Vaccination, Compliance with Preventive Measures and Mental Health during COVID-19 among Adults in Bangladesh: Do Vaccination and Compliance with Preventive Measures Improve Mental Health?
Bibliographic record
Abstract
Background and Objective: In addition to the reduction of risk of COVID-19 transmission and mortality, vaccination and compliance with the preventive measures of COVID-19 may have important additional benefits for the improvement of mental health. This study examined the levels and determinants of vaccination, compliance with preventive measures, and anxiety, depression and stress among Bangladeshi adults. It also examined the effects of vaccination and compliance with preventive measures on mental health status among Bangladeshi adults. Methods: Data for the study come from an online survey conducted during November and December 2021 among Bangladeshi adults. A total of 615 adults participated in the survey. A 21-item Depression, Anxiety, and Stress scale (DASS 21) were used for collecting data on mental health. Both descriptive and inferential statistical methods including multiple logistic regression were used for data analysis. Results: About 69% of Bangladeshi adults were vaccinated with at least one dose; 87% of adults believed that vaccines are moderate to highly effective in reducing COVID-19 infection. Among the preventive measures during a pandemic, the level of complete compliance with wearing masks and hand washing was almost universal, while the compliance level with other preventive measures was moderate. Vaccinated people had a 55% (OR=0.45; 95%CI:0.26-0.82), 67% (OR=0.33; 95%CI:0.12-0.91), and 44% (OR=0.56; 95%CI:0.27-0.97) lower chance of suffering from depression, anxiety, and stress, respectively than non-vaccinated people. People with complete compliance had 64% (OR= 0.36; 95%CI:0.18-0.72), 71% (OR=0.29; 95%CI:0.15-0.58), and 74% (OR=0.26; 95%CI:0.13-0.50) lower risk of suffering from depression, anxiety, and stress, respectively, than respondents with irregular preventive behaviors. Conclusion: This study documents the important psychological benefits of vaccination and compliance with preventive measures of COVID-19.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".