Mental Health Consequences of the July Revolution in Bangladesh: A Study on Depression and Post-traumatic Stress Disorder Among Survivors of Violence and Persecution
Bibliographic record
Abstract
Introduction This study examined the mental health consequences of Bangladesh's July Revolution (2024) by assessing the occurrence of depression and post-traumatic stress disorder (PTSD) symptoms among survivors of state violence and persecution while identifying associated risk factors. Materials and methods A hospital-based cross-sectional study was conducted from September 2024 to February 2025 among 217 injured survivors (mean age: 26.0 ± 9.7 years; 97.2% male). Participants were assessed via face-to-face interviews using validated tools: Patient Health Questionnaire-9 (PHQ-9) (cutoff: ≥10 for depression) and PTSD Checklist for DSM-5 (PCL-5) (cutoff: ≥33 for PTSD). Univariate logistic regression was performed to analyze sociodemographic and clinical associations. Results Depression and PTSD were alarmingly high: 82.5% met the threshold for depression (PHQ-9 mean: 14.7 ± 5.6) and 64.1% for PTSD (PCL-5 mean: 37.6 ± 16.1). Urban survivors had significantly lower odds of depression (OR = 0.47, 95% CI: 0.22-1.00) and PTSD (OR = 0.52, 95% CI: 0.29-0.92) than rural counterparts. Strikingly, 99.3% of PTSD cases had comorbid depression, with strong symptom correlation (r = 0.772, p < 0.001). No other sociodemographic factors (age, sex, education, occupation, and socioeconomic status) or violence type showed significant associations. Conclusions The revolution's survivors exhibited extreme mental health burdens, underscoring an urgent need for trauma-integrated care, especially in rural areas.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".