‘Hiding their troubles’: a qualitative exploration of suicide in Bhutanese refugees in the USA
Bibliographic record
Abstract
BACKGROUND: Suicide is a major global health concern. Bhutanese refugees resettled in the USA are disproportionately affected by suicide, yet little research has been conducted to identify factors contributing to this vulnerability. This study aims to investigate the issue of suicide of Bhutanese refugee communities via an in-depth qualitative, social-ecological approach. METHODS: Focus groups were conducted with 83 Bhutanese refugees (adults and children), to explore the perceived causes, and risk and protective factors for suicide, at individual, family, community, and societal levels. Audio recordings were translated and transcribed, and inductive thematic analysis conducted. RESULTS: Themes identified can be situated across all levels of the social-ecological model. Individual thoughts, feelings, and behaviors are only fully understood when considering past experiences, and stressors at other levels of an individual's social ecology. Shifting dynamics and conflict within the family are pervasive and challenging. Within the community, there is a high prevalence of suicide, yet major barriers to communicating with others about distress and suicidality. At the societal level, difficulties relating to acculturation, citizenship, employment and finances, language, and literacy are influential. Two themes cut across several levels of the ecosystem: loss; and isolation, exclusion, and loneliness. CONCLUSIONS: This study extends on existing research and highlights the necessity for future intervention models of suicide to move beyond an individual focus, and consider factors at all levels of refugees' social-ecology. Simply focusing treatment at the individual level is not sufficient. Researchers and practitioners should strive for community-driven, culturally relevant, socio-ecological approaches for prevention and treatment.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".