Predictors of positive mental health among refugees: Results from Canada’s General Social Survey
Bibliographic record
Abstract
Do refugees have lower levels of positive mental health than other migrants? If so, to what extent is this attributable to post-migration experiences, including discrimination? How does gender affect the relationships between post-migration experience and positive mental health? To address these questions, the current study uses data from Statistics Canada's 2013 General Social Survey (GSS), a nationally representative household study that included 27,695 Canadians 15 years of age and older. The study compares self-reported positive mental health among 651 refugees, 309 economic immigrants, and 448 family class immigrants from 50 source countries. Immigration-related predictors of mental health were examined including sociodemographic characteristics, discrimination, acculturation variables, and experiences of reception. Separate analyses were carried out for women and men. Refugees had lower levels of positive mental health than other migrants. Affiliative feelings towards the source country jeopardized refugee, but not immigrant mental health. A sense of belonging to Canada was a significant predictor of mental health. Perceived discrimination explained refugee mental health disadvantage among men, but not women. Bridging social networks were a mental health asset, particularly for women. The implications of anti-refugee discrimination net of the effects of anti-immigrant and anti-visible minority antipathies are discussed, as well as possible reasons for gender differences in the salience of mental health predictors.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".