Associations among past trauma, post-displacement stressors, and mental health outcomes in Rohingya refugees in Bangladesh: A secondary cross-sectional analysis
Bibliographic record
Abstract
Objective The Rohingya endured intense trauma in Myanmar and continue to experience trauma related to displacement in Bangladesh. We aimed to evaluate the association of post-displacement stressors with mental health outcomes, adjusting for previously experienced trauma, in the Rohingya refugee population in Cox's Bazar, Bangladesh. Methods We analyzed data from the Cox's Bazar Panel Survey, a cross sectional survey consisting of 5,020 household interviews and 9,386 individual interviews completed in 2019. Using logistic regression, we tested the association between post-displacement stressors such as current exposure to crime and conflict and two mental health outcomes: depression and post-traumatic stress disorder (PTSD). In adjusted analyses, we controlled for past trauma, employment status, receiving an income, food security, and access to healthcare and stratified by gender. Results The prevalence of depressive symptoms was 30.0% (n = 1,357) and PTSD 4.9% (n = 218). Most (87.1%, n = 3,938) reported experiencing at least one traumatic event. Multiple post-displacement stressors, such as current exposure to crime and conflict (for men: OR = 2.23, 95% CI = 1.52–3.28, p < 0.001; for women: OR = 1.92, 95% CI = 1.44–2.56, p < 0.001), were associated with higher odds of depressive symptoms in multivariable models. Trauma (OR = 4.98, 95% CI = 2.20–11.31, p < 0.001) was associated with increased odds of PTSD. Living in a household that received income was associated with decreased odds of PTSD (OR = 0.74, 95% CI = 0.55–1.00, p = 0.05). Conclusion Prevalence of depressive symptoms was high among Rohingya refugees living in Cox's Bazar. Adjusting for past trauma and other risk factors, exposure to post-displacement stressors was associated with increased odds of depressive symptoms. There is a need to address social determinants of health that continue to shape mental health post-displacement and increase mental healthcare access for displaced Rohingya.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".