Self-Reported Health of Working-Age Refugees, Immigrants, and the Canadian-Born
Bibliographic record
Abstract
Canada has a rapidly growing refugee population, yet, there are limited research studies on the physical health of working-age refugees in comparison to the health of immigrants and Canadian-born individuals. Investigating social capital and acculturation measures may provide important insights into the factors associated with good self-reported health and this may help to inform health promotion strategies for refugees in Canada. A secondary analysis was conducted on data collected from the Canadian General Social Survey 27 (GSS-27) comparing a sample of refugees (n = 753), immigrants (n = 5,063), and Canadian-born (n = 11,266) respondents between the ages of 15 and 64. Both bivariate and logistic regression analyses were conducted. Self-reported physical health, dichotomized into poor versus good, was the outcome of interest. The self-reported physical health status of refugees, immigrants, and Canadian-born respondents was comparable. Visible minority status was not significantly associated with self-reported health status. Among refugees, the likelihood of reporting good health was associated with being a woman, being married/common-law, being involved in a social group/organization, and having more than half of one’s friends who spoke a different mother tongue than the respondent. Refugees, however, were less likely to have a confidant and be involved in social groups/organizations as compared to immigrants or those born in Canada. The odds of reporting good health were significantly lower among those who had experienced discrimination within the last five years. Social capital and acculturation may be protective of the self-reported health of refugees in Canada. Initiatives to support refugees’ social connections are therefore warranted.
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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 | 0.001 |
| 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) | 0.002 | 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".