of 50% for PB, 40% for WRB, and 10% for CRB(Suleiman-Martos et al., 2020). One reason for the relatively lower rates in the present study could be because this was conducted during the third wave of the pandemic, and by then, HCWs may have adjusted to the pandemic-related changes. However, burnout rates lower than reported here have also been observed in India (Yella \u0026amp; Dmello, 2022).\u003c/p\u003e \u003cp\u003eAlthough significant correlations of burnout with depression, anxiety and stress were observed similar to previous studies (Creedy et al., 2017), the strength of these correlations was modest. This supports the view that, although burnout overlaps with depression, anxiety and stress, they are distinct constructs. The direction of the association between burnout and depression is debatable, but it is plausible that in the context of the COVID-19 pandemic, burnout would have been an important contributor to depression among HCWs.\u003c/p\u003e \u003cp\u003eThe inverse correlation between resilience and burnout in our study suggests that higher resilience may have protected HCWs against burnout. Significant negative correlations between resilience and burnout have been previously reported(Guo et al., 2018; McCain et al., 2018). In our study, the proportion of participants with low and high resilience, respectively, were 25.6% and 8.2%. For comparison, among HCWs in Malaysia during the pandemic, the corresponding rates were 23.9% and 1.5%, respectively (Marzo et al., 2022).\u003c/p\u003e \u003cp\u003eOur findings partly supported the hypothesis that resilience buffers against the psychological sequelae of burnout. This was suggested by the moderating effect of resilience on the relationship between CRB and depression/anxiety, and between WRB and anxiety. Comparable literature is limited, but one previous study showed a buffering role of resilience against the psychological effects of burnout(Garc\u0026iacute;a-Izquierdo et al., 2018). Conversely, we found that the effects of PB on depression, anxiety and stress were not dampened by resilience, but instead, increased. This means that\u0026rsquo;s the PB-outcome relationship is stronger in the high resilience group, and vice versa. The reasons for this are unclear. It may be because those with low resilience became depressed, anxious, and stressed due to reasons other than job-related emotional exhaustion, making the PB-outcome correlation weaker in this group. As resilience is a buffer against burnout itself, it may have distorted the moderation model that was studied. There may have been many other moderators and meditators operating on the relationship between burnout and mental health sequelae. Future research should attempt to unravel these complex interactions, preferably using longitudinal study designs.\u003c/p\u003e \u003cp\u003eThere is a need to implement strategies to tackle burnout and enhance mental wellbeing. Interventions should include practices that enhance resilience. Currently, most healthcare institutions in Sri Lanka do not routinely offer programs to mitigate burnout or promote mental wellbeing among the staff. Policy-makers should, therefore, make it a priority to introduce culturally acceptable workplace interventions such as mindfulness-based interventions (Cohen et al., 2023) to staff of hospitals and MOH offices.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe findings are cross-sectional in nature, and therefore, no causal inferences can be made regarding the associations observed here. Assessments are based on self-report scales, which may be biased. The sample consists of a heterogeneous group of professionals but the composition of the sample probably does not follow the composition of HCWs in Sri Lanka. There was a preponderance of females in the sample, again limiting the generalizability of results. The convenience sampling method may have introduced a selection bias. The relatively small sample size limited the statistical power of the analyses conducted.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe thank all the healthcare workers who participated in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was conducted under a grant received from the University of Kelaniya.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e The study was conceptualized by AH, RF, AB, RW and LK. AH contributed to funding acquisition and project administration. LK and RW provided supervision. RF, IP, KHM, FJ, and TG contributed to the investigation process. Data curation was done by RF. Formal analysis was performed by AB and checked by RW. AB wrote the original draft. All authors were involved in reviewing and editing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eEthics approval was obtained from the Ethics Review Committee of Faculty of Medicine, University of Kelaniya.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eInformed written consent was obtained from each participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eData that support the findings of this study are available in Figshare with the identifier https://doi.org/10.6084/m9.figshare.24968769\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAymerich, C., Pedruzo, B., P\u0026eacute;rez, J. 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Burnout and sleep quality among community health workers during the pandemic in selected city of Andhra Pradesh. \u003cem\u003eClinical Epidemiology and Global Health\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e, 101109. https://doi.org/10.1016/j.cegh.2022.101109\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"burnout, depression, healthcare worker, resilience, anxiety, work stress","lastPublishedDoi":"10.21203/rs.3.rs-5091541/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5091541/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to explore the relationship between burnout, depression, anxiety, stress, and resilience among healthcare workers (HCWs), and to investigate the moderating role of resilience against the mental health correlates of burnout. For this purpose, using a cross-sectional design, 318 HCWs from various categories (nurses, midwives, doctors, etc.) recruited from hospital and community settings in two Districts of Sri Lanka during the COVID-19 pandemic were surveyed using the Copenhagen Burnout Inventory (CBI), Depression, Anxiety and Stress Scale (DASS-21), and the Brief Resilience Scale. CBI assessed three domains: personal burnout (PB), work-related burnout (WRB) and client-related burnout (CRB). The prevalence of PB, WRB and CRB were 26.4%, 12.9%, and 7.9%, respectively. The prevalence of low, normal, and high resilience was 25.6%, 66.2%, and 8.6%, respectively. All three burnout domains correlated positively with depression, anxiety, and stress. Resilience correlated inversely with all CBI and DASS-21 scores. In moderator analysis, higher levels of resilience dampened the effects of CRB on depression and anxiety, and WRB on anxiety, but resilience moderated the psychological consequences of PB in the opposite direction. Our findings indicate notable rates of burnout among Sri Lankan HCWs, and suggest a possible buffering role of resilience against the psychological consequences of burnout.\u003c/p\u003e","manuscriptTitle":"The Buffering Role of Resilience on Burnout, Depression, Anxiety, and Stress among Healthcare Workers in Sri Lanka","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 17:15:12","doi":"10.21203/rs.3.rs-5091541/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-21T09:53:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-18T23:06:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328974307495712218746444467080300249999","date":"2024-11-08T14:05:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-01T00:19:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281312076218629700908786744714725859318","date":"2024-10-31T16:09:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-22T15:02:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-07T10:10:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-07T03:46:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Psychology","date":"2024-09-15T06:48:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discpsy","sideBox":"Learn more about [Discover Psychology](https://www.springer.com/44202)","snPcode":"","submissionUrl":"","title":"Discover Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6a46df1e-f6cb-41da-b7f3-440efda88f40","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-03-24T09:53:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-18 17:15:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5091541","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5091541","identity":"rs-5091541","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}