Prevalence and correlates of depression and psychological distress among garment factory employees in Hambantota District, Sri Lanka.

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Lasith Obadaarachchi, Amila Isuru, Suwin Hewage, Manuja Wipuladasa, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5850534/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jul, 2025 Read the published version in BMC Psychology → Version 1 posted 4 You are reading this latest preprint version Abstract Background Garment factory employees have been identified as a high-risk population for developing depression in Asian countries. Depression is recognised as a potentially reversible risk factor for low productivity in the garment factory workforce. Therefore, identification and treatment of depression in garment factory workers can improve productivity and their quality of life in general. The study aimed to determine the prevalence and correlates of depression in garment factory workers in the Hambantota district, Sri Lanka. Methods This is a cross-sectional descriptive study. The calculated sample size was 390, and a multistage random sampling method was used. Culturally validated General Health Questionnaire-12 and Beck Depression Inventory–II were used to screen for psychological morbidity and depression, respectively. Results The majority of 390 employees were females (n=325, 83.3%). The mean age was 32.9 years (SD—10.45 years). The prevalence of depression in the study sample was 16.80% (95% CI: 13.04% to 20.55%). Depression was associated with the presence of a chronic medical condition(OR-3.51, 95% CI:1.61-7.67, p<0.002), family history of psychiatric illness (OR-3.03 95% CI-1.11 to 8.26, p<0.03), history of deliberate self-harm (OR-10.79 95% CI-4.68 to 24.89, p<0.001), history of psychiatric illness (OR-6.12, 95% CI-2.39-15.73, p<0.001), and being divorced or separated from their partner. The only job-related factor that correlates with depression was working extra duty hours (OR-1.74, CI 1.01-3.02, p<0.05). Conclusions The prevalence of depression among garment factory employees in Hambantota district is high. However, it is relatively lower compared to garment factory populations in other developing Asian countries. Garment industry depression correlates psychological morbidity Introduction The garment manufacturing industry employs thousands of women in low and middle-income countries. In Sri Lanka, over 300,000 persons are employed in this industry, and the vast majority of them are women[ 1 ] [ 2 ]. The contribution by the females employed in this sector has enabled the sector to be Sri Lanka’s largest export earner since 1986 [ 3 ]. According to the Central Bank of Sri Lanka in 2022, the textile and garment sector accounted for 45.4% of the total export earnings of Sri Lanka [ 4 ]. Developing Asian Countries have been the main ready-made garment product exporters to the world. The lack of alternative job opportunities and the fact that it does not require prior skills make it a favourable job option to many, particularly women [ 5 ]. Considering the role played by these female employees, who at times work under tough social, financial and physically and emotionally demanding circumstances, understanding the prevalence and risk factors of depression among them is important. Most of the garment factories in Sri Lanka are clustered in urban areas of each district. As a result, there is labour migration from rural areas to urban areas leading to varied social adversities. Their pay is comparatively low, they usually have to work long hours and live in less than satisfactory accommodation [ 3 ]. Depression affects employees’ quality of life and substantially reduces working capacity at an individual level. From the point of view of employers, it leads to absenteeism and loss of productivity [ 6 ] The economic impact may be higher in developing countries like Sri Lanka where the economy depends largely on labour-intensive manufacturing industries like the garment industry [ 7 ]. Previous studies in other Asian countries have demonstrated a high prevalence of depression in this population, with rates ranging from 18–26% [ 5 ] [ 8 ] [ 9 ][ 10 ]. Research on depressive disorder among garment factory employees in Sri Lanka is not documented. A few studies that have been done look at burnout and general psychological distress as measured by the General Health Questionnaire (GHQ)[ 11 ] [ 12 ]. Another study reported suicidal ideation in 9.8% and substance use of 3.7% in female garment factory workers between the ages of 16–49 [ 13 ]. The objective of this study was to assess the prevalence and correlates of depression in garment factory employees in the Hambantota District, Sri Lanka. Methods Study design and sampling This was a cross-sectional descriptive study. The study sample was selected from employees of garment factories in the Hambantota district, Sri Lanka. Multi-stage sampling method was used in recruiting the participants for the study. Three subdivisions Tissamaharama, Tangalle and Hambanthota were identified in the district. Two garment factories with over 100 employees were randomly selected from each sub-division. A proportionate number of participants from each garment factory were selected. Systematic random sampling method was used to select the study sample. The employee register was used to identify the sample from each garment factory. Garment factory workers who were 18 years or older were included in the study and those who had been employed for less than 1 month were excluded from the study as it was assumed that the new recruits within their first month of employment may experience adjustment symptoms which can be wrongly identified as depression due to overlap of symptoms of the two conditions. Data collection Data collection was performed by two psychiatrists and a medical officer in occupational health who has training in mental health. A specifically designed data collection form was used to record general information and information related to potential correlates of depression. The General Health Questionnaire-12 (GHQ-12) validated for the Sri Lankan population was used to screen for psychological morbidity. It is a reliable and valid instrument with a stable factor structure across different samples and cultures and its brevity gives it an added advantage [14]. The standard scoring (0-0-1-1) was used and the cut -off level of >1 was used as it had been suggested to be optimal when using the standard scoring method to detect the presence of psychological morbidity [15] [16] The Beck Depression Inventory-II (BDI II) was used to screen for depression and it was administered to all individuals who were screened positive for GHQ-12. BDI II is a widely used self-rating screening tool for depression [17]with good psychometric properties, good test-retest reliability, high internal consistency, good construct and concurrent validity and discriminant validity [18]. It has 21 items, each scored on a 4-point scale according to the degree of severity. It has been validated for the Sinhala speaking Sri Lankan population [19], and a score of >15 was taken as indicative of depression. Data-analysis Demographic and employment-related characteristics were analysed as frequencies and percentages. Comparison of dichotomous variables was performed by Person’s Chi-squared test. Depression status was presented as a dichotomous variable. Multiple logistic regression analysis was performed to identify the independent predictors of depression and psychological morbidity of garment factory workers. Bivariate analysis was conducted to identify potential predictor variables for the final fitted model. The adequacy of the final fitted model was assessed with Hosmer and Lemeshow goodness of fit test. A type 1 error of 0.05 was considered in all significance analyses. Data analysis was performed by the IBM, SPSS version 22.0. Ethical approval was granted by the ethics review committee, faculty of medicine, University of Ruhuna. Administrative permission was obtained by the Regional Director of Health Services, Hambantota, and from the respective managers of garment factories. Results Baseline characteristics A total of 381 garment factory workers were included in the analysis. The sample was predominantly female (84.0%). The age of participants ranged from 18 to 68 years with a mean of 32.9 (SD = 10.5) years. All the workers were from the Sinhala ethnicity and almost all (99.2%) identified themselves as Buddhists, with the remainder being Catholic. More than half of the workers were married at the time, while 34.4% were neither currently nor previously married (See Table 1). Table 1 – Frequencies of marital status categories among the sampled garment factory workers Sewing Machine Operators (57.7%) followed by Helpers (20.7%) and Quality Controllers (10.2%) constituted the main job categories covered among the factory workers. Over 95% of the workers were monthly or weekly wage earners. A small percentage of the Machine Operators and Helpers were either paid daily or according to the amount of work done (See Table 2). Almost three-quarters (74.3%) of the respondents were working at a factory in their own hometown. Over 86% of respondents own a permanent job contract. Table 2 – Crosstabulation of job title versus salary interval category of garment factory workers In response to health-related questions in the study, 10.0% of the sample identified themselves as having chronic physical illnesses, while 5.0% have had a history of depression. Almost eight per cent of the sample (7.9%) revealed that they have previously attempted suicide. Family history of mental illness was noted by 5.8% of the responding garment factory workers. Prevalence of psychological morbidity As the screening tool for psychological morbidity, GHQ-12 scores of the garment factory workers showed the full range of values from 0 to 12, with a mean of 1.75 (SD = 2.55). Of the 381 participants, 132 had a score of over 1. Therefore, based on the categorisation of GHQ-12 scores above 1 as being positive for psychological morbidity, the estimated prevalence of psychological morbidity in the study was 34.65% (95% CI: 21.04% to 48.25%). Predictors of psychological morbidity Before fitting a logistic regression model for psychological morbidity (as screened by the GHQ-12), potential predictors were assessed using bivariate analysis. The predictors considered in this regard were: age, gender, highest education level, marital status, number of dependents, accommodation type, travel time from residence to factory, whether factory is in their hometown or not, job title, income category, salary interval, level of work experience, whether or not engaging in extra duty work, employed full-time or part-time, being a permanent or casual worker, amount of time spent daily on watching television, amount of time spent daily on social media, presence of substance use, currently being pregnant or not, presence of a chronic physical illness, history of depression, history of attempted suicide and family history of mental illness. Out of which, only six variables i.e., marital status, salary interval, presence of a chronic physical illness, history of depression, history of attempted suicide and family history of mental illness showed individual statistically significant relationships with psychological morbidity (See Table 3). When a multiple logistic regression analysis was attempted using these six predictor variables, two (marital status and family history of mental illness) became non-significant. Therefore, only four of the factors assessed in the study were found to be independent predictors of psychological morbidity (See Table 4). Table 3 – Results of bivariate analysis for associated factors for psychological morbidity Table 4 – Results of logistic regression analysis for psychological morbidity After controlling for the other three variables in the model, garment factory workers that get paid daily or by the number of pieces they finish have 6.75 (95% C.I: 2.05 to 22.24) times higher odds of having a psychological morbidity compared to those earning monthly or weekly salaries. In comparison to individuals that did not report having attempted suicide in the past, those with such a history were 6.14 (95% C.I: 2.45 to 15.39) times more likely to be positive for psychological morbidity as assessed by GHQ-12. Similarly, having a history of depression accounted for about 5 times higher odds, and having a chronic illness lead to almost 3 times higher odds of showing psychological morbidity in garment factory workers (Table 4). Prevalence of depression All of the 132 individuals suspected of having some type of psychological morbidity were screened for depression using the BDI-II. Out of the possible score range of 0 to 63, the GHQ-12 positive participants in the study had BDI-II scores from 0 to 46 with a mean score of 17.18 (SD = 10.37). Out of the 132 screened workers, 64 (48.48%) had a score equal to or above the BDI-II cut-off value of 16. Assuming that all the participants that were negative for psychological morbidity were also negative for depression, the estimated prevalence of depression in the study was 16.80% (95% CI: 13.04% to 20.55%). Predictors of depression The identification of independent predictors of depression was done using the same process as that used with psychological morbidity. In bivariate analysis, six out of the before mentioned candidate predictor variables showed a significant association with presence of depression. These variables were: marital status, whether or not engaging in extra duty work, presence of a chronic physical illness, history of depression, history of attempted suicide and family history of mental illness (See Table 5). When performing logistic regression analysis using these six variables presence of a chronic physical illness, history of attempted suicide and family history of mental illness ended up in the final fitted model. According to the results, garment workers with a suicide attempt history had 10.79 (95% C.I: 4.68 to 24.89) times higher odds (compared to those who had not reported such a history) of currently having depression, after adjusting for the effects of chronic physical illness and family history of mental illness. Having a chronic medical condition leads to 3.51 (95% C.I: 1.61 to 7.67) higher odds of depression, while those with mental illness in their family resulted in three times higher odds of depression when controlling for the other two variables (See Table 6). Table 5 – Results of bivariate analysis for associated factors for depression Table 6 – Results of logistic regression analysis for depression Discussion The study flags a history of attempted suicide and chronic physical illness as clear predictors of psychological distress and depressive illness in garment factory employees studied. Not surprisingly, a family history of mental illness was predictive of depressive disorder [ 20 ]and a history of depressive disorder was predictive of psychological distress. Interestingly, demographic and employment factors did not show an association with psychological distress or depressive disorder apart from daily or piecework earners compared to weekly or monthly wage earners who showed more psychological distress. The prevalence of depression as per the Sinhala BDI II was around 17%. In a study conducted in Colombo district in 2007 [ 21 ], the point prevalence of depression was recorded as 6.6%, which was much lower than the rate in this study. Another community study on randomly selected adults residing in parts of Gampaha district found a point prevalence of 11.3% [ 19 ]. A prevalence rate of 4.5% for major depression and 13.3% for mild depression was reported by a study in primary care facilities in the Post-conflict Northern Province [ 22 ]. In a study on police officers in Kandy district, the prevalence of depression was computed as 10.6 % [ 23 ]. In this sense the prevalence of depression in this group of garment factory employees appears higher than in the general population. To what extent the female preponderance in this population contributed to this is not known. Considering the fact that this is a relatively young population the figure is certainly more than the populaton prevalence. However, the depressive disorder was based on the screening tool BDI II and not established by a qualified professional which would have been ideal to arrive at a reliable point prevalence. The prevalence of depression however in this study was relatively lower compared to other studies among garment factory employees from the South Asian region where it ranged from 18–26% [ 10 ][ 8 ][ 9 ] Around one third the employees screened showed psychological morbidity. This figure is less than the psychological stress seen in 60.4% of workers in garment factories reported from a Bangladesh study. This finding is supported by a meta-analysis that has reported that the psychological stress among garment factory workers in Sri Lanka is less compared to other countries in the South Asian region [ 4 ]. There are several possible reasons for the relatively low prevalence of depressive symptoms and psychological distress in this group of garment factory employees in Sri Lanka compared to other South Asian countries. It is noteworthy that this study may not be representative of the garment factory employees in other parts of Sri Lanka. Our study population in Hambantota was living in close proximity to their families which is not the case in many other studies in Sri Lanka in other factory areas such as the Free Trade Zone in Katunayaka, a suburban area. Hence the majority of employees in this study did not have to face the adversities associated with factories in semi-urban settings in populous districts where many of the garment factory workers are migrants living in boardings. A larger study in Sri Lanka encompassing different areas in Sri Lanka has clearly shown that depressive symptoms were linked to relocation from their homes [ 13 ]. This study did not identify most of the occupation-related variables as independent predictors of depression or psychological distress. Studies conducted in other Asian countries reported many occupation-related variables as independent predictors. A systematic review in Asian countries found that ready-made garment factory workers had psychological vulnerabilities due to low wages, excessive workload, job insecurity, abusive language and feeling unsafe in the workplace [ 24 ]. A positive factor that may be considered as a reason for this difference would be the relatively better working conditions in Sri Lankan garment factories compared to their counterparts in Asia. A previous study reported that Sri Lankan garment workers appeared to be healthier than garment factory workers in other developing countries and their reported quality of life was better [ 25 ]. Very few employees reported being subjected to emotional abuse, and none reported sexual or physical abuse at work. Furthermore, the majority of workers in our study were permanent employees and had better job security. It is heartening however that occupational health services in Sri Lanka work with factory owners and employees to ensure a favorable work environment. The employers themselves are aware of the psycho-social stressors of their employees and provide psychological services such as access to a counsellor. These measures may have mitigated occupation related stress factors from impacting mental health directly. Limitations It has to be accepted that the sample size, which was calculated to determine the prevalence, was not perhaps adequate to capture the socio-economic factors that were studied resulting in a type 1 error. Furthermore, the study instruments were based on self-reports. Conclusions The study shows that the prevalence of depression among garment factory workers studied in southern Sri Lanka is higher than in the general population but lower than in other South Asian countries. The vulnerability factors are those common to depressive disorder in general. Employees need to be aware of the higher degree of mental health issues and continue to address them to improve employee wellbeing and productivity through targeted support that address both mental and physical occupational health. Declarations Author contribution: LO, AI- writing of the research proposal, manuscript writing, and data collection. SH- data analysis and writing of the manuscript. MV-data collection. SW overall supervision of the research proposal and manuscript writing. All authors reviewed and approved the final manuscript. Financial statement: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of interests: None Ethical standards: Ethical approval for this study was obtained from the ethics review committee of the University of Kelaniya, Sri Lanka (Ref. No: P/05/01/2020). The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation. All participants voluntarily gave informed consent and were assured anonymity during the survey. All experiments involving human participants were performed in accordance with relevant guidelines and regulations. Data availability: The data sets used and/or analysed during the current study are available from the corresponding author on reasonable request. Acknowledgement: Not applicable References Industry Capability Report Sri Lankan Apparel Sector. 2017. Textile And Apparel Sector Ministry of Industries. Dheerasinghe R. Garment Industry in Sri Lanka Challenges, Prospects and Strategies. Staff Studies. 2009; 33:33. Annual Report 2022 | Central Bank of Sri Lanka. https://www.cbsl.gov.lk/en/publications/economic-and-financial-reports/annual-reports/annual-report-2022. Accessed 14 May 2024. Kabir H, Maple M, Usher K, Islam MS. Health vulnerabilities of readymade garment (RMG) workers: a systematic review. https://doi.org/10.1186/s12889-019-6388-y. Stewart WF, Ricci JA, Chee E, Hahn SR, Morganstein D. Cost of lost productive work time among US workers with depression. 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Tables Table 1 – Frequencies of marital status categories among the sampled garment factory workers Marital status category Number (percentage out of total) Currently married 203 (53.3%) Never married 131 (34.4%) Currently not in a relationship 67 (17.6%) Currently in a relationship 64 (16.8%) Previously married 47 (12.3%) Separated 24 (6.3%) Widowed 14 (3.7%) Divorced 9 (2.4%) Total 381 (100.0%) Table 2 – Crosstabulation of job title versus salary interval category of garment factory workers Job title Salary interval category Number (percentage within job title) Total Number (percentage out of total) Monthly or Weekly Daily or Piece work Machine operator 209 (95.0%) 11 (5.0%) 220 (57.7%) Helper 76 (96.2%) 3 (3.8%) 79 (20.7%) Quality controller 39 (100.0%) 0 (0.0%) 39 (10.2%) Supervisor 24 (100.0%) 0 (0.0%) 24 (6.3%) Other 19 (100.0%) 0 (0.0%) 19 (5.0%) Total 367 (96.3%) 14 (3.7%) 381 (100.0%) Table 3 – Results of bivariate analysis for associated factors for psychological morbidity Potential Predictor Variables Considered Unadjusted Odds Ratio (95% Confidence Interval) Statistical Significance Age 0.99 (0.97 to 1.01) p = 0.397 Gender Female compared to male 1.21 (0.67 to 2.17) p = 0.531 Highest level of education Above Ordinary Level compared to up to Ordinary Level 1.33 (0.84 to 2.10) p = 0.220 Marital status Never married compared to currently married Separated, divorced or widowed compared to currently married 1.54 (0.97 to 2.46) 2.61 (1.36 to 4.99) p = 0 .009 p = 0.068 p = 0.004 Number of dependents 0.97 (0.81 to 1.18) p = 0.789 Type of accommodation Rented house compared to family house Rented room compared to family house 1.84 (0.37 to 9.27) 0.18 (0.02 to 1.46) p = 0 .206 p = 0.458 p = 0.109 Travel time from residence to factory 15 minutes to 1 hour compared to less than 15 minutes More than 1 hour compared to less than 15 minutes 1.10 (0.69 to 1.76) 1.15 (0.60 to 2.20) p = 0 .892 p = 0.693 p = 0.676 Working at hometown Factory at hometown compared to not 0.74 (0.46 to 1.19) p = 0.215 Job title Machine operator compared to Helper Quality controller compared to Helper Supervisor compared to Helper Other position compared to Helper 1.60 (0.91 to 2.81) 1.02 (0.43 to 2.39) 1.30 (0.49 to 3.45) 1.51 (0.53 to 4.34) p = 0 .467 p = 0.101 p = 0.968 p = 0.605 p = 0.442 Monthly income category Less than Rs. 25,000 compared to more than Rs. 25,000 0.87 (0.50 to 1.50) p = 0.613 Salary interval category Daily or piece work earners compared to weekly or monthly earners 5.02 (1.54 to 16.33) p = 0.007 Level of work experience 1-10 years compared to less than 1 year More than 10 years compared to less than 1 year 1.23 (0.75 to 2.03) 1.05 (0.49 to 2.21) p = 0 .684 p = 0.417 p = 0.907 Engaging in extra duty work Yes compared to no 0.93 (0.59 to 1.48) p = 0.768 Full-time or part-time employed Full-time compared to part-time 0.94 (0.55 to 1.62) p = 0.835 Permanent or casual worker Permanent compared to casual 1.23 (0.65 to 2.30) p = 0.528 Time spent daily on watching television Less than 1 hour compared to none 1 to 3 hours compared to none More than 3 hours compared to none 0.81 (0.47 to 1.38) 0.78 (0.42 to 1.45) 0.15 (0.02 to 1.25) p = 0 .336 p = 0.431 p = 0.428 p = 0.080 Time spent daily on social media Less than 1 hour compared to none 1 to 3 hours compared to none More than 3 hours compared to none 1.10 (0.69 to 1.78) 1.49 (0.69 to 3.21) 3.44 (0.80 to 14.80) p = 0 .307 p = 0.684 p = 0.305 p = 0.096 Substance use Presence compared to absence 1.07 (0.52 to 2.20) p = 0.846 Currently being pregnant or not Yes compared to not relevant (male) No compared to not relevant (male) 0.98 (0.27 to 3.59) 1.22 (0.67 to 2.20) p = 0 .773 p = 0.979 p = 0.514 Chronic physical illness Presence compared to absence 2.91 (1.47 to 5.76) p = 0.002 History of depression Presence compared to absence 7.85 (2.55 to 24.18) p < 0.001 History of attempted suicide Presence compared to absence 7.29 (3.04 to 17.51) p < 0.001 Family history of mental illness Presence compared to absence 2.91 (1.21 to 7.01) p = 0.017 Table 4 – Results of logistic regression analysis for psychological morbidity Final Fitted Predictor Variables Odds Ratio (95% Confidence Interval) Statistical Significance Salary interval category Daily or piece work earners compared to weekly or monthly earners 6.75 (2.05 to 22.24) p = 0.002 Chronic physical illness Presence compared to absence 2.98 (1.45 to 6.11) p = 0.003 History of depression Presence compared to absence 4.82 (1.44 to 16.15) p = 0.011 History of attempted suicide Presence compared to absence 6.14 (2.45 to 15.39) p < 0.001 Table 5 – Results of bivariate analysis for associated factors for depression Potential Predictor Variables Considered Unadjusted Odds Ratio (95% Confidence Interval) Statistical Significance Age 1.00 (0.97 to 1.03) p = 0.980 Gender Female compared to male 2.03 (0.83 to 4.94) p = 0.119 Highest level of education Above Ordinary Level compared to up to Ordinary Level 0.63 (0.33 to 1.19) p = 0.150 Marital status Never married compared to currently married Separated, divorced or widowed compared to currently married 1.13 (0.61 to 2.10) 3.23 (1.57 to 6.65) p = 0 .005 p = 0.708 p = 0.002 Number of dependents 1.01 (0.79 to 1.28) p = 0.950 Type of accommodation Rented house compared to family house Rented room compared to family house 0.97 (0.11 to 8.48) 0.49 (0.06 to 3.87) p = 0 .794 p = 0.981 p = 0.497 Travel time from residence to factory 15 minutes to 1 hour compared to less than 15 minutes More than 1 hour compared to less than 15 minutes 1.36 (0.74 to 2.52) 1.26 (0.54 to 2.93) p = 0 .614 p = 0.325 p = 0.590 Working at hometown Factory at hometown compared to not 1.16 (0.62 to 2.18) p = 0.647 Job title Machine operator compared to Helper Quality controller compared to Helper Supervisor compared to Helper Other position compared to Helper 1.55 (0.75 to 3.17) 0.71 (0.21 to 2.38) 0.88 (0.23 to 3.47) 0.73 (0.15 to 3.59) p = 0 .402 p = 0.234 p = 0.575 p = 0.859 p = 0.696 Monthly income category Less than Rs. 25,000 compared to more than Rs. 25,000 1.60 (0.72 to 3.54) p = 0.245 Salary interval category Daily or piece work earners compared to weekly or monthly earners 2.05 (0.62 to 6.74) p = 0.239 Level of work experience 1-10 years compared to less than 1 year More than 10 years compared to less than 1 year 1.12 (0.59 to 2.14) 1.35 (0.54 to 3.35) p = 0 .815 p = 0.724 p = 0.523 Engaging in extra duty work Yes compared to no 0.56 (0.32 to 0.99) p = 0.044 Full-time or part-time employed Full-time compared to part-time 1.10 (0.54 to 2.24) p = 0.788 Permanent or casual worker Permanent compared to casual 1.64 (0.67 to 4.02) p = 0.279 Time spent daily on watching television Less than 1 hour compared to none 1 to 3 hours compared to none More than 3 hours compared to none 0.82 (0.42 to 1.59) 0.73 (0.33 to 1.61) 0.41 (0.05 to 3.41) p = 0 .774 p = 0.551 p = 0.433 p = 0.407 Time spent daily on social media Less than 1 hour compared to none 1 to 3 hours compared to none More than 3 hours compared to none 0.79 (0.42 to 1.49) 1.14 (0.44 to 2.96) 1.58 (0.31 to 8.13) p = 0 .781 p = 0.467 p = 0.788 p = 0.582 Substance use Presence compared to absence 0.99 (0.39 to 2.49) p = 0.982 Currently being pregnant or not Yes compared to not relevant (male) No compared to not relevant (male) 1.67 (0.30 to 9.37) 2.05 (0.84 to 4.99) p = 0 .286 p = 0.562 p = 0.116 Chronic physical illness Presence compared to absence 3.42 (1.66 to 7.05) p = 0.001 History of depression Presence compared to absence 6.34 (2.46 to 16.32) p < 0.001 History of attempted suicide Presence compared to absence 11.75 (5.25 to 26.29) p < 0.001 Family history of mental illness Presence compared to absence 4.71 (1.94 to 11.44) p = 0.001 Table 6 – Results of logistic regression analysis for depression Predictor Variables Odds Ratio (95% Confidence Interval) Statistical Significance History of attempted suicide Presence compared to absence 10.79 (4.68 to 24.89) p < 0.001 Chronic physical illness Presence compared to absence 3.51 (1.61 to 7.67) p = 0.002 Family history of mental illness Presence compared to absence 3.03 (1.11 to 8.26) p = 0.030 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Jul, 2025 Read the published version in BMC Psychology → Version 1 posted Editorial decision: Revision requested 29 Jan, 2025 Editor assigned by journal 22 Jan, 2025 Submission checks completed at journal 22 Jan, 2025 First submitted to journal 17 Jan, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5850534","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405608800,"identity":"1b7fa573-7c16-487b-bee0-f7eceef3d587","order_by":0,"name":"Lasith Obadaarachchi","email":"data:image/png;base64,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","orcid":"","institution":"Ministry of Health, Nutrition and Indigenous Medicine","correspondingAuthor":true,"prefix":"","firstName":"Lasith","middleName":"","lastName":"Obadaarachchi","suffix":""},{"id":405608801,"identity":"44c7f41c-49b0-459b-a380-39c774a0a9ff","order_by":1,"name":"Amila Isuru","email":"","orcid":"","institution":"Rajarata University of Sri Lanka","correspondingAuthor":false,"prefix":"","firstName":"Amila","middleName":"","lastName":"Isuru","suffix":""},{"id":405608802,"identity":"233898a2-8ff5-421a-857d-8032a7129c81","order_by":2,"name":"Suwin Hewage","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Suwin","middleName":"","lastName":"Hewage","suffix":""},{"id":405608803,"identity":"41c31d07-d6c7-467e-a1fa-d7df5a492d07","order_by":3,"name":"Manuja Wipuladasa","email":"","orcid":"","institution":"University of Kelaniya","correspondingAuthor":false,"prefix":"","firstName":"Manuja","middleName":"","lastName":"Wipuladasa","suffix":""},{"id":405608804,"identity":"ecd95d3e-b634-4371-a88c-c73f4205f944","order_by":4,"name":"shehan Williams","email":"","orcid":"","institution":"University of Kelaniya","correspondingAuthor":false,"prefix":"","firstName":"shehan","middleName":"","lastName":"Williams","suffix":""}],"badges":[],"createdAt":"2025-01-17 15:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5850534/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5850534/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40359-025-03137-6","type":"published","date":"2025-07-17T15:57:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88506169,"identity":"a6521ab9-8f8d-4e2a-b1e7-2317a5b503b3","added_by":"auto","created_at":"2025-08-07 07:32:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1952107,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5850534/v1/b2b11603-85c6-4616-b09c-38eb8feab7b6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and correlates of depression and psychological distress among garment factory employees in Hambantota District, Sri Lanka.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe garment manufacturing industry employs thousands of women in low and middle-income countries. In Sri Lanka, over 300,000 persons are employed in this industry, and the vast majority of them are women[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The contribution by the females employed in this sector has enabled the sector to be Sri Lanka\u0026rsquo;s largest export earner since 1986 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to the Central Bank of Sri Lanka in 2022, the textile and garment sector accounted for 45.4% of the total export earnings of Sri Lanka [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Developing Asian Countries have been the main ready-made garment product exporters to the world. The lack of alternative job opportunities and the fact that it does not require prior skills make it a favourable job option to many, particularly women [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsidering the role played by these female employees, who at times work under tough social, financial and physically and emotionally demanding circumstances, understanding the prevalence and risk factors of depression among them is important. Most of the garment factories in Sri Lanka are clustered in urban areas of each district. As a result, there is labour migration from rural areas to urban areas leading to varied social adversities. Their pay is comparatively low, they usually have to work long hours and live in less than satisfactory accommodation [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDepression affects employees\u0026rsquo; quality of life and substantially reduces working capacity at an individual level. From the point of view of employers, it leads to absenteeism and loss of productivity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] The economic impact may be higher in developing countries like Sri Lanka where the economy depends largely on labour-intensive manufacturing industries like the garment industry [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies in other Asian countries have demonstrated a high prevalence of depression in this population, with rates ranging from 18\u0026ndash;26% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eResearch on depressive disorder among garment factory employees in Sri Lanka is not documented. A few studies that have been done look at burnout and general psychological distress as measured by the General Health Questionnaire (GHQ)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Another study reported suicidal ideation in 9.8% and substance use of 3.7% in female garment factory workers between the ages of 16\u0026ndash;49 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe objective of this study was to assess the prevalence and correlates of depression in garment factory employees in the Hambantota District, Sri Lanka.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and sampling\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis was a cross-sectional descriptive study. The study sample was selected from employees of garment factories in the Hambantota district, Sri Lanka.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Multi-stage sampling method was used in recruiting the participants for the study. Three \u0026nbsp; subdivisions Tissamaharama, Tangalle and Hambanthota were identified in the district. Two garment factories with over 100 employees were randomly selected from each sub-division. A proportionate number of participants from each garment factory were selected. Systematic random sampling method was used to select the study sample. The employee register was used to identify the sample from each garment factory. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGarment factory workers who were 18 years or older were included in the study and those who had been employed for less than 1 month were excluded from the study as it was assumed that the new recruits within their first month of employment may experience adjustment symptoms which can be wrongly identified as depression due to overlap of symptoms of the two conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData collection \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData collection was performed by two psychiatrists and a medical officer in occupational health who has training in mental health. A specifically designed data collection form was used to record general information and information related to potential correlates of depression. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe General Health Questionnaire-12 (GHQ-12) validated for the Sri Lankan population was used to screen for psychological morbidity. It is a reliable and valid instrument with a stable factor structure across different samples and cultures and its brevity gives it an added advantage \u0026nbsp; [14]. The standard scoring (0-0-1-1) was used and the cut -off level of \u0026gt;1 was used as it had been suggested to be optimal when using the standard scoring method to detect the presence of psychological morbidity [15] [16]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Beck Depression Inventory-II (BDI II) was used to screen for depression and it was administered to all individuals who were screened positive for GHQ-12. BDI II is a widely used self-rating screening tool for depression [17]with good psychometric properties, good test-retest reliability, high internal consistency, good construct and concurrent validity and discriminant validity [18]. It has 21 items, each scored on a 4-point scale according to the degree of severity. It has been validated for the Sinhala speaking Sri Lankan population \u0026nbsp;[19], and a score of \u0026gt;15 was taken as indicative of depression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData-analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDemographic and employment-related characteristics were analysed as frequencies and percentages. Comparison of dichotomous variables was performed by Person\u0026rsquo;s Chi-squared test. Depression status was presented as a dichotomous variable. Multiple logistic regression analysis was performed to identify the independent predictors of depression and psychological morbidity of garment factory workers. Bivariate analysis was conducted to identify potential predictor variables for the final fitted model. The adequacy of the final fitted model was assessed with Hosmer and Lemeshow goodness of fit test. A type 1 error of 0.05 was considered in all significance analyses. Data analysis was performed by the IBM, SPSS version 22.0. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical approval was granted by the ethics review committee, faculty of medicine, University of Ruhuna. Administrative permission was obtained by the Regional Director of Health Services, Hambantota, and from the respective managers of garment factories.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 381 garment factory workers were included in the analysis. The sample was predominantly female (84.0%). The age of participants ranged from 18 to 68 years with a mean of 32.9 (SD = 10.5) years. All the workers were from the Sinhala ethnicity and almost all (99.2%) identified themselves as Buddhists, with the remainder being Catholic. More than half of the workers were married at the time, while 34.4% were neither currently nor previously married (See Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 \u0026ndash; Frequencies of marital status categories among the sampled garment factory workers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSewing Machine Operators (57.7%) followed by Helpers (20.7%) and Quality Controllers (10.2%) constituted the main job categories covered among the factory workers. Over 95% of the workers were monthly or weekly wage earners. A small percentage of the Machine Operators and Helpers were either paid daily or according to the amount of work done (See Table 2). Almost three-quarters (74.3%) of the respondents were working at a factory in their own hometown. Over 86% of respondents own a permanent job contract.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 \u0026ndash; Crosstabulation of job title versus salary interval category of garment factory workers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn response to health-related questions in the study, 10.0% of the sample identified themselves as having chronic physical illnesses, while 5.0% have had a history of depression. Almost eight per cent of the sample (7.9%) revealed that they have previously attempted suicide. Family history of mental illness was noted by 5.8% of the responding garment factory workers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of psychological morbidity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the screening tool for psychological morbidity, GHQ-12 scores of the garment factory workers showed the full range of values from 0 to 12, with a mean of 1.75 (SD = 2.55). Of the 381 participants, 132 had a score of over 1. Therefore, based on the categorisation of GHQ-12 scores above 1 as being positive for psychological morbidity, the estimated prevalence of psychological morbidity in the study was 34.65% (95% CI: 21.04% to 48.25%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictors of psychological morbidity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore fitting a logistic regression model for psychological morbidity (as screened by the GHQ-12), potential predictors were assessed using bivariate analysis. The predictors considered in this regard were: age, gender, highest education level, marital status, number of dependents, accommodation type, travel time from residence to factory, whether factory is in their hometown or not, job title, income category, salary interval, level of work experience, whether or not engaging in extra duty work, employed full-time or part-time, being a permanent or casual worker, amount of time spent daily on watching television, amount of time spent daily on social media, presence of substance use, currently being pregnant or not, presence of a chronic physical illness, history of depression, history of attempted suicide and family history of mental illness. Out of which, only six variables i.e., marital status, salary interval, presence of a chronic physical illness, history of depression, history of attempted suicide and family history of mental illness showed individual statistically significant relationships with psychological morbidity (See Table 3). When a multiple logistic regression analysis was attempted using these six predictor variables, two (marital status and family history of mental illness) became non-significant. Therefore, only four of the factors assessed in the study were found to be independent predictors of psychological morbidity (See Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 \u0026ndash; Results of bivariate analysis for associated factors for psychological morbidity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 \u0026ndash; Results of logistic regression analysis for psychological morbidity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter controlling for the other three variables in the model, garment factory workers that get paid daily or by the number of pieces they finish have 6.75 (95% C.I: 2.05 to 22.24) times higher odds of having a psychological morbidity compared to those earning monthly or weekly salaries. In comparison to individuals that did not report having attempted suicide in the past, those with such a history were 6.14 (95% C.I: 2.45 to 15.39) times more likely to be positive for psychological morbidity as assessed by GHQ-12. Similarly, having a history of depression accounted for about 5 times higher odds, and having a chronic illness lead to almost 3 times higher odds of showing psychological morbidity in garment factory workers (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence of depression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll of the 132 individuals suspected of having some type of psychological morbidity were screened for depression using the BDI-II. Out of the possible score range of 0 to 63, the GHQ-12 positive participants in the study had BDI-II scores from 0 to 46 with a mean score of 17.18 (SD = 10.37). Out of the 132 screened workers, 64 (48.48%) had a score equal to or above the BDI-II cut-off value of 16. Assuming that all the participants that were negative for psychological morbidity were also negative for depression, the estimated prevalence of depression in the study was 16.80% (95% CI: 13.04% to 20.55%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictors of depression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe identification of independent predictors of depression was done using the same process as that used with psychological morbidity. In bivariate analysis, six out of the before mentioned candidate predictor variables showed a significant association with presence of depression. These variables were: marital status, whether or not engaging in extra duty work, presence of a chronic physical illness, history of depression, history of attempted suicide and family history of mental illness (See Table 5). When performing logistic regression analysis using these six variables presence of a chronic physical illness, history of attempted suicide and family history of mental illness ended up in the final fitted model. According to the results, garment workers with a suicide attempt history had 10.79 (95% C.I: 4.68 to 24.89) times higher odds (compared to those who had not reported such a history) of currently having depression, after adjusting for the effects of chronic physical illness and family history of mental illness. Having a chronic medical condition leads to 3.51 (95% C.I: 1.61 to 7.67) higher odds of depression, while those with mental illness in their family resulted in three times higher odds of depression when controlling for the other two variables (See Table 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 \u0026ndash; Results of bivariate analysis for associated factors for depression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 \u0026ndash; Results of logistic regression analysis for depression\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study flags a history of attempted suicide and chronic physical illness as clear predictors of psychological distress and depressive illness in garment factory employees studied. Not surprisingly, a family history of mental illness was predictive of depressive disorder [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]and a history of depressive disorder was predictive of psychological distress. Interestingly, demographic and employment factors did not show an association with psychological distress or depressive disorder apart from daily or piecework earners compared to weekly or monthly wage earners who showed more psychological distress.\u003c/p\u003e \u003cp\u003eThe prevalence of depression as per the Sinhala BDI II was around 17%. In a study conducted in Colombo district in 2007 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the point prevalence of depression was recorded as 6.6%, which was much lower than the rate in this study. Another community study on randomly selected adults residing in parts of Gampaha district found a point prevalence of 11.3% [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A prevalence rate of 4.5% for major depression and 13.3% for mild depression was reported by a study in primary care facilities in the Post-conflict Northern Province [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In a study on police officers in Kandy district, the prevalence of depression was computed as 10.6 % [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this sense the prevalence of depression in this group of garment factory employees appears higher than in the general population. To what extent the female preponderance in this population contributed to this is not known. Considering the fact that this is a relatively young population the figure is certainly more than the populaton prevalence. However, the depressive disorder was based on the screening tool BDI II and not established by a qualified professional which would have been ideal to arrive at a reliable point prevalence.\u003c/p\u003e \u003cp\u003eThe prevalence of depression however in this study was relatively lower compared to other studies among garment factory employees from the South Asian region where it ranged from 18\u0026ndash;26% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAround one third the employees screened showed psychological morbidity. This figure is less than the psychological stress seen in 60.4% of workers in garment factories reported from a Bangladesh study. This finding is supported by a meta-analysis that has reported that the psychological stress among garment factory workers in Sri Lanka is less compared to other countries in the South Asian region [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are several possible reasons for the relatively low prevalence of depressive symptoms and psychological distress in this group of garment factory employees in Sri Lanka compared to other South Asian countries. It is noteworthy that this study may not be representative of the garment factory employees in other parts of Sri Lanka. Our study population in Hambantota was living in close proximity to their families which is not the case in many other studies in Sri Lanka in other factory areas such as the Free Trade Zone in Katunayaka, a suburban area. Hence the majority of employees in this study did not have to face the adversities associated with factories in semi-urban settings in populous districts where many of the garment factory workers are migrants living in boardings. A larger study in Sri Lanka encompassing different areas in Sri Lanka has clearly shown that depressive symptoms were linked to relocation from their homes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study did not identify most of the occupation-related variables as independent predictors of depression or psychological distress. Studies conducted in other Asian countries reported many occupation-related variables as independent predictors. A systematic review in Asian countries found that ready-made garment factory workers had psychological vulnerabilities due to low wages, excessive workload, job insecurity, abusive language and feeling unsafe in the workplace [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A positive factor that may be considered as a reason for this difference would be the relatively better working conditions in Sri Lankan garment factories compared to their counterparts in Asia. A previous study reported that Sri Lankan garment workers appeared to be healthier than garment factory workers in other developing countries and their reported quality of life was better [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Very few employees reported being subjected to emotional abuse, and none reported sexual or physical abuse at work. Furthermore, the majority of workers in our study were permanent employees and had better job security.\u003c/p\u003e \u003cp\u003eIt is heartening however that occupational health services in Sri Lanka work with factory owners and employees to ensure a favorable work environment. The employers themselves are aware of the psycho-social stressors of their employees and provide psychological services such as access to a counsellor. These measures may have mitigated occupation related stress factors from impacting mental health directly.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eIt has to be accepted that the sample size, which was calculated to determine the prevalence, was not perhaps adequate to capture the socio-economic factors that were studied resulting in a type 1 error. Furthermore, the study instruments were based on self-reports.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe study shows that the prevalence of depression among garment factory workers studied in southern Sri Lanka is higher than in the general population but lower than in other South Asian countries. The vulnerability factors are those common to depressive disorder in general. Employees need to be aware of the higher degree of mental health issues and continue to address them to improve employee wellbeing and productivity through targeted support that address both mental and physical occupational health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eLO, AI- writing of the research proposal, manuscript writing, and data collection. SH- data analysis and writing of the manuscript. MV-data collection. SW overall supervision of the research proposal and manuscript writing. All authors reviewed and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial statement:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interests:\u003c/strong\u003e None\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical standards:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eEthical approval for this study was obtained from the ethics review committee of the University of Kelaniya, Sri Lanka (Ref. No: P/05/01/2020). The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation. All participants voluntarily gave informed consent and were assured anonymity during the survey. All experiments involving human participants were performed in accordance with relevant guidelines and regulations.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe data sets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e Not applicable\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eIndustry Capability Report Sri Lankan Apparel Sector. 2017.\u003c/li\u003e\n \u003cli\u003eTextile And Apparel Sector Ministry of Industries.\u003c/li\u003e\n \u003cli\u003eDheerasinghe R. Garment Industry in Sri Lanka Challenges, Prospects and Strategies. Staff Studies. 2009; 33:33.\u003c/li\u003e\n \u003cli\u003eAnnual Report 2022 | Central Bank of Sri Lanka. https://www.cbsl.gov.lk/en/publications/economic-and-financial-reports/annual-reports/annual-report-2022. Accessed 14 May 2024.\u003c/li\u003e\n \u003cli\u003eKabir H, Maple M, Usher K, Islam MS. Health vulnerabilities of readymade garment (RMG) workers: a systematic review. https://doi.org/10.1186/s12889-019-6388-y.\u003c/li\u003e\n \u003cli\u003eStewart WF, Ricci JA, Chee E, Hahn SR, Morganstein D. Cost of lost productive work time among US workers with depression. JAMA. 2003; 289:3135\u0026ndash;44.\u003c/li\u003e\n \u003cli\u003eBohra N, Srivastava S, Bhatia M. Depression in women in Indian context. Indian J Psychiatry. 2015;57 Suppl 2:239\u0026ndash;45.\u003c/li\u003e\n \u003cli\u003ePham Minh K, Pham Minh K. O r i g i n a l p a p e r Work-related Depression and Associated Factors in a Shoe Manufacturing Factory in Haiphong City, Vietnam. Int J Occup Med Environ Health. 2014; 27:950\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eMou J, Cheng J, Griffiths SM, Wong SYS, Hillier S, Zhang D. Internal migration and depressive symptoms among migrant factory workers in Shenzhen, China. J Community Psychol. 2011; 39:212\u0026ndash;30.\u003c/li\u003e\n \u003cli\u003eFitch TJ, Moran J, Villanueva G, Sagiraju HKR, Quadir MM, Alamgir H. Prevalence and risk factors of depression among garment workers in Bangladesh. International Journal of Social Psychiatry. 2017; 63:244\u0026ndash;54.\u003c/li\u003e\n \u003cli\u003ePallewatte NC. prevalence of chronic fatigue and common mental disorders among female workers in the free trade zone Katunayake and some occupational and some occupational and life risk factors for chronic fatigue. Thesis. Post Graduate Institute of Medicine, University of Colombo; 2005.\u003c/li\u003e\n \u003cli\u003eSellar T, Arulrajah AA. The Role of Social Support on Job Burnout in the Apparel Firm. International Business Research. 2018; 12:110.\u003c/li\u003e\n \u003cli\u003eA Psychological Study of Blue Collar Female Workers (2000) - Women\u0026rsquo;s Education \u0026amp; Research Centre | Women NGO in Sri Lanka | WERC | A Psychological Study of Blue Collar Female Workers (2000) - Women\u0026rsquo;s Education \u0026amp; Research Centre | Women NGO in Sri Lanka | WERC. https://www.wercsl.org/a-psychological-study-of-blue-collar-female-workers-2000/. Accessed 15 May 2024.\u003c/li\u003e\n \u003cli\u003ePicardi A, Abeni D, Pasquini P. Assessing psychological distress in patients with skin diseases: reliability, validity and factor structure of the GHQ-12. Journal of the European Academy of Dermatology and Venereology. 2001; 15:410\u0026ndash;7.\u003c/li\u003e\n \u003cli\u003eAbeysena H, Jayawardana P, Peiris U, Rodrigo A. Validation of the Sinhala version of the 12-item General Health Questionnaire. Journal of the Postgraduate Institute of Medicine. 2014; 1:8.\u003c/li\u003e\n \u003cli\u003eAbeysena HTCS, Jayawardana PL, Peiris MUPK, Rodrigo A. Validity of the Sinhala Version of the General Health Questionnaires Item 12 and 30: Using Different Sampling Strategies and Scoring Methods. International Journal of Medical Research Professionals. 2016;2.\u003c/li\u003e\n \u003cli\u003eZimmerman M, McGlinchey JB. Why Don\u0026rsquo;t Psychiatrists Use Scales to Measure Outcome When Treating Depressed Patients? J Clin Psychiatry. 2008; 69:22452.\u003c/li\u003e\n \u003cli\u003eBeck Depression Inventory\u0026ndash;II. https://psycnet.apa.org/doiLanding?doi=10.1037%2Ft00742-000. Accessed 17 May 2024.\u003c/li\u003e\n \u003cli\u003eRodrigo A, Kuruppuarachchi K, Pathmeswaran A. Validation of the Beck Depression Inventory II among the Sinhalese speaking population in Sri Lanka.\u003c/li\u003e\n \u003cli\u003eMonroe SM, Slavich GM, Gotlib IH. Life stress and family history for depression: The moderating role ofpast depressive episodes. J Psychiatr Res. 2014; 49:90\u0026ndash;5.\u003c/li\u003e\n \u003cli\u003eBall HA, Siribaddana SH, Kovas Y, Glozier N, McGuffin P, Sumathipala A, et al. Epidemiology and symptomatology of depression in Sri Lanka: A cross-sectional population-based survey in Colombo District. J Affect Disord. 2010; 123:188.\u003c/li\u003e\n \u003cli\u003eSenarath U, Wickramage K, Peiris SL. Prevalence of depression and its associated factors among patients attending primary care settings in the post-conflict Northern Province in Sri Lanka: a cross-sectional study. 2014.\u003c/li\u003e\n \u003cli\u003eWickramasinghe ND, Wijesinghe PR, Dharmaratne SD, Agampodi SB. The prevalence and associated factors of depression in policing: a cross sectional study in Sri Lanka. Springerplus. 2016;5.\u003c/li\u003e\n \u003cli\u003eKabir H, Maple M, Usher K, Islam MS. Health vulnerabilities of readymade garment (RMG) workers: A systematic review. BMC Public Health. 2019;19.\u003c/li\u003e\n \u003cli\u003eDe Silva V, Lipscomb H, Ostbye T. Occupational health problems among female garment factory workers in Sri Lanka. Occup Environ Med. 2011;68 Suppl_1: A127\u0026ndash;A127.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 \u0026ndash; Frequencies of marital status categories among the sampled garment factory workers\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber (percentage out of total)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrently married\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e203 (53.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNever married\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e131 (34.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eCurrently not in a relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e67 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eCurrently in a relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e64 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreviously married\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e47 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e24 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e14 (3.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e9 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e381 (100.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 2 \u0026ndash; Crosstabulation of job title versus salary interval category of garment factory workers\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob title\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSalary interval category\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNumber (percentage within job title)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNumber (percentage out of total)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly or Weekly\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDaily or Piece work\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMachine operator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e209 (95.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e11 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e220 (57.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHelper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e76 (96.2%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e3 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e79 (20.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuality controller\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e39 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e39 (10.2%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSupervisor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e24 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24 (6.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e19 (100.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19 (5.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e367 (96.3%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14 (3.7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e381 (100.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 \u0026ndash; Results of bivariate analysis for associated factors for psychological morbidity\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotential Predictor Variables Considered\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted Odds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% Confidence Interval)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical Significance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e0.99 (0.97 to 1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003ep = 0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFemale compared to male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.21 (0.67 to 2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHighest level of education\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAbove Ordinary Level compared to up to Ordinary Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.33 (0.84 to 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNever married compared to currently married\u003c/p\u003e\n \u003cp\u003eSeparated, divorced or widowed compared to currently married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.54 (0.97 to 2.46)\u003c/p\u003e\n \u003cp\u003e2.61 (1.36 to 4.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.009\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.068\u003c/p\u003e\n \u003cp\u003ep = 0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of dependents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e0.97 (0.81 to 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003ep = 0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of accommodation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRented house compared to family house\u003c/p\u003e\n \u003cp\u003eRented room compared to family house\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.84 (0.37 to 9.27)\u003c/p\u003e\n \u003cp\u003e0.18 (0.02 to 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.206\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.458\u003c/p\u003e\n \u003cp\u003ep = 0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTravel time from residence to factory\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e15 minutes to 1 hour compared to less than 15 minutes\u003c/p\u003e\n \u003cp\u003eMore than 1 hour compared to less than 15 minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.10 (0.69 to 1.76)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;1.15 (0.60 to 2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.892\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.693\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;p = 0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking at hometown\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFactory at hometown compared to not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.74 (0.46 to 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob title\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMachine operator compared to Helper\u003c/p\u003e\n \u003cp\u003eQuality controller compared to Helper\u003c/p\u003e\n \u003cp\u003eSupervisor compared to Helper\u003c/p\u003e\n \u003cp\u003eOther position compared to Helper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.60 (0.91 to 2.81)\u003c/p\u003e\n \u003cp\u003e1.02 (0.43 to 2.39)\u003c/p\u003e\n \u003cp\u003e1.30 (0.49 to 3.45)\u003c/p\u003e\n \u003cp\u003e1.51 (0.53 to 4.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.467\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.101\u003c/p\u003e\n \u003cp\u003ep = 0.968\u003c/p\u003e\n \u003cp\u003ep = 0.605\u003c/p\u003e\n \u003cp\u003ep = 0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly income category\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLess than Rs. 25,000 compared to more than Rs. 25,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.87 (0.50 to 1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.613\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSalary interval category\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDaily or piece work earners compared to weekly or monthly earners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e5.02 (1.54 to 16.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of work experience\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1-10 years compared to less than 1 year\u003c/p\u003e\n \u003cp\u003eMore than 10 years compared to less than 1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.23 (0.75 to 2.03)\u003c/p\u003e\n \u003cp\u003e1.05 (0.49 to 2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.684\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.417\u003c/p\u003e\n \u003cp\u003ep = 0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEngaging in extra duty work\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes compared to no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.93 (0.59 to 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull-time or part-time employed\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFull-time compared to part-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.94 (0.55 to 1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePermanent or casual worker\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePermanent compared to casual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.23 (0.65 to 2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime spent daily on watching television\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLess than 1 hour compared to none\u003c/p\u003e\n \u003cp\u003e1 to 3 hours compared to none\u003c/p\u003e\n \u003cp\u003eMore than 3 hours compared to none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.81 (0.47 to 1.38)\u003c/p\u003e\n \u003cp\u003e0.78 (0.42 to 1.45)\u003c/p\u003e\n \u003cp\u003e0.15 (0.02 to 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.336\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.431\u003c/p\u003e\n \u003cp\u003ep = 0.428\u003c/p\u003e\n \u003cp\u003ep = 0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime spent daily on social media\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLess than 1 hour compared to none\u003c/p\u003e\n \u003cp\u003e1 to 3 hours compared to none\u003c/p\u003e\n \u003cp\u003eMore than 3 hours compared to none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.10 (0.69 to 1.78)\u003c/p\u003e\n \u003cp\u003e1.49 (0.69 to 3.21)\u003c/p\u003e\n \u003cp\u003e3.44 (0.80 to 14.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.307\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.684\u003c/p\u003e\n \u003cp\u003ep = 0.305\u003c/p\u003e\n \u003cp\u003ep = 0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubstance use\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.07 (0.52 to 2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrently being pregnant or not\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes compared to not relevant (male)\u003c/p\u003e\n \u003cp\u003eNo compared to not relevant (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.98 (0.27 to 3.59)\u003c/p\u003e\n \u003cp\u003e1.22 (0.67 to 2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.773\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.979\u003c/p\u003e\n \u003cp\u003ep = 0.514\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic physical illness\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e2.91 (1.47 to 5.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of depression\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e7.85 (2.55 to 24.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of attempted suicide\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e7.29 (3.04 to 17.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.0586%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily history of mental illness\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.3164%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e2.91 (1.21 to 7.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4922%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 \u0026ndash; Results of logistic regression analysis for psychological morbidity\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.8125%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal Fitted Predictor Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.0625%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% Confidence Interval)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.0137%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical Significance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.8125%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSalary interval category\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDaily or piece work earners compared to weekly or monthly earners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.0625%;\"\u003e\n \u003cp\u003e6.75 (2.05 to 22.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.0137%;\"\u003e\n \u003cp\u003ep = 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.8125%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic physical illness\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.0625%;\"\u003e\n \u003cp\u003e2.98 (1.45 to 6.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.0137%;\"\u003e\n \u003cp\u003ep = 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.8125%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of depression\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.0625%;\"\u003e\n \u003cp\u003e4.82 (1.44 to 16.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.0137%;\"\u003e\n \u003cp\u003ep = 0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55.8125%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of attempted suicide\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.0625%;\"\u003e\n \u003cp\u003e6.14 (2.45 to 15.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.0137%;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5 \u0026ndash; Results of bivariate analysis for associated factors for depression\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePotential Predictor Variables Considered\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted Odds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% Confidence Interval)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical Significance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e1.00 (0.97 to 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep = 0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFemale compared to male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e2.03 (0.83 to 4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep = 0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHighest level of education\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAbove Ordinary Level compared to up to Ordinary Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e0.63 (0.33 to 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep = 0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eNever married compared to currently married\u003c/p\u003e\n \u003cp\u003eSeparated, divorced or widowed compared to currently married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.13 (0.61 to 2.10)\u003c/p\u003e\n \u003cp\u003e3.23 (1.57 to 6.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.005\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.708\u003c/p\u003e\n \u003cp\u003ep = 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of dependents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e1.01 (0.79 to 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep = 0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of accommodation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eRented house compared to family house\u003c/p\u003e\n \u003cp\u003eRented room compared to family house\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.97 (0.11 to 8.48)\u003c/p\u003e\n \u003cp\u003e0.49 (0.06 to 3.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.794\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.981\u003c/p\u003e\n \u003cp\u003ep = 0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTravel time from residence to factory\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e15 minutes to 1 hour compared to less than 15 minutes\u003c/p\u003e\n \u003cp\u003eMore than 1 hour compared to less than 15 minutes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.36 (0.74 to 2.52)\u003c/p\u003e\n \u003cp\u003e1.26 (0.54 to 2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.614\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.325\u003c/p\u003e\n \u003cp\u003ep = 0.590\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorking at hometown\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFactory at hometown compared to not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e1.16 (0.62 to 2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep = 0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob title\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMachine operator compared to Helper\u003c/p\u003e\n \u003cp\u003eQuality controller compared to Helper\u003c/p\u003e\n \u003cp\u003eSupervisor compared to Helper\u003c/p\u003e\n \u003cp\u003eOther position compared to Helper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.55 (0.75 to 3.17)\u003c/p\u003e\n \u003cp\u003e0.71 (0.21 to 2.38)\u003c/p\u003e\n \u003cp\u003e0.88 (0.23 to 3.47)\u003c/p\u003e\n \u003cp\u003e0.73 (0.15 to 3.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.402\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.234\u003c/p\u003e\n \u003cp\u003ep = 0.575\u003c/p\u003e\n \u003cp\u003ep = 0.859\u003c/p\u003e\n \u003cp\u003ep = 0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMonthly income category\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLess than Rs. 25,000 compared to more than Rs. 25,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e1.60 (0.72 to 3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep = 0.245\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSalary interval category\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eDaily or piece work earners compared to weekly or monthly earners\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e2.05 (0.62 to 6.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep = 0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of work experience\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e1-10 years compared to less than 1 year\u003c/p\u003e\n \u003cp\u003eMore than 10 years compared to less than 1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.12 (0.59 to 2.14)\u003c/p\u003e\n \u003cp\u003e1.35 (0.54 to 3.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.815\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.724\u003c/p\u003e\n \u003cp\u003ep = 0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEngaging in extra duty work\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes compared to no\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e0.56 (0.32 to 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep = 0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull-time or part-time employed\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFull-time compared to part-time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e1.10 (0.54 to 2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep = 0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePermanent or casual worker\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePermanent compared to casual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e1.64 (0.67 to 4.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep = 0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime spent daily on watching television\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLess than 1 hour compared to none\u003c/p\u003e\n \u003cp\u003e1 to 3 hours compared to none\u003c/p\u003e\n \u003cp\u003eMore than 3 hours compared to none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.82 (0.42 to 1.59)\u003c/p\u003e\n \u003cp\u003e0.73 (0.33 to 1.61)\u003c/p\u003e\n \u003cp\u003e0.41 (0.05 to 3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.774\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.551\u003c/p\u003e\n \u003cp\u003ep = 0.433\u003c/p\u003e\n \u003cp\u003ep = 0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime spent daily on social media\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLess than 1 hour compared to none\u003c/p\u003e\n \u003cp\u003e1 to 3 hours compared to none\u003c/p\u003e\n \u003cp\u003eMore than 3 hours compared to none\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.79 (0.42 to 1.49)\u003c/p\u003e\n \u003cp\u003e1.14 (0.44 to 2.96)\u003c/p\u003e\n \u003cp\u003e1.58 (0.31 to 8.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.781\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.467\u003c/p\u003e\n \u003cp\u003ep = 0.788\u003c/p\u003e\n \u003cp\u003ep = 0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubstance use\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e0.99 (0.39 to 2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep = 0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrently being pregnant or not\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes compared to not relevant (male)\u003c/p\u003e\n \u003cp\u003eNo compared to not relevant (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.67 (0.30 to 9.37)\u003c/p\u003e\n \u003cp\u003e2.05 (0.84 to 4.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep = 0\u003c/strong\u003e\u003cstrong\u003e.286\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ep = 0.562\u003c/p\u003e\n \u003cp\u003ep = 0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic physical illness\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e3.42 (1.66 to 7.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep = 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of depression\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e6.34 (2.46 to 16.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of attempted suicide\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e11.75 (5.25 to 26.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 53.4375%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily history of mental illness\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.6875%;\"\u003e\n \u003cp\u003e4.71 (1.94 to 11.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7246%;\"\u003e\n \u003cp\u003ep = 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 \u0026ndash; Results of logistic regression analysis for depression\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePredictor Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% Confidence Interval)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical Significance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of attempted suicide\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e10.79 (4.68 to 24.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ep \u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic physical illness\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e3.51 (1.61 to 7.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ep = 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily history of mental illness\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePresence compared to absence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003e3.03 (1.11 to 8.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003ep = 0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Garment industry, depression, correlates, psychological morbidity","lastPublishedDoi":"10.21203/rs.3.rs-5850534/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5850534/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\n\u003cp\u003eGarment factory employees have been identified as a high-risk population for developing depression in Asian countries. Depression is recognised as a potentially reversible risk factor for low productivity in the garment factory workforce. Therefore, identification and treatment of depression in garment factory workers can improve productivity and their quality of life in general. The study aimed to determine the prevalence and correlates of depression in garment factory workers in the Hambantota district, Sri Lanka.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eThis is a cross-sectional descriptive study. The calculated sample size was 390, and a multistage random sampling method was used. Culturally validated General Health Questionnaire-12 and Beck Depression Inventory–II were used to screen for psychological morbidity and depression, respectively.\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eThe majority of 390 employees were females (n=325, 83.3%). The mean age was 32.9 years (SD—10.45 years). The prevalence of depression in the study sample was 16.80% (95% CI: 13.04% to 20.55%). Depression was associated with the presence of a chronic medical condition(OR-3.51, 95% CI:1.61-7.67, p\u0026lt;0.002), family history of psychiatric illness (OR-3.03 95% CI-1.11 to 8.26, p\u0026lt;0.03), history of deliberate self-harm (OR-10.79 95% CI-4.68 to 24.89, p\u0026lt;0.001), history of psychiatric illness (OR-6.12, 95% CI-2.39-15.73, p\u0026lt;0.001), and being divorced or separated from their partner. The only job-related factor that correlates with depression was working extra duty hours (OR-1.74, CI 1.01-3.02, p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eConclusions\u003c/p\u003e\n\u003cp\u003eThe prevalence of depression among garment factory employees in Hambantota district is high. However, it is relatively lower compared to garment factory populations in other developing Asian countries.\u003c/p\u003e","manuscriptTitle":"Prevalence and correlates of depression and psychological distress among garment factory employees in Hambantota District, Sri Lanka.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-24 07:05:15","doi":"10.21203/rs.3.rs-5850534/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-29T15:55:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-22T12:17:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-22T12:15:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-01-17T15:39:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3fd6ec32-e0b7-4287-9e6d-001e2da00290","owner":[],"postedDate":"January 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-07T07:19:39+00:00","versionOfRecord":{"articleIdentity":"rs-5850534","link":"https://doi.org/10.1186/s40359-025-03137-6","journal":{"identity":"bmc-psychology","isVorOnly":false,"title":"BMC Psychology"},"publishedOn":"2025-07-17 15:57:16","publishedOnDateReadable":"July 17th, 2025"},"versionCreatedAt":"2025-01-24 07:05:15","video":"","vorDoi":"10.1186/s40359-025-03137-6","vorDoiUrl":"https://doi.org/10.1186/s40359-025-03137-6","workflowStages":[]},"version":"v1","identity":"rs-5850534","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5850534","identity":"rs-5850534","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}