A statistical research study analyzing how the COVID-19 pandemic disrupted
the work-life balance of frontline workers — doctors, nurses, police, and
Anganwadi teachers — across Mysore and Mandya districts.
This project was submitted as Phase 2 of the final year project at PESCE Mandya, under the guidance of Dr. Rudresh Addamani, Associate Professor & HOD, Department of Mechanical Engineering. Survey conducted across government hospitals and police stations in Mysore and Mandya.
The COVID-19 pandemic placed extraordinary pressure on frontline workers — not just physically, but psychologically. While the world stayed home, doctors, nurses, police officers, and Anganwadi teachers continued working, often without adequate PPE, clear guidelines, or organizational support.
Work-life balance (WLB) — defined by Greenhause (2002) as satisfaction and good functioning at work and at home with minimum role conflict — became nearly impossible to achieve. Heavy workloads, fear of infecting family members, staff shortages, and absence of mental health support created a compounding crisis that rarely made headlines.
Understanding the WLB disruption of frontline workers is not just academic — it directly informs how organizations, governments, and healthcare systems must prepare for future pandemics. Without institutional reform, the same collapse in worker wellbeing will repeat.
| Occupation | Target | Approached | Responses | WLB Balanced |
|---|---|---|---|---|
| Doctors | 250 | 209 | 187 | 82 / 187 |
| Nurses | 150 | 144 | 121 | 38 / 121 |
| Police | 150 | 139 | 106 | 47 / 106 |
| Anganwadi Teachers | 100 | 80 | 63 | 12 / 63 |
| Total | 650 | 572 | 477 | 179 / 477 |
Geographic focus: government hospitals, police stations, and Anganwadi centres across Mysore and Mandya districts, Karnataka.
A structured 20-question questionnaire was designed across 11 WLB factors. Questions used a 5-point Likert scale (1 = Extremely Poor → 5 = Excellent). Section 1 captured demographic data; Section 2 captured ordinal WLB experience data during COVID-19.
Reviewed 23 published papers on COVID-19's psychological impact, WLB disruption, and frontline worker experiences across India, UK, Spain, Italy, and China.
WLB Policies · Team & Org Support · Work Load · Job Stress · Flexibility · Working Hours · Family Support · Healthcare · Media Opinions · Job Satisfaction · Work Commitment
Data collected via social media and in-person at government hospitals and police stations in Mysore and Mandya. 572 approached; 477 complete, valid responses retained.
Descriptive Statistics · Percentage Analysis · Multiple Linear Regression · Logistic Regression · Chi-Square Test of Independence · Mann-Whitney U Test · One-Way ANOVA
Results analyzed across gender, age group, and occupation. Regression models identified the four most significant factors driving work commitment and WLB outcomes.
Work-Life Balance Outcomes
Across both multiple regression and logistic regression models, family support emerged as the single largest contributor to work commitment (coefficient: 0.958 in logistic model).
Extended working hours during the pandemic significantly impacted WLB. Regression confirmed working hours as one of the top 4 independent factors affecting work commitment.
Chi-square tests confirmed gender has a statistically significant relationship with WLB (p = 4.2×10⁻⁶), job satisfaction (p = 0.0007), and work commitment (p = 0.0004).
Surprisingly, chi-square showed no statistically significant relationship between occupation type and job satisfaction (p = 0.087 > 0.05) — dissatisfaction was universal across roles.
Welch ANOVA confirmed significant WLB difference across the 4 groups (F = 6.739, p < 0.001). Anganwadi teachers had the lowest WLB mean (0.190) vs. Police (0.443).
Team and organizational support was one of the 4 final factors in both regression models. Organizations that provided structured support saw better WLB outcomes.
The four factors that mattered most — family support, working hours, job stress, and team/organizational support — are all controllable through policy. This study provides actionable levers for healthcare administrators and policymakers to improve frontline resilience before the next crisis.
Two regression models were built using backward elimination — starting with 9 independent WLB factors and reducing to only statistically significant predictors.
| Model | Outcome Variable | Final Factors (p < 0.05) | Method |
|---|---|---|---|
Model 1 |
Work Commitment | Family Support · Working Hours · Job Stress · Org Support | Multiple Linear Regression (Backward) |
Model 2 |
WLB (Balanced/Not) | Team Support · Workload · Job Stress · Family Support · Healthcare | Logistic Regression (Backward) |
Final multiple regression equation for Work Commitment:
F3 = Family Support · F4 = Working Hours · F5 = Job Stress · F8 = Org Support