Population mental health in Burma after 2021 military coup: online non-probability survey
Bibliographic record
Abstract
BACKGROUND: Humanitarian crises and armed conflicts lead to a greater prevalence of poor population mental health. Following the 1 February 2021 military coup in Burma, the country's civilians have faced humanitarian crises that have probably caused rising rates of mental disorders. However, a dearth of data has prevented researchers from assessing the extent of the problem empirically. AIMS: To better understand prevalence of depressive and anxiety disorders among the Burmese adult population after the February 2021 military coup. METHOD: We fielded an online non-probability survey of 7720 Burmese adults aged 18 and older during October 2021 and asked mental health and demographic questions. We used the Patient Health Questionnaire-4 to measure probable depression and anxiety in respondents. We also estimated logistic regressions to assess variations in probable depression and anxiety across demographic subgroups and by level of trust in various media sources, including those operated by the Burmese military establishment. RESULTS: We found consistently high rates of probable anxiety and depression combined (60.71%), probable depression (61%) and probable anxiety (58%) in the sample overall, as well as across demographic subgroups. Respondents who 'mostly' or 'completely' trusted military-affiliated media sources (about 3% of the sample) were significantly less likely than respondents who did not trust these sources to report symptoms of anxiety and depression (AOR = 0.574; 95% CI 0.370-0.889), depression (AOR = 0.590; 95% CI 0.383-0.908) or anxiety (AOR = 0.609; 95% CI 0.390-0.951). CONCLUSIONS: The widespread symptoms of anxiety and depression we observed demonstrate the need for both continuous surveillance of the current situation and humanitarian interventions to address mental health needs in Burma.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".