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Burnout and sleep quality among community health workers during the pandemic in selected city of Andhra Pradesh

Open AccessPublished:July 11, 2022DOI:https://doi.org/10.1016/j.cegh.2022.101109

      Abstract

      Background

      This pandemic has resulted in physical and emotional exhaustion among everyone, especially among community healthcare workers (CHWs), resulting in increased burnout and poor sleep quality. This pandemic has increased responsibilities for Accredited Social Health Activist (ASHA) workers and Auxiliary Nurse Midwife (ANM) at the grassroots. Previous studies have shown that infectious diseases like SARS and MERS directly affect sleep. With the COVID-19 pandemic, burnout and poor sleep among healthcare workers are expected to increase.

      Aim

      To determine the prevalence of burnout and sleep quality among community health workers

      Settings

      and Design: This Cross-sectional study was conducted from February to April 2020 in Urban Primary Health care centres of Guntur city, Andhra Pradesh.

      Methods and material

      Copenhagen Burnout Inventory scale (CBI) with a 5-point Likert scale and Pittsburgh Sleep Quality Index (PSQI) Questionnaire were used among four hundred and ten study participants. Descriptive statistics and the Chi-square test were used; a p-value of ≤0.05 is considered significant.

      Results

      The prevalence of personal burnout was 16.8%, while work-related and pandemic-related burnouts were 10.5% and 25.4%, respectively. The prevalence of poor sleep quality was 35.09%. Fear of contracting the virus was present among 38.3%. Fear of infecting the family members because of their exposure was reported by 36.6% of the respondents; 71.7% and 79.3% reported receiving support from the organisation and colleagues, respectively.

      Conclusions

      The provision of necessary equipment, regular check-ups and timely interventions will minimise the risk of stress and burnout.

      Keywords

      1. Introduction

      Community Health Workers (CHW) are the community members chosen to work closely with the people supported by the health system but are not necessarily a part of the organisation. The main advantage of having a CHW is that the people easily accept them. In India, the concept of CHW has a long and rich history; there are four cadres for CHW: ASHA (Accredited Social Health Activist), ANM (Auxiliary nurse midwife), Anganwadi worker and multi-purpose health assistant.
      • Lehmann Uta
      • Sanders David
      A report on Community health workers: WHO [Internet].
      CHW are assigned various responsibilities under national health programs. With the declaration of the COVID-19 pandemic, additional duties are assigned to ASHA workers and ANM, such as contact tracing, community surveillance, implementing home quarantine, and identification of high-risk groups (HRG) and probable cases.
      COVID-19 Book of Five
      Response and containment measures for ANM, ASHA, AWW[Internet].
      Burnout is the emotional and physical exhaustion when one reaches their limits. Burnout in health care professionals can be more prevalent during the pandemic because of the increased workload.
      • Queen D.
      • Harding K.
      Societal pandemic burnout: a COVID legacy.
      Good sleep, in terms of quality and quantity, is essential for optimal day-to-day functioning. Sleep problems can lead to changes in mental health, but mental health conditions can also worsen problems with sleep.
      • D C H.s S.
      • Singh A.
      Sleep pattern, sleep problems and comorbidities among resident doctors at a tertiary care institution in India: a cross-sectional study.
      It is essential to know the mental health status of the CHW and provide them with the necessary measures. There are several studies conducted on healthcare professionals (Physicians, Nurses etc.) working in a hospital, but very few on community health workers (ANM/ASHA) who were the focal point of contact to the community members during the COVID-19 pandemic. Hence, this study aims to determine the prevalence of burnout and sleep quality among community health workers during the pandemic. The study also determines the factors associated with burnout and sleep quality.

      2. Materials and methods

      This Cross-sectional study was conducted in 13 urban primary health care centres (UPHC) in Guntur city from February to April 2020. A sample size of 410 was estimated, taking the prevalence as 50% and considering a 5% non-responsive rate. ASHA workers and ANMs present on the day of data collection and those who gave consent were included in the study. The complete enumeration of CHW from all the UPHCs was done. A self-administered pre-structured questionnaire was used as a tool; it was translated to the Telugu language for easy understanding. The tool consisted of three parts: First part contains socio-demographic details (age, marital status, type of family, type of residence etc.) and work-related information (experience, hours of work, population covered, field visits and designation) of the participants.
      The second part was the Copenhagen Burnout Inventory (CBI) which consisted of 3 domains: personal burnout, work-related burnout, and pandemic-related burnout. The personal and work-related burnout were taken from CBI, pandemic related burnout was adapted from a study in India.
      • Khasne R.W.
      • Dhakulkar B.S.
      • Mahajan H.C.
      • Kulkarni A.P.
      Burnout among healthcare workers during COVID-19 pandemic in India: results of a questionnaire-based survey.
      The responses are 5-point Likert scale (1: always; 2: often; 3: sometimes; 4: seldom; 5: never). The total scores were categorised as No Burnout (less than 50), Moderate Burnout (50–74) and High Burnout (75–99). To calculate the prevalence of burnout was calculated by adding moderate and high burnout, low level was considered to be normal burnout and hence ignored. The third part of the tool was Pittsburgh Sleep Quality Index (PSQI).
      • Buysse D.J.
      • Reynolds C.F.
      • Monk T.H.
      • Berman S.R.
      • Kupfer D.J.
      The Pittsburgh Sleep Quality Index (PSQI): a new instrument for psychiatric research and practice.
      The scores of PSQI ranged from 0 to 21. As there are no standard cut-off values to determine the level of sleep quality, quartiles were used to classify the same. Scores below 25th, 25th to 75th and above 75th percentile were considered to categorise sleep quality into good, moderate, and poor, respectively. A score below 6 as good sleep quality, 6–8 as moderate sleep quality, and above 9 as poor sleep quality.
      A pilot study was conducted before the original research in a similar survey setting to check the feasibility of the study and reliability of the study tools. The Institutional Ethics Committee approved the study, and formal approval was taken from the District Health Officer of the study area. The data were coded, entered, and analysed using SPSS (Statistical Package for Social Science) version 15. Socio-demographic variables were described as frequency and percentage. To find the association Chi-square test was used, level of p ≤ 0.05 was taken as statistically significant.

      3. Results

      A total of 410 CHWs participated in the study, out of which 221 were ASHAs, and 189 were ANMs. Table 1 represents the distribution of respondents based on socio-demographic and work-related characteristics. The mean age of study participants is 34 ± 5 years, and all the participants were females. The majority, 305 (74.4%) of the study participants) live in a nuclear family; 360 (87.8%) were married. Among married, 261 (69.4%) had more than one child. The level of education for ANMs is ANM/GNM nursing for 94.7% of the respondents, whereas ten respondents have done BSC Nursing. For work experience, the mean is 6 ± 4 years. The minimum number of working hours is reported as 8 h and the maximum as 12 h; the number of weekly field visits ranges between 5 and 7 days.
      Table 1Distribution of respondents based on socio-demographic and work related characteristics.
      CharacteristicsASHA n (%)ANM n (%)Total n (%)
      Designation221 (54)189 (46)410 (100)
      Age (in years)
      <3081 (36.7)39 (20.6)120 (29.3)
      30–3563 (28.5)61 (32.3)124 (30.2)
      36–4060 (27.1)63 (33.3)123 (30.0)
      >4017 (7.7)26 (13.8)43 (10.5)
      Type of family
      Nuclear family155 (70.1)150 (79.4)305 (74.4)
      Extended family66 (29.9)39 (20.6)105 (25.6)
      Marital status
      Unmarried21 (9.5)13 (6.9)34 (8.3)
      Married196 (88.7)164 (86.8)360 (87.8)
      Widowed/Divorced4 (1.8)12 (6.3)16 (3.9)
      Number of children (n=376)
      No children11 (5.5)6 (3.4)17 (4.5)
      One57 (28.5)41 (23.3)98 (26.1)
      More than one child132 (66)129 (73.3)261 (69.4)
      Type of residence
      Rented house92 (41.6)77 (40.7)169 (41.2)
      Own house129 (58.4)112 (59.3)241 (58.8)
      Years of work experience
      <588 (39.8)127 (67.2)215 (52.4)
      5–1084 (38)33 (17.5)117 (28.5)
      >1049 (22.2)29 (15.3)78 (19.1)
      Number of working hours (in a day)
      8128 (57.9)84 (44.4)212 (51.7)
      >893 (42.1)105 (55.6)198 (48.3)
      Number of field visits (in a week)
      535 (15.8)26 (13.8)61 (14.9)
      >5186 (84.2)163 (86.2)349 (85.1)
      Total population covered
      <400076 (34.4)66 (34.9)142 (34.6)
      4000–450087 (39.4)74 (39.2)161 (39.3)
      >450058 (26.2)49 (25.9)107 (26.1)
      Table 2 represents the distribution of participants based on burnout and sleep quality. 83.2% of the respondents were classified under a low level of personal burnout, while 2.7% reported having high burnout. The pandemic had little effect on community health workers showing 24.9% had moderate pandemic-related burnout. 35.9% of the community health worker had poor sleep quality. The prevalence of personal burnout is 16.8%, work-related burnout is 10.5%, and pandemic-related burnout is 25.4%. Among ASHA workers, 21.3% had personal burnout, 12.7% had work-related burnout, and 29% had pandemic-related burnout. Among ANMs, 11.6% had personal burnout, 6.9% had work-related burnout, and 21.2% had pandemic-related burnout.
      Table 2Distribution of respondents based on burnout, and sleep quality.
      CharacteristicsASHA n (%)ANM n (%)Total n (%)
      Personal burnout
      Low174 (78.7)167 (88.4)341 (83.2)
      Moderate40 (18.1)18 (9.5)58 (14.1)
      High7 (3.2)4 (2.1)11 (2.7)
      Work related burnout
      Low193 (87.3)174 (92.1)367 (89.5)
      Moderate22 (10)15 (6.9)37 (9)
      High6 (2.7)06 (1.5)
      Pandemic related burnout
      Low157 (71)149 (78.8)306 (74.6)
      Moderate63 (28.5)39 (20.6)102 (24.9)
      High1 (0.5)1 (0.6)2 (0.5)
      Sleep Quality
      Good62 (28)46 (24.3)108 (26.3)
      Moderate85 (38.5)70 (37.1)155 (37.8)
      Poor74 (33.5)73 (38.6)147 (35.9)
      Table 3 represents the distribution of respondents according to pandemic-related burnout. The respondents feared getting infected with the COVID-19 virus for themselves and family while working during the pandemic (mean score = 50.7 ± 38.0 and 47.9 ± 38.2, respectively). The respondents reported that their colleagues supported them during the pandemic and did not fear death due to COVID-19 (mean score = 11.0 ± 25.6 and 11.8 ± 26.7, respectively).
      Table 3Distribution of Respondents according to Pandemic related burnout.
      QuestionsAlways n (%)Often n (%)Sometimes n (%)Seldom n (%)Never n (%)Mean score
      Feeling hard to work in the current scenario14 (3.4)24 (5.9)102 (24.8)148 (36.1)122 (29.8)29.2 ± 25.7
      Drain more of my energy to work during the current scenario20 (4.9)39 (9.5)86 (21)115 (28)150 (36.6)29.5 ± 29.2
      Finding fruitful while performing my work during the current scenario191 (46.6)46 (11.2)51 (12.4)45 (11)77 (18.8)36.0 ± 39.7
      Feeling of giving more than what you get back while working in the current scenario37 (9)26 (6.3)78 (19.1)76 (18.5)193 (47.1)27.9 ± 32.7
      Hesitation to work during this current scenario19 (4.6)20 (4.9)68 (16.6)68 (16.6)235 (57.3)20.7 ± 28.7
      Depressed due of the current scenario21 (5.1)19 (4.6)64 (15.6)66 (16.2)240 (58.5)20.4 ± 29.1
      Feeling that my patience is tested while working in the current scenario31 (7.6)24 (5.9)58 (14.1)72 (17.6)225 (54.8)23.4 ± 31.6
      Fear of catch of COVID-19 infection while working in the current scenario114 (27.8)43 (10.5)91 (22.2)66 (16.1)96 (23.4)50.7 ± 38.0
      Fear of family members catching COVID-19 infection because of my work exposure103 (25.1)47 (11.5)81 (19.8)71 (17.3)108 (26.3)47.9 ± 38.2
      Feel welcomed by the community because I'm HCW and working in the current scenario202 (49.3)51 (12.4)57 (13.9)28 (6.8)72 (17.6)32.4 ± 38.6
      Fear of death while working in the current scenario25 (6.1)5 (1.2)18 (4.4)44 (10.7)318 (77.6)11.8 ± 26.7
      Feel you are being properly protected by the organisation while working in the current scenario294 (71.7)59 (14.4)21 (5.1)10 (2.4)26 (6.4)14.3 ± 28.0
      Feel you are being supported by colleagues during the current scenario325 (79.3)37 (9)20 (4.9)8 (2)20 (4.8)11.0 ± 25.6
      Total mean score42 ± 13.08
      Table 4 illustrates the distribution of respondents according to personal and work-related burnout. The mean score of personal burnout is 35 ± 18; for work-related burnout, the mean score is 34 ± 15; and for pandemic-related burnout, the mean score is 42 ± 15.
      Table 4Distribution of Respondents according to Personal burnout and work related burnout.
      QuestionsAlways n (%)Often n (%)Sometimes n (%)Seldom n (%)Never n (%)Mean score
      Personal Burnout
      Feel tired11 (2.7)76 (18.5)164 (40)123 (30)36 (8.8)44.0 ± 23.5
      I'm physically exhausted10 (2.4)65 (15.9)151 (36.8)132 (32.2)52 (12.7)40.7 ± 24.3
      I'm emotionally exhausted14 (3.4)47 (11.5)118 (28.8)157 (38.3)74 (18)35.9 ± 25.5
      I think “I can't take it anymore?”5 (1.2)40 (9.8)109 (26.6)130 (31.7)126 (30.7)29.7 ± 25.4
      Feeling worn out (extremely tired)6 (1.5)35 (8.5)115 (28)131 (32)123 (30)29.8 ± 25.2
      Feeling weak and susceptible to illness17 (4.1)30 (7.3)107 (26.1)112 (27.3)144 (35.1)29.5 ± 27.9
      Total mean score35 ± 18.15
      Work-related burnout
      Feeling that my work is emotionally exhausting6 (1.5)15 (3.6)138 (33.7)191 (46.6)60 (14.6)32.6 ± 20.4
      Do you feeling completely physical and mental burn-out due of my work13 (3.2)9 (4.6)134 (32.7)161 (39.3)83 (20.2)32.8 ± 23.7
      My work frustrate me14 (3.4)22 (5.4)117 (28.5)133 (32.4)124 (30.3)29.8 ± 25.9
      Feel worn out at the end of the working day46 (11.2)47 (11.5)101 (24.6)90 (22)126 (30.7)37.6 ± 33.2
      I'm exhausted in the morning at the thought of another day at work10 (2.4)8 (2)57 (13.9)109 (26.6)226 (55.1)17.5 ± 23.7
      Feeling that every working hour is tiring for me12 (2.9)12 (2.9)65 (15.9)97 (23.7)224 (54.6)18.9 ± 25.3
      Have enough energy for family and friends during leisure time197 (48)72 (17.6)63 (15.4)33 (8)45 (11)29.0 ± 34.6
      Total mean score34.3 ± 15.1
      Table 5 represents the association of burnout with socio-demographic and work-related variables. None of the work-related variables was significantly associated with personal burnout. Age (p = 0.309) and type of family (p = 0.582) did not affect substantially personal burnout. Factors such as the number of field visits and hours of work did not show any significant association with work-related or pandemic-related burnout. The total population covered (p < 0.05) and the number of children (p = 0.029) was significantly associated with pandemic burnout. None of the socio-demographic variables was significantly associated with work-related and pandemic-related burnout.
      Table 5Association of burnout with socio-demographic and work-related variables.


      Variables
      Level of personal burnout
      TotalLow (%)Moderate (%)High (%)p-value
      Designation
      ASHA221174 (78.7)40 (18.1)7 (3.2)0.033*
      ANM189167 (88.4)18 (9.5)4 (2.1)
      Marital status
      Unmarried3430 (88.2)4 (11.8)0
      Married360297 (82.5)52 (14.4)11 (3.1)0.522
      Widowed/Divorced1614 (87.5)2 (12.5)0
      Number of children (n=376)
      No children1717 (100)00
      One child9886 (87.8)11 (11.2)1 (1)0.028*
      More than one261208 (79.7)43 (16.5)10 (3.8)
      Level of work-related burnout
      VariablesTotalLow (%)Moderate (%)High (%)p-value
      Designation
      ASHA221193 (87.3)22 (10)6 (2.7)0.017*
      ANM189174 (92.1)15 (7.9)0
      Years of work experience
      <5215196 (91.2)19 (8.8)00.011*
      5–1117107 (91.5)8 (6.8)2 (1.7)
      >107864 (82.1)10 (12.8)4 (5.1)
      Number of working hours (in a day)
      8212190 (89.6)17 (8)5 (2.4)0.208
      >8198177 (89.4)20 (10.1)1 (0.5)
      Number of field visits (in a week)
      56159 (96.8)1 (1.6)1 (1.6)0.092
      >5349308 (88.3)36 (10.3)5 (1.4)
      Level of pandemic-related burnout
      Population covered
      <35006239 (62.9)23 (37.1)00.050*
      >3500348267 (76.7)79 (22.7)2 (0.6)
      Number of working hours
      8212152 (71.7)59 (27.8)1 (0.5)0.358
      >8198154 (77.8)43 (21.7)1 (0.5)
      *p-value ≤0.05.
      The age of the respondents was found to be significantly associated with sleep quality (p < 0.001), marital status (p = 0.018), type of housing (p = 0.008) and years of work experience (p = 0.039). In contrast, factors such as hours of work, field visits, and the population covered did not show any association with sleep quality. The problem of keeping up the enthusiasm to get things done was present among 46.8% of the respondents, and trouble staying awake while engaging in any activity was present among 24.6% of the respondents. Burnout did not show a significant association with sleep quality (Table 6).
      Table 6Association of sleep quality with socio-demographic and work-related variables.
      VariablesTotalLevel of sleep quality
      Good n (%)Moderate n (%)Poor n (%)p-value
      Designation
      ASHA22162 (28.1)85 (38.5)74 (33.4)0.512
      ANM18946 (24.4)70 (37)73 (38.6)
      Age (in years)
      <3012046 (38.3)42 (35)32 (26.7)0.001**
      >3029062 (21.3)113 (39)115 (39.7)
      Type of family
      Nuclear30577 (25.2)123 (40.3)105 (34.5)0.199
      Extended10531 (29.5)32 (30.5)42 (40)
      Marital status
      Unmarried3417 (50)8 (23.5)9 (26.5)0.018*
      Married36087 (24.2)139 (38.6)134 (37.2)
      Widowed/Divorced164 (25)8 (50)4 (25)
      Type of housing
      Own house24150 (20.7)96 (39.8)95 (39.5)0.008*
      Rented house16958 (34.3)59 (34.9)52 (30.8)
      Years of work experience
      <521568 (31.6)79 (36.7)68 (31.7)0.039*
      5–1011725 (21.4)50 (42.7)42 (35.9)
      >107815 (19.2)26 (33.3)37 (47.5)
      Number of working hours in a day
      821254 (25.5)88 (41.5)70 (33)0.259
      >819854 (27.3)67 (33.8)77 (38.9)
      *p-value ≤0.05.

      4. Discussion

      The prevalence of personal burnout was 16.8%, work-related burnout was 10.5%, and pandemic burnout was 25.4%. Sleep quality was moderate among 37.8% of the participants and poor among 36.05%. Personal burnout in the current study was associated significantly with the designation of the respondents. In contrast, studies conducted in Ethiopia and China found that nurses had the highest prevalence of burnout.
      • Žutautienė R.
      • Radišauskas R.
      • Kaliniene G.
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      The prevalence of burnout and its associations with psychosocial work environment among kaunas region (Lithuania) hospitals' physicians.
      ,
      • Hu H.-X.
      • Liu L.-T.
      • Zhao F.-J.
      • Yao Y.-Y.
      • Gao Y.-X.
      • Wang G.-R.
      Factors related to job burnout among community nurses in changchun, China.
      The number of working hours in a day was significantly associated with personal burnout among healthcare professionals in a tertiary healthcare setting in Kerala. In contrast, no association was found in the current study.
      • Binub K.
      Burnout among health professionals in a tertiary medical college of northern Kerala, India.
      The present study found that years of work experience were significantly associated with work-related burnout; the studies conducted in Delhi and Andhra Pradesh among nurses and clinicians found that respondents with low years of expertise reported high levels of burnout.
      • Negi Y.
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      Burnout among nursing professionals in tertiary care hospitals of Delhi.
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      Burnout among clinicians in a tertiary care setting.
      According to studies in India and Andhra Pradesh, the pandemic burnout was more among the age group of 31–40. In contrast, age did not correlate with pandemic burnout in the current study.
      • Khasne R.W.
      • Dhakulkar B.S.
      • Mahajan H.C.
      • Kulkarni A.P.
      Burnout among healthcare workers during COVID-19 pandemic in India: results of a questionnaire-based survey.
      ,
      • Vinnakota A.
      • Srikrishna N.
      • Srinivas S.
      • Shvetha C.
      • Abhilash G.V.
      • Vidya S.
      Burnout and its impact on the mental health of physicians during the COVID -19 pandemic: a cross-sectional study from South India.
      There is no association between the number of working hours and years of experience with pandemic burnout in the present study, which is similar to the findings of a multinational study. In contrast, a study conducted in North India in a tertiary healthcare setting found that the average number of working hours was associated with burnout during the pandemic.
      • Grover S.
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      • Avasthi A.
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      ,
      • Denning M.
      • Goh E.T.
      • Tan B.
      • et al.
      Determinants of burnout and other aspects of psychological well-being in healthcare workers during the Covid-19 pandemic: a multinational cross-sectional study.
      In the current study, age was significantly associated with sleep quality. The finding is similar to a study in Iran which found that respondents above 45 years had poor sleep quality.
      • Ghalichi Leila
      • Pournik Omid
      • Ghaffari Mostafa
      • Vingard Eva
      Sleep quality among health care workers.
      Studies conducted in China and India also found that poor sleep quality increased with age, whereas a study conducted in Tripura did not find any association.
      • Nag K.
      • Datta A.
      • Karmakar N.
      • Chakraborty T.
      • Bhattacharjee P.
      Sleep disturbance and its effect on work performance of staffs following shifting duties: a cross-sectional study in a medical college and hospital of Tripura.
      • Zhou Y.
      • Yang Y.
      • Shi T.
      • et al.
      Prevalence and demographic correlates of poor sleep quality among frontline health professionals in liaoning province, China during the COVID-19 outbreak.
      • Yang Y.
      • Zhu J.
      • Yang S.
      • et al.
      Prevalence and associated factors of poor sleep quality among Chinese returning workers during the COVID-19 pandemic.
      • Aryal S.
      • D'mello M.K.
      Occupational stress and coping strategy among community health workers of Mangalore Taluk, Karnataka.
      The type of housing was significantly associated with sleep quality in the present study, similar to a survey in Tripura.
      • Nag K.
      • Datta A.
      • Karmakar N.
      • Chakraborty T.
      • Bhattacharjee P.
      Sleep disturbance and its effect on work performance of staffs following shifting duties: a cross-sectional study in a medical college and hospital of Tripura.
      The designation of the respondents was not associated with sleep quality in the current study. In contrast, a study in China among healthcare professionals during the pandemic revealed that being a nurse was related to poor sleep quality.
      • Yang Y.
      • Zhu J.
      • Yang S.
      • et al.
      Prevalence and associated factors of poor sleep quality among Chinese returning workers during the COVID-19 pandemic.
      The total population covered by ASHA workers is not per norms, which may lead to an increased workload and, in return, occupational stress.
      • Aryal S.
      • D'mello M.K.
      Occupational stress and coping strategy among community health workers of Mangalore Taluk, Karnataka.
      Most respondents feared contracting the virus and infecting their family members because of their exposure. The majority of respondents reported a feeling of non-acceptability by the community and a lack of time to spend with family and friends. Early identification of the factors causing burnout is necessary to improve the overall well-being.

      4.1 Strength and limitations

      The strength of the current study was that the questionnaires used (CBI and PSQI) are tested and validated in various national and international studies. Also, a pilot study was conducted to check for the reliability of the questionnaire. However, the study's weakness was relying on the participants' self-reporting. Being a cross-sectional study may limit our ability to identify causal relationships between burnout and sleep quality.

      5. Conclusions

      The present study showed that designation, level of education, and years of experience were associated with work-related burnout. The number of children and designation of the respondents were determining factors for personal burnout. For sleep quality, age, marital status, and type of housing were found to be determining factors. The total population covered by ASHA workers is not per norms which may lead to increased workload. The provision of necessary equipment, regular check-ups and timely interventions will benefit in minimising the risk of stress and burnout. Subsequently, more in-depth assessments are needed for a better understanding of the mental health of CHWs.

      Sources of support

      Nil

      Declaration of competing interest

      Nil

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