Resettled Bhutanese Refugees in Ottawa: What Coping Strategies Promote Psychological Well-Being?
Bibliographic record
Abstract
Background Since 2008, Bhutanese refugees have been resettled in Canada, including Ottawa. This relocation and resettling process is associated with significant physical and psychological stress, as individuals acclimatize to a new country. Purpose To assess the relationship between coping strategies and psychological well-being of Bhutanese refugees resettled in Ottawa. Methods A cross-sectional survey utilizing a convenience sample of adults (n = 110) was conducted in the fall of 2015 in Ottawa. Two tools, Brief COPE and general well-being schedule were used. Results Bhutanese refugees were in moderate distress. Using multiple linear regression, age, education, and three coping strategies (positive reframing, self-blame, and venting) were identified as predictors of general well-being ( F (11, 96) = 3.61, p < .001, R 2 = 21.2%). Higher levels of education and positive reframing were associated with greater general well-being scores, while self-blame and well-being between ages 41 and 50 years were inversely associated with general well-being. Conclusions Findings suggest that a broad intersectorial approach between nurses and partner agencies is needed to enhance the mental health of this population for better adjustment in the host country. Nurses could provide support and counseling to minimize the use of self-blame and venting and promote positive coping strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".