“Solving Tension”: coping among Bhutanese refugees in Nepal
Bibliographic record
Abstract
Purpose The Bhutanese refugee camps of eastern Nepal are home to a mass resettlement operation; over half the population has been relocated within the past five years. While recent research suggests Bhutanese refugees are experiencing degradation of social networks and rising suicide rates, little is known about ethnocultural pathways to coping and resilience in this population. Design/methodology/approach A common coping measure (Brief COPE) was adapted to the linguistic and cultural context of the refugee camps and administered to a representative sample of 193 Bhutanese refugees as part of a broader ten‐month ethnographic study of resilience. Findings Active coping, planning, and positive reframing were the most frequently utilized strategies, followed by acceptance, religion, and seeking emotional support. Exploratory factor analysis resulted in five factors: humor, denial, behavioral disengagement; positive reframing, planning, active coping; emotional support, instrumental support; interpersonal (a new sub‐scale), acceptance, self‐blame; and venting, religion. Research implications Data support the relevance of some dimensions of coping while revealing particularities of this population. Practical implications Findings can inform future research and intervention efforts aimed at reducing suicide and promoting mental health across the Bhutanese refugee diaspora. Originality/value This is the first mixed‐methods study of coping in the Bhutanese refugee camp population since the start of a mass resettlement exercise. Qualitative data and ethnography were used to illuminate measured trends in local coping behavior.
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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 | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".