Prevalence of mental disorders and torture among Tibetan refugees: A systematic review
Bibliographic record
Abstract
BACKGROUND: Many Tibetan refugees flee Tibet in order to escape physical and mental hardships, and to access the freedoms to practice their culture and religion. We aimed to determine the prevalence of mental illnesses within the refugee population and determine the prevalence of previous torture reported within this population. METHODS: We performed a systematic literature search of 10 electronic databases from inception to May 2005. In addition, we searched the internet, contacted all authors of located studies, and contacted the Tibetan Government-in-exile, to locate unpublished studies. We included any study reporting on prevalence of mental illness within the Tibetan refugee populations. We determined study quality according to validation, translation, and interview administration. We calculated proportions with exact confidence intervals. RESULTS: Five studies that met our inclusion criteria (total n = 410). All studies were conducted in North India and 4 were specifically in adult populations. Four studies provided details on the prevalence of torture and previous imprisonment within the populations. The prevalence of post-traumatic stress disorder ranged from 11-23%, anxiety ranged from 25-77%, and major depression ranged from 11.5-57%. CONCLUSION: Our review indicates that the prevalence of serious mental health disorders within this population is elevated. The reported incidence of torture and imprisonment is a possible contributor to the illnesses. Non-government organizations and international communities should be aware of the human rights abuses being levied upon this vulnerable population and the mental health outcomes that may be associated with it.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".