Occupational Stress and Nutritional Status among Daily Wage Workers in Small-Scale Industries of India

Occupational Stress and Nutritional Status among Daily Wage Workers in Small-Scale Industries of India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Occupational Stress and Nutritional Status among Daily Wage Workers in Small-Scale Industries of India Tirthaspada Rout, Tapati Saha, Puspanjali Mohapatro, Supriya Sahoo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8783178/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract Background: Daily wage workers in small-scale industries frequently face job insecurity, financial stress, and substandard working conditions, which contribute to occupational stress and inadequate nutrition. Odisha reports a high prevalence of undernutrition, with many adults having a body mass index (BMI) below 18.5. Understanding stress and nutritional patterns among these workers is vital for designing nursing-led workplace health interventions. Methods: A descriptive cross-sectional study was conducted among 300 randomly selected daily wage workers from two small-scale industries in Bhubaneswar, Odisha. Data were collected using a sociodemographic questionnaire, the Perceived Stress Scale (PSS-10), and anthropometric measurements for BMI assessment. Data were analysed using SPSS version 20 with descriptive statistics, chi-square tests, Spearman’s correlation, and binary logistic regression. A p-value of < 0.05 was considered significant. Results: Among the participants, 55.7% reported moderate stress, 21.0% reported high stress, and 23.3% reported low stress. Regarding nutritional status, 60.0% had a normal BMI, 26.3% were underweight, 10.7% were overweight, and 3.0% were obese. Stress and nutritional status were weakly negatively correlated (r = − 0.06, p = 0.28). Stress was significantly associated with age, gender, marital status, job duration, dependents, and salary, while nutritional status showed no significant associations with sociodemographic variables. Conclusions: Daily wage workers experience substantial occupational stress and varying nutritional status. Integrating nursing-led workplace interventions that focus on stress reduction, nutrition education, and routine health assessments could enhance their overall well-being and productivity. Occupational stress Nutritional status Daily wage workers Small-scale industries Workplace health Introduction Small- and medium-sized industries (SMIs) are globally recognised as vital contributors to economic growth, employment generation, and social stability. They sustain livelihoods, foster innovation, and uplift vulnerable groups by creating job opportunities [ 1 ]. In India, the Micro, Small, and Medium Enterprises (MSME) sector employs over 110 million workers, including 31.95 lakh in small industries and 1.75 lakh in medium industries [ 2 ]. In Odisha alone, approximately 3.31 lakh small enterprises provide livelihoods to a substantial portion of the workforce [ 3 ]. Despite their economic significance, workers in SMIs often face precarious work conditions, irregular income, and minimal social protection, placing them at heightened risk of occupational stress and poor health outcomes. Work-related stress arises when job demands exceed an individual’s ability, skills, or coping capacity [ 4 ]. It can result from poor work organisation, unsafe environments, long hours, or job insecurity [ 5 ]. Prolonged exposure to such stress diminishes quality of life and contributes to cardiovascular, metabolic, and psychological disorders [ 6 ]. Among low-income populations, such as daily wage workers, financial instability and limited access to healthcare further magnify this vulnerability. The World Health Organisation recognises occupational stress as a major global public health concern [ 7 ]. Nutrition is another essential determinant of health and productivity. Adequate nutrition enhances resilience and supports physiological demands, whereas undernutrition remains prevalent in many low- and middle-income countries [ 8 ]. Concurrently, rapid lifestyle transitions have led to increasing overweight and obesity, creating a “double burden of malnutrition” [ 9 ]. In India, both extremes coexist — the National Family Health Survey (NFHS-4) reported that 36% of women have a body mass index (BMI) below 18.5, while overweight and obesity rates are rising in urban and coastal regions [ 10 ]. In Odisha, 55% of women and 24% of men are anaemic, reflecting widespread nutritional deficiencies [ 11 ]. Gender disparities in the workforce also warrant attention. According to the Ministry of Labour and Employment (2021–22), women constitute 149.8 million of India’s workforce, with a majority employed in informal and agricultural sectors [ 12 ]. Within SMIs, women often encounter dual challenges of workplace stress and inadequate nutrition, increasing their susceptibility to health problems. Although the economic and health impacts of occupational stress have been extensively documented in high-income countries [ 13 ], limited evidence exists on the relationship between stress and nutritional status in the Indian context. Most research has focused on maternal and child undernutrition [ 14 ] or the affordability of healthy diets [ 15 ], while adult industrial workers remain underrepresented. The Odisha State of Food Security and Nutrition Report (2020) highlighted the coexistence of undernutrition and overweight in the state, but the link between occupational stress and nutritional outcomes among industrial workers remains poorly understood [ 16 ]. Daily wage earners in small-scale industries represent a marginalised and vulnerable segment of the workforce. They often contend with unstable employment, occupational stress, and limited access to nutritious food. Understanding how these factors interact is crucial for designing effective, nursing-led interventions that aim to improve both occupational health and nutritional well-being. Therefore, this study was undertaken to assess the levels of perceived stress and nutritional status among daily wage workers in selected small-scale industries of Odisha, India. Materials and Methods Study design A quantitative, descriptive cross-sectional design was employed to assess stress levels and nutritional status among daily wage workers employed in small-scale industries in Bhubaneswar, Odisha, India. The study was conducted between June 6 and July 3, 2022, in two small-scale industries selected based on accessibility and administrative permission. Study population and setting The study population comprised daily wage workers employed in small-scale industries in Bhubaneswar. The study setting consisted of two industries, selected conveniently to meet the study objectives. A total of 300 participants who fulfilled the eligibility criteria were recruited. Sample size The minimum sample size was estimated using Cochran’s formula for proportions with 95% confidence and 5% margin of error, assuming maximum variability (p = 0.5): $$\:{n}_{0}=\frac{{N}^{2}p(1-p)}{{e}^{2}}$$ Applying the finite population correction for a total population of 8,300 daily wage workers yielded an adjusted sample size of approximately 367 participants. Considering field logistics and participant availability, a total of 300 participants were ultimately enrolled, with a response rate of 81.1% (370 workers invited, 300 consented). A post-hoc precision analysis indicated that with n = 300, the margin of error for a proportion estimate was approximately ± 5.6% at a 95% confidence level, which was considered acceptable for the descriptive objectives of this study. Sampling technique A simple random sampling method was applied. A lottery technique was used, where slips containing participant identifiers were thoroughly mixed, and individuals with odd numbers were selected. Inclusion and Exclusion criteria Inclusion criteria: (Daily wage workers who (1) were present during data collection, (2) could read and write Odia, and (3) provided written informed consent. Exclusion criteria: Workers unable to read or write Odia were excluded. Instruments Data were collected using three tools: Socio-demographic questionnaire : A structured tool with 11 items capturing participants’ personal and occupational characteristics such as age, sex, marital status, education, job duration, working hours, family size, dependents, salary, and per capita income. (Supplementary File 1: Part A: Sociodemographic questionnaire) Perceived Stress Scale (PSS-10) : A standardised 10-item instrument measuring perceived stress on a 5-point Likert scale (0 = never to 4 = very often). Total scores ranged from 0 to 40, categorised as low (0–13), moderate (14–26), and high stress (27–40). The PSS-10 has demonstrated high internal consistency (Cronbach’s α = 0.78–0.91). In the present study, Cronbach’s alpha was 0.82, confirming good reliability. (Supplementary File 1: Part B: Perceived Stress Scale) Anthropometric measurements : Body weight and height were measured using calibrated scales and a stadiometer. The Body Mass Index (BMI) was calculated as weight (kg) divided by height (m²) and classified according to World Health Organisation (WHO) reference standards[ 17 ]. Data collection procedure Prior permission was obtained from the administrative and human resources departments of the selected industries. The researchers explained the study purpose and procedures to participants and obtained written informed consent. Data were collected through face-to-face interviews to obtain sociodemographic details and perceived stress scores, followed by direct measurement of height and weight. Each interview lasted approximately 20 minutes. Data completeness and accuracy were verified on a daily basis. Ethical considerations Ethical approval was granted by the Institutional Ethics Committee of the Institute of Medical Sciences and SUM Hospital, Siksha ‘O’ Anusandhan University, Bhubaneswar (Approval No: Ref.no/IEC/IMS.SH/SOA/2022/368). Participation was voluntary, and confidentiality and anonymity were maintained throughout the study. Data analysis Data were coded and entered into Microsoft Excel, then analysed using SPSS version 20. Descriptive statistics (frequency, percentage, mean, and standard deviation) summarised participants’ characteristics, stress levels, and nutritional status. Inferential analyses included the chi-square test to determine associations between variables and Spearman’s correlation to examine relationships between stress and BMI. Binary logistic regression was used to identify predictors of moderate to high stress. Statistical significance was set at p < 0.05. Result Socio-demographic characteristics of participants A total of 300 daily waiger workers participated in the study. The majority (37.0%) were aged 30–39 years, while 32.3% were between 40 and 49 years. Most participants were male (78.8%), married (62.0%), and had a primary education (42.0%). Approximately one-third (33.3%) had 4–5 years of work experience, and 74.0% reported working 8 hours per day. Nearly two-thirds (67.0%) lived in nuclear families, and more than half (54.7%) had households with 3–4 members. Regarding income, 55.3% of participants earned ₹8,000–10,000 per month, and all participants reported a per capita monthly income between ₹2,000 and ₹ 5,000. Perceived stress levels among daily wage workers As shown in Table 1 , more than half of the participants (55.7%) reported a moderate level of stress, while 23.3% experienced low stress and 21.0% reported high stress according to the Perceived Stress Scale (PSS-10). Table 1 Distribution of perceived stress levels among daily wage workers (N = 300) Level of Stress PSS-10 score Range Frequency (n) Percentage (%) Low 0–13 70 23.3 Moderate 14–26 167 55.7 High 27–40 63 21.0 Total — 300 100.0 PSS-10 = Perceived Stress Scale (10 items). Stress levels classified according to Cohen et al. (1983). Nutritional status of daily wage workers Based on Body Mass Index (BMI) classification, the majority of participants (60.0%) had a healthy nutritional status, followed by 26.3% who were underweight, 10.7% who were overweight, and 3.0% who were obese (Table 2 ). These findings indicate that while most daily wage workers maintained a normal BMI, a substantial proportion exhibited undernutrition. Table 2 Nutritional status of daily wage workers based on BMI classification (N = 300) BMI (kg/m²) Nutritional status n (%) < 18.5 Underweight 79 (26.3) 18.5–24.9 Healthy 180 (60.0) 25.0–29.9 Overweight 32 (10.7) ≥ 30.0 Obese 9 (3.0) BMI classification based on World Health Organisation (WHO) reference standards (WHO, 2020). These findings indicate that while most daily wage workers maintained a normal BMI, a considerable proportion were undernourished. Correlation between perceived stress and nutritional status As shown in Table 3 , a weak negative correlation was observed between stress and nutritional status (r = − 0.06, p = 0.28), indicating that higher stress levels were slightly associated with lower BMI, although this relationship was not statistically significant. Table 3 Correlation between perceived stress and nutritional status among daily wage workers (N = 300) Characteristics MEAN ± SD r value p value Stress 20.2 ± 5.96 -0.06 0.28 Nutritional status 21.1 ± 0.69 Correlation computed using Spearman’s rank correlation coefficient. p < 0.05 was considered statistically significant. Association between perceived stress and socio-demographic variables As shown in Table 4 , stress levels were significantly associated with age, gender, marital status, job duration, working hours, family size, number of dependents, and monthly salary (all p < 0.05). No significant association was observed between education, family type, or per capita monthly income. Table 4 Association between perceived stress levels and socio-demographic characteristics of daily wage workers (N = 300) Variable χ² df p-value Association Age 21.05 6 < 0.001* Significant Gender 41.89 2 < 0.001* Significant Marital status 58.68 6 < 0.001* Significant Education 3.6 6 0.73 Not significant Duration of the job 35.24 6 < 0.001* Significant Working hours/day 10.95 4 0.028* Significant Type of family 9.44 4 0.06 Not significant Family size 32.24 6 < 0.001* Significant Dependents at home 16.34 4 0.003* Significant Monthly salary 25.62 4 < 0.001* Significant Per capita monthly income – – – Not significant *P < 0.05 = statistically significant Association between nutritional status with socio-demographic variables As shown in Table 5 , no significant associations were found between nutritional status and any of the selected socio-demographic variables, including job duration, working hours, type of family, family size, number of dependents, monthly salary, or per capita monthly income (p > 0.05). Table 5 Association between nutritional status and socio-demographic characteristics of daily wage workers (N = 300) Variable χ² df p-value Association Duration of job (years) 12.12 9 0.2 Not significant Working hours/day 4.41 6 0.62 Not significant Type of family 7.09 6 0.31 Not significant Family size 7.45 9 0.59 Not significant Dependents at home 3.08 6 0.79 Not significant Monthly salary 5.7 6 0.45 Not significant Per capita monthly income – – – Not significant Logistic regression predicting moderate/high stress Binary logistic regression analysis revealed that age, gender, marital status, job duration, number of dependents, and salary were significant predictors of moderate to high stress. Workers aged 40–49 years (OR = 5.92, 95% CI: 2.69–13.00) and 50–59 years (OR = 8.16, 95% CI: 2.10–31.64) were substantially more likely to report stress compared with those aged 20–29 years. Female workers were nearly five times more likely to experience stress than males (OR = 4.72, 95% CI: 2.28–9.76). Longer job duration (≥ 6 years: OR = 3.56, 95% CI: 1.66–7.63) and higher salary categories were also associated with greater stress. Workers with 7 or more dependents had significantly higher odds of experiencing stress (OR = 2.62, 95% CI: 1.14–6.03). Per capita income was not a significant predictor. The model explained 34% of the variance in stress outcomes (Nagelkerke R² = 0.34) and showed good fit (Hosmer–Lemeshow p = 0.62) (Table 6 ). Table 6 Binary Logistic Regression predicting Moderate/High Stress among Daily waiger workers (N = 300) Predictor Variable B (β) SE Wald χ² p-value OR (Exp(B)) 95% CI for OR Age group (Ref: 20–29 yrs) 30–39 yrs 0.92 0.35 6.89 0.009* 2.51 1.26–4.99 40–49 yrs 1.78 0.4 19.8 < 0.001* 5.92 2.69–13.00 50–59 yrs 2.1 0.68 9.55 0.002* 8.16 2.10–31.64 Gender (Ref: Male) Female 1.55 0.37 17.57 < 0.001* 4.72 2.28–9.76 Marital Status (Ref: Unmarried) Married 0.84 0.29 8.34 0.004* 2.31 1.30–4.09 Widow/Divorced 1.02 0.65 2.44 0.118 2.77 0.76–10.02 Duration of Job (Ref: 0–1 year) 2–3 yrs 0.61 0.33 3.45 0.063 1.84 0.96–3.54 4–5 yrs 0.92 0.34 7.36 0.007* 2.51 1.28–4.90 ≥ 6 yrs 1.27 0.39 10.65 0.001* 3.56 1.66–7.63 Dependents (Ref: 2–4) 5–6 0.41 0.32 1.64 0.2 1.51 0.80–2.85 ≥ 7 0.96 0.42 5.18 0.023* 2.62 1.14–6.03 Monthly Salary (Ref: ₹5000–7000) ₹8000–10000 0.83 0.31 7.14 0.008* 2.29 1.24–4.22 ₹11000–13000 1.2 0.42 8.16 0.004* 3.32 1.48–7.43 Model Fit: Nagelkerke R² = 0.34; Hosmer-Lemeshow χ² = 6.21, p = 0.62 Effect sizes for predictors of stress and nutritional status Cramer’s V indicated that marital status (V = 0.44), gender (V = 0.37), and job duration (V = 0.34) had the strongest associations with stress, while education and family type showed weak associations. Logistic regression analysis confirmed that older age, female gender, longer job tenure, higher salary, and having more dependents significantly increased the odds of experiencing stress (Table 7 ). In contrast, all predictors showed weak and non-significant associations with nutritional status, with Cramer’s V values below 0.15 (Table 8 ). Table 7 Effect sizes (Cramer’s V and Odds Ratios) for predictors of stress among daily waiger workers (N = 300) Predictor χ² (df) p -value Cramer’s V OR (95% CI) Interpretation Age group 21.05 (6) < 0.001* 0.26 40–49 yrs: 5.92 (2.4–10.3) 50–59 yrs: 8.16 (3.0–12.8) Older workers had higher odds of stress (large effect) Gender 41.89 (2) < 0.001* 0.37 Female: 4.72 (2.1–8.6) Females are more likely to report stress (large effect) Marital status 58.68 (6) < 0.001* 0.44 Married vs. unmarried: 2.15 (1.2–3.8) Strong association, marriage increased stress Education 3.60 (6) 0.73 0.11 – No significant effect Job duration (≥ 6 yrs) 35.24 (6) < 0.001* 0.34 3.56 (1.8–6.4) Longer duration predicted stress (moderate–large effect) Working hours/day 10.95 (4) 0.027* 0.19 10–12 hrs: 1.75 (1.1–3.0) Extended hours moderately increased stress Family type 9.44 (4) 0.06 0.18 – Not significant Dependents (≥ 7) 16.34 (4) 0.003* 0.23 2.62 (1.1–4.9) Larger families predicted stress (moderate effect) Salary/month 25.62 (4) < 0.001* 0.29 Higher salary: 1.8–2.3 Salary showed a small–moderate effect Per capita income – – – 1.0 (ns) Not a significant predictor P < 0.05 is statistically significant. OR = Odds Ratio; CI = Confidence Interval; ns = not significant. Table 8 Effect sizes (Cramer’s V and Odds Ratios) for predictors of nutritional status among daily wage workers (N = 300) Predictor χ² (df) p -value Cramer’s V OR (95% CI) Interpretation Duration of job (yrs) 12.12 (9) 0.2 0.12 – Weak, not significant Working hours/day 4.41 (6) 0.62 0.08 – No effect Family type 7.09 (6) 0.31 0.11 – No effect Family size 7.45 (9) 0.59 0.1 – Not significant Dependents at home 3.08 (6) 0.79 0.06 – No effect Salary/month 5.70 (6) 0.45 0.09 – No effect Per capita income – – – – Not significant Discussion This study examined stress levels and nutritional status among daily wage workers in small-scale industries in Bhubaneswar, Odisha. More than half of the participants reported moderate stress, while nearly one-fifth experienced high stress. These findings are consistent with earlier research, which has shown that informal sector workers are particularly prone to occupational stress due to job insecurity, low income, and inadequate social protection [ 16 , 18 ]. Age was a significant predictor of stress, with workers aged 40–59 years reporting higher odds compared with younger participants. Similar studies have noted that middle-aged workers often face increased financial and family responsibilities, leading to higher stress levels [ 19 , 20 ]. Gender also emerged as an important determinant; female workers were almost five times more likely to experience moderate or high stress than males, consistent with findings from Indian and international studies highlighting the dual burden of employment and domestic responsibilities faced by women [ 21 , 22 ]. Marital status and job duration were also significantly associated with stress. Married workers reported higher stress levels, possibly due to family obligations, childcare responsibilities, and economic pressure, as found in earlier occupational health research [ 23 ]. Longer job tenure (≥ 6 years) was linked to increased stress, possibly reflecting cumulative exposure to workplace hazards and monotonous tasks [ 24 ]. Interestingly, higher income was positively associated with stress, which may be explained by the increased workload, responsibility, and performance expectations that accompany higher wages [ 25 ]. Workers with larger families or more dependents also reported higher stress, supporting evidence that financial and caregiving burdens contribute to psychological strain [ 26 ]. In contrast, per capita income was not a significant predictor, suggesting that psychosocial factors such as job insecurity and physical strain may influence stress more strongly than income alone [ 27 ]. Regarding nutritional status, most participants had a healthy BMI, though 26.3% were underweight and 13.7% were either overweight or obese. These results mirror findings from other Indian and low-income settings where undernutrition and overnutrition coexist, indicating a “double burden of malnutrition” [ 28 , 29 ]. The absence of significant associations between nutritional status and sociodemographic factors suggests that lifestyle behaviours and dietary patterns may play a greater role than job-related characteristics [ 30 ]. The weak, negative correlation between stress and BMI suggests that stress alone may not directly affect nutritional status but could interact with coping mechanisms and food behaviours. Prior studies have shown mixed findings, some linking stress to appetite loss and weight reduction, while others associate it with overeating and weight gain [ 31 , 32 ]. The study highlights the multifaceted nature of occupational stress among daily wage workers and underscores the need for integrated workplace and community-level health interventions. Implications for practices The findings emphasise the importance of workplace-based interventions that address both psychological and nutritional well-being among daily wage earners. Occupational health programs should incorporate stress management strategies such as counselling, relaxation techniques, and peer support, alongside nutrition education and access to affordable, balanced meals. Nurses and community health professionals can play a crucial role by conducting periodic health assessments and stress screenings, providing guidance on stress-coping techniques and healthy dietary habits, collaborating with employers to ensure safe and health-promoting work environments, and advocating for policies that extend occupational health services to informal sector workers. By integrating these strategies, nursing professionals can contribute to reducing occupational stress, improving nutritional health, and enhancing productivity among vulnerable industrial populations. Strengths and Recommendations for Future Research A key strength of this study lies in its focus on daily wage workers a marginalised group often excluded from occupational health research. The use of validated tools, including the Perceived Stress Scale (PSS-10) and standardised anthropometric measurements, strengthens the reliability of findings. The study also followed ethical procedures and a systematic data collection process, ensuring methodological rigour. Future research should employ longitudinal or mixed-method designs to establish causal pathways between occupational stress, nutrition, and health outcomes. Including dietary assessments, biochemical measures, and qualitative interviews would enrich the understanding of behavioural and contextual influences on nutrition and stress. Comparative studies across industries and regions may also help generalise findings. From a nursing perspective, future studies should evaluate the effectiveness of workplace-based, nurse-led health promotion programs such as stress management workshops and nutrition education interventions aimed at improving both mental and physical health among informal sector workers. Limitation The study was conducted in only two selected small-scale industries in Bhubaneswar, which may limit the generalizability of findings. Stress levels were self-reported, which could introduce response bias. Nutritional status was assessed using BMI alone, without assessing dietary patterns or micronutrient deficiencies. Moreover, the cross-sectional design precludes the establishment of causal relationships between stress and nutritional status. Conclusion This study revealed that more than half of daily wage workers experienced moderate stress, and about one-fourth were underweight. Stress and nutritional status showed a weak, negative correlation. Significant predictors of stress included age, gender, marital status, job duration, number of dependents, and salary, whereas nutritional status was not associated with sociodemographic factors. The findings highlight the need for comprehensive workplace health programs. Nurse-led interventions focusing on stress management, nutrition education, and regular health screening can substantially improve the well-being and productivity of daily wage workers. Strengthening occupational health nursing practices within small-scale industries can help bridge existing care gaps and contribute to a more equitable workforce health. Abbreviations BMI Body Mass Index MSMEs Micro, Small, and Medium Enterprises NFHS National Family Health Survey PSS Perceived Stress Scale SPSS Statistical Package for the Social Sciences Declarations Ethical considerations and consent to participants Ethical clearance and approval were obtained from the Institutional Ethics Committee of the Institute of Medical Sciences and SUM Hospital, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar (Approval No: Ref.no/IEC/IMS.SH/SOA/2022/368). Formal permission was secured from the Manager of the Small-Scale Industry of Bhubaneswar, Odisha. The investigator introduced themselves to the participants, explained the purpose of the study, and assured them of confidentiality. Written informed consent was obtained from each participant before data collection. All procedures involving human participants were conducted in accordance with the ethical standards of the Institutional Ethics Committee and with the 1964 Helsinki Declaration and its later amendments. Consent for publication: Not applicable Availability of data and materials: The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: No funding has been received for the conduct of the study Author’s contribution Tirthaspada Rout, Tapati Saha, Puspanjali Mohapatro and Supriya Sahoo contributed to the conception and design of the study, participated in data collection, data analysis, and interpretation, and drafted the initial manuscript. Tirthaspada Rout, Puspanjali Mohapatro contributed to revising the study design, assisted with data analysis, and critically reviewed and edited the manuscript. All authors read and approved the final manuscript, agreed to submit to BMC Nursing, and accepted responsibility for the content of the work. Acknowledgements The authors would like to thank all the participants and the management of the selected small-scale industries for their support. Conclusion This study assessed the stress and nutritional status of daily wage workers in small-scale industries in Bhubaneswar. More than half experienced moderate stress, one-fifth reported high stress, and over a quarter were underweight, indicating nutritional risk. Stress and nutritional status were weakly negatively correlated, suggesting that poor nutrition may worsen stress. Significant predictors of stress included age, gender, marital status, job duration, dependents, and salary, while nutritional status was not associated with these variables. The findings emphasise the need for workplace health promotion through stress management, nutrition education, and access to balanced diets to improve the well-being and productivity of daily wage earners. References Singh A, Sharma R. Role of SMEs in economic development of India. Int J Appl Res. 2015;1(9):606–11. Ministry of Micro. Small and Medium Enterprises (MSME). Annual Report 2020–2021. Government of India, 2021. National Sample Survey Office (NSSO). Key indicators of unincorporated non-agricultural enterprises in India: NSS 73rd round (2016). Ministry of Statistics and Programme Implementation, Government of India; 2016. World Health Organisation (WHO). Stress at the workplace. Geneva: WHO; 2020. Cooper CL, Quick JC. The handbook of stress and health. Chichester: Wiley; 2017. Schneiderman N, Ironson G, Siegel SD. Stress and health: psychological, behavioural, and biological determinants. Annu Rev Clin Psychol. 2005;1:607–28. Ganster DC, Rosen CC. Work stress and employee health: a multidisciplinary review. J Manag. 2013;39(5):1085–122. Food and Agriculture Organisation (FAO). Human nutrition in the developing world. Rome: FAO; 2001. Popkin BM, Corvalan C, Grummer-Strawn LM. Dynamics of the double burden of malnutrition. Lancet. 2020;395(10217):65–74. International Institute for Population Sciences (IIPS) and ICF. National Family Health Survey (NFHS-4), 2015–16: India. Mumbai: IIPS; 2017. International Institute for Population Sciences (IIPS). NFHS-4 Fact Sheet: Odisha. Mumbai: IIPS; 2017. Ministry of Labour and Employment. Annual Report 2021–2022. Government of India; 2022. Bejean S, Sultan-Taieb H. Modelling the economic burden of diseases imputable to stress at work. Eur J Health Econ. 2005;6(1):16–23. Black RE, Victora CG, Walker SP, et al. Maternal and child undernutrition and overweight in low- and middle-income countries. Lancet. 2013;382(9890):427–51. Raghunathan K, Headey D, Herforth A. Affordability of nutritious diets in rural India. Agric Econ. 2020;51(2):1–15. International Labour Organisation (ILO). World Employment and Social Outlook: Trends 2023. Geneva: ILO; 2023. World Health Organization. Body mass index (BMI): Classification and interpretation. Geneva: WHO; 2020. Singh R, Choudhary SK. Occupational stress among industrial workers in India: a review. Ind Psychiatry J. 2018;27(2):222–30. Li J, Yang W, Cho S. Gender, age, and occupational stress: a cross-sectional study in Chinese workers. J Occup Health. 2019;61(2):173–81. Subramanian SV, Corsi DJ, Subramanyam MA, Davey Smith G. Jumping the gun: the problematic discourse on socioeconomic status and health in India. Int J Epidemiol. 2013;42(5):1410–26. Sharma N, Sharma P. Stress and coping among working women: a review of literature. Indian J Health Wellbeing. 2016;7(12):1136–9. Johnson JV, Hall EM. Job strain, workplace social support, and cardiovascular disease: a cross-sectional study of a random sample of the Swedish working population. Am J Public Health. 1988;78(10):1336–42. Jain R, Gupta S. Marital status and its effect on stress among industrial workers. J Health Psychol Res. 2017;3(1):45–52. Banerjee A, Sharma N. Job tenure and psychological stress among factory workers in eastern India. Ind Health. 2015;53(4):329–36. Sapolsky RM. Why zebras don’t get ulcers: stress and health. 3rd ed. New York: Holt Paperbacks; 2004. Rao S, Kulkarni S. Family burden and psychological stress among industrial employees. Indian J Occup Environ Med. 2017;21(3):101–7. Lundberg U, Cooper CL. The science of occupational health: stress, psychobiology, and the new world of work. Oxford: Wiley-Blackwell; 2011. Swaminathan S, Vaz M, Kurpad AV. Protein intakes in India. Br J Nutr. 2012;108(S2):S50–8. Popkin BM, Adair LS, Ng SW. Global Nutrition Transition and the Pandemic of Obesity in Developing Countries. Nutr Rev. 2012;70(1):3–21. Ghosh S. Malnutrition among workers in the unorganised sector in India. Econ Polit Wkly. 2014;49(31):56–63. Torres SJ, Nowson CA. Relationship between stress, eating behaviour, and obesity. Nutrition. 2007;23(11–12):887–94. Oliver G, Wardle J. Perceived effects of stress on food choice. Physiol Behav. 1999;66(3):511–5. Additional Declarations No competing interests reported. Supplementary Files supplementaryfile1.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers agreed at journal 03 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers invited by journal 26 Feb, 2026 Editor invited by journal 26 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 04 Feb, 2026 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-8783178","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":599502485,"identity":"4eb7a6b1-dae1-46e7-a52a-d5f59fd3ce40","order_by":0,"name":"Tirthaspada Rout","email":"","orcid":"","institution":"SUM Nursing College, Shiksha ‘O’ Anusandhan (SOA) University","correspondingAuthor":false,"prefix":"","firstName":"Tirthaspada","middleName":"","lastName":"Rout","suffix":""},{"id":599502486,"identity":"2f04cf1a-368a-4f46-8640-79ae8d1240f2","order_by":1,"name":"Tapati Saha","email":"","orcid":"","institution":"SUM Nursing College, Shiksha ‘O’ Anusandhan (SOA) University","correspondingAuthor":false,"prefix":"","firstName":"Tapati","middleName":"","lastName":"Saha","suffix":""},{"id":599502487,"identity":"a8b4cccf-af7e-40ca-8c59-e7045e8c29bd","order_by":2,"name":"Puspanjali Mohapatro","email":"data:image/png;base64,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","orcid":"","institution":"SUM Nursing College, Shiksha ‘O’ Anusandhan (SOA) University","correspondingAuthor":true,"prefix":"","firstName":"Puspanjali","middleName":"","lastName":"Mohapatro","suffix":""},{"id":599502488,"identity":"66e7e904-80cf-490d-8cc3-daefc4bc51c3","order_by":3,"name":"Supriya Sahoo","email":"","orcid":"","institution":"SUM Nursing College, Shiksha ‘O’ Anusandhan (SOA) University","correspondingAuthor":false,"prefix":"","firstName":"Supriya","middleName":"","lastName":"Sahoo","suffix":""}],"badges":[],"createdAt":"2026-02-04 07:39:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8783178/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8783178/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104808265,"identity":"d061b03b-39b9-4d13-a4af-652de6bbbac2","added_by":"auto","created_at":"2026-03-17 12:35:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1487313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8783178/v1/8f498597-f91f-4876-b0df-4013cf45b053.pdf"},{"id":103856173,"identity":"2d9ac041-4c12-438b-bead-09617a0b131a","added_by":"auto","created_at":"2026-03-03 18:15:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19069,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8783178/v1/2994be2f802b0d8983336c67.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eOccupational Stress and Nutritional Status among Daily Wage Workers in Small-Scale Industries of India\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSmall- and medium-sized industries (SMIs) are globally recognised as vital contributors to economic growth, employment generation, and social stability. They sustain livelihoods, foster innovation, and uplift vulnerable groups by creating job opportunities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In India, the Micro, Small, and Medium Enterprises (MSME) sector employs over 110\u0026nbsp;million workers, including 31.95 lakh in small industries and 1.75 lakh in medium industries [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Odisha alone, approximately 3.31 lakh small enterprises provide livelihoods to a substantial portion of the workforce [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite their economic significance, workers in SMIs often face precarious work conditions, irregular income, and minimal social protection, placing them at heightened risk of occupational stress and poor health outcomes.\u003c/p\u003e \u003cp\u003eWork-related stress arises when job demands exceed an individual\u0026rsquo;s ability, skills, or coping capacity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It can result from poor work organisation, unsafe environments, long hours, or job insecurity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Prolonged exposure to such stress diminishes quality of life and contributes to cardiovascular, metabolic, and psychological disorders [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among low-income populations, such as daily wage workers, financial instability and limited access to healthcare further magnify this vulnerability. The World Health Organisation recognises occupational stress as a major global public health concern [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNutrition is another essential determinant of health and productivity. Adequate nutrition enhances resilience and supports physiological demands, whereas undernutrition remains prevalent in many low- and middle-income countries [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Concurrently, rapid lifestyle transitions have led to increasing overweight and obesity, creating a \u0026ldquo;double burden of malnutrition\u0026rdquo; [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In India, both extremes coexist \u0026mdash; the National Family Health Survey (NFHS-4) reported that 36% of women have a body mass index (BMI) below 18.5, while overweight and obesity rates are rising in urban and coastal regions [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In Odisha, 55% of women and 24% of men are anaemic, reflecting widespread nutritional deficiencies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGender disparities in the workforce also warrant attention. According to the Ministry of Labour and Employment (2021\u0026ndash;22), women constitute 149.8\u0026nbsp;million of India\u0026rsquo;s workforce, with a majority employed in informal and agricultural sectors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Within SMIs, women often encounter dual challenges of workplace stress and inadequate nutrition, increasing their susceptibility to health problems. Although the economic and health impacts of occupational stress have been extensively documented in high-income countries [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], limited evidence exists on the relationship between stress and nutritional status in the Indian context. Most research has focused on maternal and child undernutrition [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] or the affordability of healthy diets [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while adult industrial workers remain underrepresented. The Odisha State of Food Security and Nutrition Report (2020) highlighted the coexistence of undernutrition and overweight in the state, but the link between occupational stress and nutritional outcomes among industrial workers remains poorly understood [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDaily wage earners in small-scale industries represent a marginalised and vulnerable segment of the workforce. They often contend with unstable employment, occupational stress, and limited access to nutritious food. Understanding how these factors interact is crucial for designing effective, nursing-led interventions that aim to improve both occupational health and nutritional well-being. Therefore, this study was undertaken to assess the levels of perceived stress and nutritional status among daily wage workers in selected small-scale industries of Odisha, India.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eA quantitative, descriptive cross-sectional design was employed to assess stress levels and nutritional status among daily wage workers employed in small-scale industries in Bhubaneswar, Odisha, India. The study was conducted between June 6 and July 3, 2022, in two small-scale industries selected based on accessibility and administrative permission.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population and setting\u003c/h3\u003e\n\u003cp\u003eThe study population comprised daily wage workers employed in small-scale industries in Bhubaneswar. The study setting consisted of two industries, selected conveniently to meet the study objectives. A total of 300 participants who fulfilled the eligibility criteria were recruited.\u003c/p\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eThe minimum sample size was estimated using Cochran’s formula for proportions with 95% confidence and 5% margin of error, assuming maximum variability (p = 0.5):\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{n}_{0}=\\frac{{N}^{2}p(1-p)}{{e}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e \u003cp\u003eApplying the finite population correction for a total population of 8,300 daily wage workers yielded an adjusted sample size of approximately 367 participants. Considering field logistics and participant availability, a total of 300 participants were ultimately enrolled, with a response rate of 81.1% (370 workers invited, 300 consented). A post-hoc precision analysis indicated that with \u003cem\u003en\u003c/em\u003e = 300, the margin of error for a proportion estimate was approximately ± 5.6% at a 95% confidence level, which was considered acceptable for the descriptive objectives of this study.\u003c/p\u003e\n\u003ch3\u003eSampling technique\u003c/h3\u003e\n\u003cp\u003eA simple random sampling method was applied. A lottery technique was used, where slips containing participant identifiers were thoroughly mixed, and individuals with odd numbers were selected.\u003c/p\u003e\n\u003ch3\u003eInclusion and Exclusion criteria\u003c/h3\u003e\n\u003cp\u003eInclusion criteria: (Daily wage workers who (1) were present during data collection, (2) could read and write Odia, and (3) provided written informed consent.\u003c/p\u003e \u003cp\u003eExclusion criteria: Workers unable to read or write Odia were excluded.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInstruments\u003c/h2\u003e \u003cp\u003eData were collected using three tools:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSocio-demographic questionnaire\u003c/b\u003e: A structured tool with 11 items capturing participants’ personal and occupational characteristics such as age, sex, marital status, education, job duration, working hours, family size, dependents, salary, and per capita income. (Supplementary File 1: Part A: Sociodemographic questionnaire)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePerceived Stress Scale (PSS-10)\u003c/b\u003e: A standardised 10-item instrument measuring perceived stress on a 5-point Likert scale (0 = never to 4 = very often). Total scores ranged from 0 to 40, categorised as low (0–13), moderate (14–26), and high stress (27–40). The PSS-10 has demonstrated high internal consistency (Cronbach’s α = 0.78–0.91). In the present study, Cronbach’s alpha was 0.82, confirming good reliability. (Supplementary File 1: Part B: Perceived Stress Scale)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAnthropometric measurements\u003c/b\u003e: Body weight and height were measured using calibrated scales and a stadiometer. The Body Mass Index (BMI) was calculated as weight (kg) divided by height (m²) and classified according to World Health Organisation (WHO) reference standards[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection procedure\u003c/h3\u003e\n\u003cp\u003ePrior permission was obtained from the administrative and human resources departments of the selected industries. The researchers explained the study purpose and procedures to participants and obtained written informed consent. Data were collected through face-to-face interviews to obtain sociodemographic details and perceived stress scores, followed by direct measurement of height and weight. Each interview lasted approximately 20 minutes. Data completeness and accuracy were verified on a daily basis.\u003c/p\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003c/p\u003e\u003cp\u003e was granted by the Institutional Ethics Committee of the Institute of Medical Sciences and SUM Hospital, Siksha ‘O’ Anusandhan University, Bhubaneswar (Approval No: Ref.no/IEC/IMS.SH/SOA/2022/368). Participation was voluntary, and confidentiality and anonymity were maintained throughout the study.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData were coded and entered into Microsoft Excel, then analysed using SPSS version 20. Descriptive statistics (frequency, percentage, mean, and standard deviation) summarised participants’ characteristics, stress levels, and nutritional status. Inferential analyses included the chi-square test to determine associations between variables and Spearman’s correlation to examine relationships between stress and BMI. Binary logistic regression was used to identify predictors of moderate to high stress. Statistical significance was set at p \u0026lt; 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003ch2\u003eSocio-demographic characteristics of participants\u003c/h2\u003e\u003cp\u003eA total of 300 daily waiger workers participated in the study. The majority (37.0%) were aged 30–39 years, while 32.3% were between 40 and 49 years. Most participants were male (78.8%), married (62.0%), and had a primary education (42.0%). Approximately one-third (33.3%) had 4–5 years of work experience, and 74.0% reported working 8 hours per day. Nearly two-thirds (67.0%) lived in nuclear families, and more than half (54.7%) had households with 3–4 members. Regarding income, 55.3% of participants earned ₹8,000–10,000 per month, and all participants reported a per capita monthly income between ₹2,000 and ₹ 5,000.\u003c/p\u003e\u003ch2\u003ePerceived stress levels among daily wage workers\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, more than half of the participants (55.7%) reported a moderate level of stress, while 23.3% experienced low stress and 21.0% reported high stress according to the Perceived Stress Scale (PSS-10).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of perceived stress levels among daily wage workers (N = 300)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eLevel of Stress\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePSS-10 score Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eLow\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0–13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eModerate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e14–26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e55.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eHigh\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e27–40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e—\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cem\u003ePSS-10 = Perceived Stress Scale (10 items). Stress levels classified according to Cohen et al. (1983).\u003c/em\u003e \u003c/p\u003e\u003ch2\u003eNutritional status of daily wage workers\u003c/h2\u003e\u003cp\u003eBased on Body Mass Index (BMI) classification, the majority of participants (60.0%) had a healthy nutritional status, followed by 26.3% who were underweight, 10.7% who were overweight, and 3.0% who were obese (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings indicate that while most daily wage workers maintained a normal BMI, a substantial proportion exhibited undernutrition.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutritional status of daily wage workers based on BMI classification (N = 300)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eBMI (kg/m²)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eNutritional status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e79 (26.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18.5–24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e180 (60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25.0–29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e32 (10.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e9 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cem\u003eBMI classification based on World Health Organisation (WHO) reference standards (WHO, 2020).\u003c/em\u003e \u003c/p\u003e\u003cp\u003eThese findings indicate that while most daily wage workers maintained a normal BMI, a considerable proportion were undernourished.\u003c/p\u003e\u003ch2\u003eCorrelation between perceived stress and nutritional status\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, a weak negative correlation was observed between stress and nutritional status (r = − 0.06, p = 0.28), indicating that higher stress levels were slightly associated with lower BMI, although this relationship was not statistically significant.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab3\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between perceived stress and nutritional status among daily wage workers (N = 300)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eMEAN ± SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003er value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e20.2 ± 5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNutritional status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e21.1 ± 0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cem\u003eCorrelation computed using Spearman’s rank correlation coefficient. p \u0026lt; 0.05 was considered statistically significant.\u003c/em\u003e \u003c/p\u003e\u003ch2\u003eAssociation between perceived stress and socio-demographic variables\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, stress levels were significantly associated with age, gender, marital status, job duration, working hours, family size, number of dependents, and monthly salary (all p \u0026lt; 0.05). No significant association was observed between education, family type, or per capita monthly income.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab4\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between perceived stress levels and socio-demographic characteristics of daily wage workers (N = 300)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eχ²\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eAssociation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e21.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e41.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e58.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDuration of the job\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e35.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWorking hours/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.028*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eType of family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e32.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDependents at home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e16.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMonthly salary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSignificant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePer capita monthly income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*P \u0026lt; 0.05 = statistically significant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e\u003ch2\u003eAssociation between nutritional status with socio-demographic variables\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, no significant associations were found between nutritional status and any of the selected socio-demographic variables, including job duration, working hours, type of family, family size, number of dependents, monthly salary, or per capita monthly income (p \u0026gt; 0.05).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab5\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between nutritional status and socio-demographic characteristics of daily wage workers (N = 300)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eχ²\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eAssociation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDuration of job (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWorking hours/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eType of family\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDependents at home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMonthly salary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePer capita monthly income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003ch2\u003eLogistic regression predicting moderate/high stress\u003c/h2\u003e\u003cp\u003eBinary logistic regression analysis revealed that age, gender, marital status, job duration, number of dependents, and salary were significant predictors of moderate to high stress. Workers aged 40–49 years (OR = 5.92, 95% CI: 2.69–13.00) and 50–59 years (OR = 8.16, 95% CI: 2.10–31.64) were substantially more likely to report stress compared with those aged 20–29 years. Female workers were nearly five times more likely to experience stress than males (OR = 4.72, 95% CI: 2.28–9.76). Longer job duration (≥ 6 years: OR = 3.56, 95% CI: 1.66–7.63) and higher salary categories were also associated with greater stress. Workers with 7 or more dependents had significantly higher odds of experiencing stress (OR = 2.62, 95% CI: 1.14–6.03). Per capita income was not a significant predictor. The model explained 34% of the variance in stress outcomes (Nagelkerke R² = 0.34) and showed good fit (Hosmer–Lemeshow p = 0.62) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab6\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinary Logistic Regression predicting Moderate/High Stress among Daily waiger workers (N = 300)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePredictor Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eB (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eWald χ²\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eOR (Exp(B))\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e95% CI for OR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e (Ref: 20–29 yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e30–39 yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.26–4.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e40–49 yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.69–13.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e50–59 yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e9.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.10–31.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGender (Ref: Male)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e17.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.28–9.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eMarital Status (Ref: Unmarried)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.30–4.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWidow/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.76–10.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eDuration of Job (Ref: 0–1\u0026nbsp;year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2–3 yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.96–3.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4–5 yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e7.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.28–4.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 6 yrs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e10.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.66–7.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDependents (Ref: 2–4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5–6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.80–2.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.023*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.14–6.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eMonthly Salary (Ref: ₹5000–7000)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e₹8000–10000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e7.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.008*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.24–4.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e₹11000–13000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.48–7.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003ch2\u003eModel Fit: Nagelkerke R² = 0.34; Hosmer-Lemeshow χ² = 6.21, p = 0.62\u003c/h2\u003e\u003ch2\u003eEffect sizes for predictors of stress and nutritional status\u003c/h2\u003e\u003cp\u003eCramer’s V indicated that marital status (V = 0.44), gender (V = 0.37), and job duration (V = 0.34) had the strongest associations with stress, while education and family type showed weak associations. Logistic regression analysis confirmed that older age, female gender, longer job tenure, higher salary, and having more dependents significantly increased the odds of experiencing stress (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). In contrast, all predictors showed weak and non-significant associations with nutritional status, with Cramer’s V values below 0.15 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab7\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect sizes (Cramer’s V and Odds Ratios) for predictors of stress among daily waiger workers (N = 300)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eχ² (df)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eCramer’s V\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e21.05 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e40–49 yrs: 5.92 (2.4–10.3) 50–59 yrs: 8.16 (3.0–12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eOlder workers had higher odds of stress (large effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e41.89 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFemale: 4.72 (2.1–8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFemales are more likely to report stress (large effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e58.68 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMarried vs. unmarried: 2.15 (1.2–3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eStrong association, marriage increased stress\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.60 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo significant effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eJob duration (≥ 6 yrs)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e35.24 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.56 (1.8–6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLonger duration predicted stress (moderate–large effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eWorking hours/day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10.95 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.027*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10–12 hrs: 1.75 (1.1–3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eExtended hours moderately increased stress\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eFamily type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9.44 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eDependents (≥ 7)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e16.34 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.62 (1.1–4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLarger families predicted stress (moderate effect)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSalary/month\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25.62 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHigher salary: 1.8–2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSalary showed a small–moderate effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ePer capita income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.0 (ns)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot a significant predictor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003e \u003cb\u003eP \u0026lt; 0.05 is statistically significant. OR = Odds Ratio; CI = Confidence Interval; ns = not significant.\u003c/b\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab8\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect sizes (Cramer’s V and Odds Ratios) for predictors of nutritional status among daily wage workers (N = 300)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eχ² (df)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eCramer’s V\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eDuration of job (yrs)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12.12 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWeak, not significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eWorking hours/day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.41 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eFamily type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7.09 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eFamily size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7.45 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eDependents at home\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.08 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003eSalary/month\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5.70 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u003cb\u003ePer capita income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e–\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNot significant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined stress levels and nutritional status among daily wage workers in small-scale industries in Bhubaneswar, Odisha. More than half of the participants reported moderate stress, while nearly one-fifth experienced high stress. These findings are consistent with earlier research, which has shown that informal sector workers are particularly prone to occupational stress due to job insecurity, low income, and inadequate social protection [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAge was a significant predictor of stress, with workers aged 40\u0026ndash;59 years reporting higher odds compared with younger participants. Similar studies have noted that middle-aged workers often face increased financial and family responsibilities, leading to higher stress levels [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Gender also emerged as an important determinant; female workers were almost five times more likely to experience moderate or high stress than males, consistent with findings from Indian and international studies highlighting the dual burden of employment and domestic responsibilities faced by women [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMarital status and job duration were also significantly associated with stress. Married workers reported higher stress levels, possibly due to family obligations, childcare responsibilities, and economic pressure, as found in earlier occupational health research [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Longer job tenure (\u0026ge;\u0026thinsp;6 years) was linked to increased stress, possibly reflecting cumulative exposure to workplace hazards and monotonous tasks [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Interestingly, higher income was positively associated with stress, which may be explained by the increased workload, responsibility, and performance expectations that accompany higher wages [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Workers with larger families or more dependents also reported higher stress, supporting evidence that financial and caregiving burdens contribute to psychological strain [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In contrast, per capita income was not a significant predictor, suggesting that psychosocial factors such as job insecurity and physical strain may influence stress more strongly than income alone [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRegarding nutritional status, most participants had a healthy BMI, though 26.3% were underweight and 13.7% were either overweight or obese. These results mirror findings from other Indian and low-income settings where undernutrition and overnutrition coexist, indicating a \u0026ldquo;double burden of malnutrition\u0026rdquo; [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The absence of significant associations between nutritional status and sociodemographic factors suggests that lifestyle behaviours and dietary patterns may play a greater role than job-related characteristics [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The weak, negative correlation between stress and BMI suggests that stress alone may not directly affect nutritional status but could interact with coping mechanisms and food behaviours. Prior studies have shown mixed findings, some linking stress to appetite loss and weight reduction, while others associate it with overeating and weight gain [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study highlights the multifaceted nature of occupational stress among daily wage workers and underscores the need for integrated workplace and community-level health interventions.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eImplications for practices\u003c/h2\u003e \u003cp\u003eThe findings emphasise the importance of workplace-based interventions that address both psychological and nutritional well-being among daily wage earners. Occupational health programs should incorporate stress management strategies such as counselling, relaxation techniques, and peer support, alongside nutrition education and access to affordable, balanced meals.\u003c/p\u003e \u003cp\u003eNurses and community health professionals can play a crucial role by conducting periodic health assessments and stress screenings, providing guidance on stress-coping techniques and healthy dietary habits, collaborating with employers to ensure safe and health-promoting work environments, and advocating for policies that extend occupational health services to informal sector workers.\u003c/p\u003e \u003cp\u003eBy integrating these strategies, nursing professionals can contribute to reducing occupational stress, improving nutritional health, and enhancing productivity among vulnerable industrial populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Recommendations for Future Research\u003c/h2\u003e \u003cp\u003eA key strength of this study lies in its focus on daily wage workers a marginalised group often excluded from occupational health research. The use of validated tools, including the Perceived Stress Scale (PSS-10) and standardised anthropometric measurements, strengthens the reliability of findings. The study also followed ethical procedures and a systematic data collection process, ensuring methodological rigour.\u003c/p\u003e \u003cp\u003eFuture research should employ longitudinal or mixed-method designs to establish causal pathways between occupational stress, nutrition, and health outcomes. Including dietary assessments, biochemical measures, and qualitative interviews would enrich the understanding of behavioural and contextual influences on nutrition and stress. Comparative studies across industries and regions may also help generalise findings.\u003c/p\u003e \u003cp\u003eFrom a nursing perspective, future studies should evaluate the effectiveness of workplace-based, nurse-led health promotion programs such as stress management workshops and nutrition education interventions aimed at improving both mental and physical health among informal sector workers.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eThe study was conducted in only two selected small-scale industries in Bhubaneswar, which may limit the generalizability of findings. Stress levels were self-reported, which could introduce response bias. Nutritional status was assessed using BMI alone, without assessing dietary patterns or micronutrient deficiencies. Moreover, the cross-sectional design precludes the establishment of causal relationships between stress and nutritional status.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study revealed that more than half of daily wage workers experienced moderate stress, and about one-fourth were underweight. Stress and nutritional status showed a weak, negative correlation. Significant predictors of stress included age, gender, marital status, job duration, number of dependents, and salary, whereas nutritional status was not associated with sociodemographic factors. The findings highlight the need for comprehensive workplace health programs. Nurse-led interventions focusing on stress management, nutrition education, and regular health screening can substantially improve the well-being and productivity of daily wage workers. Strengthening occupational health nursing practices within small-scale industries can help bridge existing care gaps and contribute to a more equitable workforce health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMSMEs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMicro, Small, and Medium Enterprises\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNFHS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Family Health Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePerceived Stress Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSPSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical considerations and consent to participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical clearance and approval were obtained from the Institutional Ethics Committee of the Institute of Medical Sciences and SUM Hospital, Siksha \u0026lsquo;O\u0026rsquo; Anusandhan (Deemed to be University), Bhubaneswar (Approval No: Ref.no/IEC/IMS.SH/SOA/2022/368). Formal permission was secured from the Manager of the Small-Scale Industry of Bhubaneswar, Odisha. The investigator introduced themselves to the participants, explained the purpose of the study, and assured them of confidentiality. Written informed consent was obtained from each participant before data collection. All procedures involving human participants were conducted in accordance with the ethical standards of the Institutional Ethics Committee and with the 1964 Helsinki Declaration and its later amendments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Availability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding has been received for the conduct of the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTirthaspada Rout, Tapati Saha, Puspanjali Mohapatro and Supriya Sahoo contributed to the conception and design of the study, participated in data collection, data analysis, and interpretation, and drafted the initial manuscript. Tirthaspada Rout, Puspanjali Mohapatro contributed to revising the study design, assisted with data analysis, and critically reviewed and edited the manuscript. All authors read and approved the final manuscript, agreed to submit to BMC Nursing, and accepted responsibility for the content of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all the participants and the management of the selected small-scale industries for their support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study assessed the stress and nutritional status of daily wage workers in small-scale industries in Bhubaneswar. More than half experienced moderate stress, one-fifth reported high stress, and over a quarter were underweight, indicating nutritional risk. Stress and nutritional status were weakly negatively correlated, suggesting that poor nutrition may worsen stress. Significant predictors of stress included age, gender, marital status, job duration, dependents, and salary, while nutritional status was not associated with these variables. The findings emphasise the need for workplace health promotion through stress management, nutrition education, and access to balanced diets to improve the well-being and productivity of daily wage earners.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSingh A, Sharma R. Role of SMEs in economic development of India. Int J Appl Res. 2015;1(9):606\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Micro. Small and Medium Enterprises (MSME). \u003cem\u003eAnnual Report 2020\u0026ndash;2021.\u003c/em\u003e Government of India, 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Sample Survey Office (NSSO). Key indicators of unincorporated non-agricultural enterprises in India: NSS 73rd round (2016). Ministry of Statistics and Programme Implementation, Government of India; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organisation (WHO). Stress at the workplace. Geneva: WHO; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCooper CL, Quick JC. The handbook of stress and health. Chichester: Wiley; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneiderman N, Ironson G, Siegel SD. Stress and health: psychological, behavioural, and biological determinants. Annu Rev Clin Psychol. 2005;1:607\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGanster DC, Rosen CC. Work stress and employee health: a multidisciplinary review. J Manag. 2013;39(5):1085\u0026ndash;122.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFood and Agriculture Organisation (FAO). Human nutrition in the developing world. Rome: FAO; 2001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePopkin BM, Corvalan C, Grummer-Strawn LM. Dynamics of the double burden of malnutrition. Lancet. 2020;395(10217):65\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Institute for Population Sciences (IIPS) and ICF. National Family Health Survey (NFHS-4), 2015\u0026ndash;16: India. Mumbai: IIPS; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Institute for Population Sciences (IIPS). NFHS-4 Fact Sheet: Odisha. Mumbai: IIPS; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinistry of Labour and Employment. \u003cem\u003eAnnual Report 2021\u0026ndash;2022.\u003c/em\u003e Government of India; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBejean S, Sultan-Taieb H. Modelling the economic burden of diseases imputable to stress at work. Eur J Health Econ. 2005;6(1):16\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlack RE, Victora CG, Walker SP, et al. Maternal and child undernutrition and overweight in low- and middle-income countries. Lancet. 2013;382(9890):427\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaghunathan K, Headey D, Herforth A. Affordability of nutritious diets in rural India. Agric Econ. 2020;51(2):1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Labour Organisation (ILO). World Employment and Social Outlook: Trends 2023. Geneva: ILO; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Body mass index (BMI): Classification and interpretation. Geneva: WHO; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh R, Choudhary SK. Occupational stress among industrial workers in India: a review. Ind Psychiatry J. 2018;27(2):222\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Yang W, Cho S. Gender, age, and occupational stress: a cross-sectional study in Chinese workers. J Occup Health. 2019;61(2):173\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian SV, Corsi DJ, Subramanyam MA, Davey Smith G. Jumping the gun: the problematic discourse on socioeconomic status and health in India. Int J Epidemiol. 2013;42(5):1410\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma N, Sharma P. Stress and coping among working women: a review of literature. Indian J Health Wellbeing. 2016;7(12):1136\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson JV, Hall EM. Job strain, workplace social support, and cardiovascular disease: a cross-sectional study of a random sample of the Swedish working population. Am J Public Health. 1988;78(10):1336\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJain R, Gupta S. Marital status and its effect on stress among industrial workers. J Health Psychol Res. 2017;3(1):45\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBanerjee A, Sharma N. Job tenure and psychological stress among factory workers in eastern India. Ind Health. 2015;53(4):329\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSapolsky RM. Why zebras don\u0026rsquo;t get ulcers: stress and health. 3rd ed. New York: Holt Paperbacks; 2004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRao S, Kulkarni S. Family burden and psychological stress among industrial employees. Indian J Occup Environ Med. 2017;21(3):101\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLundberg U, Cooper CL. The science of occupational health: stress, psychobiology, and the new world of work. Oxford: Wiley-Blackwell; 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwaminathan S, Vaz M, Kurpad AV. Protein intakes in India. Br J Nutr. 2012;108(S2):S50\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePopkin BM, Adair LS, Ng SW. Global Nutrition Transition and the Pandemic of Obesity in Developing Countries. Nutr Rev. 2012;70(1):3\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhosh S. Malnutrition among workers in the unorganised sector in India. Econ Polit Wkly. 2014;49(31):56\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorres SJ, Nowson CA. Relationship between stress, eating behaviour, and obesity. Nutrition. 2007;23(11\u0026ndash;12):887\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliver G, Wardle J. Perceived effects of stress on food choice. Physiol Behav. 1999;66(3):511\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Occupational stress, Nutritional status, Daily wage workers, Small-scale industries, Workplace health","lastPublishedDoi":"10.21203/rs.3.rs-8783178/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8783178/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDaily wage workers in small-scale industries frequently face job insecurity, financial stress, and substandard working conditions, which contribute to occupational stress and inadequate nutrition. Odisha reports a high prevalence of undernutrition, with many adults having a body mass index (BMI) below 18.5. Understanding stress and nutritional patterns among these workers is vital for designing nursing-led workplace health interventions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA descriptive cross-sectional study was conducted among 300 randomly selected daily wage workers from two small-scale industries in Bhubaneswar, Odisha. Data were collected using a sociodemographic questionnaire, the Perceived Stress Scale (PSS-10), and anthropometric measurements for BMI assessment. Data were analysed using SPSS version 20 with descriptive statistics, chi-square tests, Spearman\u0026rsquo;s correlation, and binary logistic regression. A p-value of \u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAmong the participants, 55.7% reported moderate stress, 21.0% reported high stress, and 23.3% reported low stress. Regarding nutritional status, 60.0% had a normal BMI, 26.3% were underweight, 10.7% were overweight, and 3.0% were obese. Stress and nutritional status were weakly negatively correlated (r = \u0026minus;\u0026thinsp;0.06, p\u0026thinsp;=\u0026thinsp;0.28). Stress was significantly associated with age, gender, marital status, job duration, dependents, and salary, while nutritional status showed no significant associations with sociodemographic variables.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDaily wage workers experience substantial occupational stress and varying nutritional status. Integrating nursing-led workplace interventions that focus on stress reduction, nutrition education, and routine health assessments could enhance their overall well-being and productivity.\u003c/p\u003e","manuscriptTitle":"Occupational Stress and Nutritional Status among Daily Wage Workers in Small-Scale Industries of India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 18:15:51","doi":"10.21203/rs.3.rs-8783178/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-18T04:38:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T19:03:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T09:03:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T06:50:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T14:26:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"46593388129930117317526210649719592795","date":"2026-03-04T05:30:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36437290122329837854916969890483615585","date":"2026-03-03T16:26:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26220500204781375846880020144738781203","date":"2026-03-02T17:34:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159288183629631281893186595375869562553","date":"2026-02-26T17:06:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63881813131255030996217451026600600485","date":"2026-02-26T16:54:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-26T15:55:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-26T09:37:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-09T09:12:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-09T09:10:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-02-04T07:06:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c39cb54b-5166-4572-a093-f6bd073950ac","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T03:53:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 18:15:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8783178","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8783178","identity":"rs-8783178","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}