A qualitative study of perceptions of risk and protective factors for suicide among Bhutanese refugees.
Bibliographic record
Abstract
century. The Lhotshampa people were forced to flee their homes in southern Bhutan and enter refugee camps in Nepal for over 20 years. As of this writing, most Bhutanese refugees have been resettled in other countries (primarily the United States, Canada, and Australia). As the two remaining Nepalese refugee camps prepare to close, a growing suicide crisis is developing among many Bhutanese refugees. Bhutanese refugees resettled in the United States are dying by suicide at approximately twice the rate of the general U.S. population. It is crucial to examine, qualitatively, the nature of both risk and protective factors from the perspective of Bhutanese refugees, themselves. Our study included 15 Bhutanese refugees (8 men, 7 women) recruited from a community sample as part of a parent project examining culturally responsive suicide risk assessment. Mean age across both genders was 38.4 years (range of 22-55 years). Participants in our study were asked open-ended questions about suicide risk and prevention. We conducted a thematic analysis, synthesized risk and protective themes, and applied a socio-ecological framework to the data. We found risk themes included psychological distress and vulnerability, substance use, social and familial discord, interpersonal violence, isolation, and postmigration stressors. Protective themes included low levels of substance use, de-stigmatization of mental health concerns, strong social connections, reduced postmigration stressors, increased access to mental health care, and strong awareness within the host community of migration-related challenges.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".