Coping with COVID-19 Pandemic: A Population-Based Study in Bangladesh
Bibliographic record
Abstract
ABSTRACT This study aims to investigate coping strategies used by Bangladeshi citizens during the COVID-19 pandemic. Design Prospective, cross-sectional survey of adults (N=2001) living in Bangladesh. Methods Participants were interviewed for socio-demographic data and completed the Bengali translated Brief-COPE Inventory. Statistical data analysis was conducted using SPSS (Version 20). Results Participants (N=2001), aged 18 to 86 years, were recruited from eight administrative divisions within Bangladesh (mean age 31.85±14.2 years). Male to female participant ratio was 53.4% (n=1074) to 46.6% (n=927). Higher scores were reported for approach coping styles (29.83±8.9), with lower scores reported for avoidant coping styles (20.83 ± 6.05). Humor coping scores were reported at 2.68±1.3 and religion coping scores at 5.64±1.8. Both men and women showed similar coping styles. Multivariate analysis found a significant relationship between male gender and both humor and avoidant coping (p <.01). Male gender was found to be inversely related to both religion and approach coping (p <.01). Marital status and education were significantly related to all coping style domains (p<.01). Occupation was significantly related to approach coping (p <.01). Rural and urban locations differed significantly in participant coping styles (p <.01). Factor analysis revealed two cluster groups (Factor 1 and 2) comprised of unique combinations from all coping style domains. Conclusion Participants in this study coped with the COVID-19 pandemic by utilizing a combination of coping strategies. Factor 1 revealed both avoidant and approach coping strategies and Factor 2 revealed a combination of humor and avoidant coping strategies. Overall, a higher utilization of approach coping strategies was reported, which has previously been associated with better physical and mental health outcomes. Religion was found to be a coping strategy for all participants. Future research may focus on understanding resilience in vulnerable populations, including people with disability or with migrant or refugee status in Bangladesh.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".