Shift in work-life balance in Pakistan due to COVID-19 Pandemic: Analysis through the mental health lens (Preprint)
Bibliographic record
Abstract
BACKGROUND The outburst of infectious diseases threatens the psychological stability and disrupts work-life balance. OBJECTIVE The objective of this study is to investigate shift in work-life balance due to COVID-19 pandemic by examining the mental health status of general population of Pakistan. METHODS An online survey was conducted during COVID-19 peak (23-June till 29-June 2020). Out of 2050, 1775 responses from Karachi were included in this study. T-tests and ANOVAs were performed to identify difference among sociodemographic factors. Stepwise linear regression was conducted to determine association of depression, anxiety and stress with each factor of sociodemographic, knowledge and concern, contact, occupational and psychological variables. RESULTS In total, 30%, 22.2% and 21.96% of respondents reported severe to extremely severe depression, anxiety and stress on the Depression, anxiety stress scales (DASS). Well educated, unmarried, small scale business holders and changes in income due to COVID-19 reported higher depression. Moreover, respondents who acquired infection, consulted a doctor in fear of being COVID-19 positive, teleworkers and variations in earning reported being more anxious. Female gender, frontline workers, respondents with increased online working hours, clinical consultation doubtful of virus positive and self-acquisition of virus reported higher stress. The attributes of age (under 30 years), more concern regarding virus contraction from others, lower survival chance, new cases, COVID-19 dreams, lockdown, and spending more time on social media, strongly correlated with DASS scores. CONCLUSIONS This study confirms that the outbreak of COVID-19 pandemic has adversely impacted work-life balance of general population of Pakistan. Moreover, the aforementioned variables allow health sectors, government authorities and social media to design guidelines necessary for controlling negative post-pandemic effect and future infections outbreaks. CLINICALTRIAL NED University of Engineering & Technology
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".