Association Between Employment Status and Mental Health of People: An Evidence-Based Analysis in The Post Covid-19 Era
Bibliographic record
Abstract
The COVID-19 pandemic has inflated many megacities in the world and petrified the mental health of people. Mental health complications of people during this pandemic were spread at different levels. This situation created the ground for this study to see whether the employment status of people was in line with fluctuating mental health conditions in Dhaka City Corporation during this invasion or in the immediate past. It was a cross-sectional study that applied a multistage sampling method to define sample size. It selected the participants randomly and collected data through a self-administered structured questionnaire. This questionnaire was based on the DASS-21 to measure the conditions of mental health stability. Different statistical tools, including cross-tabulation, were used to reveal the association between the variables, and a chi-square test was conducted to examine the significance of such association. The findings of the study exposed the stern predisposition of mental health situations to employment status in the Dhaka City Corporation during and immediately after the COVID-19 invasion, which was at different levels depending on their demographic attributes. Thus, the findings have a significant conclusion that taking preventive measures for the employment security of people is essential to maintaining their mental health in the future. However, it could also be said that keeping this study only in urban areas and among educated people is a limitation, though such a limitation has opened further sites for potential studies.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".