Psychological benefits of the COVID‐19 vaccination: A Bangladeshi comparative study
Bibliographic record
Abstract
Background and Aims: Despite evidence that COVID-19 vaccination can strengthen mental health, there is limited evidence about this in Bangladesh. Thus, this comparative study assessed the prevalence and factors associated with mental health problems between vaccine receivers and nonreceivers. Methods: Using a snowball sampling technique, a web-based cross-sectional study was conducted among a total of 459 participants. The survey questionnaire included sociodemographic information, the Patient Health Questionnaire (PHQ-9), the Generalized Anxiety Disorder (GAD-7), and the Trauma Screening Questionnaire (TSQ-10). Results: The study found that mental health problems were nonsignificantly prevalent in the vaccine nonreceivers than those who received it (i.e., 24.79% vs. 20.60% for depression, 21.20% vs. 16.60% for anxiety, and 15.30% vs. 12.60% for posttraumatic stress disorder). Female gender, chronic condition, smoking status, and alcohol consumption were the risk factors for mental health problems. Conclusion: This study's findings suggest that the COVID-19 vaccination necessarily improves mental health outcomes. However, the study had limitations in terms of its design and sampling technique, and further research is needed to establish a cause-effect relationship between vaccination and mental health problems.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".