Discrimination and Racial Inequities in Self-reported Mental Health Among Immigrants and Canadian-Born Individuals in a Large, Nationally Representative Canadian Survey
Bibliographic record
Abstract
We examined the link between discrimination and self-rated mental health (SRMH) among immigrants and Canadian-born individuals, stratified according to an individual's identification as racialized or white. Using data from Canada's General Social Survey (2014) (weighted N = 27,575,000) with a novel oversample of immigrants, we estimated the association of perceived discrimination with SRMH separately among immigrants and Canadian-born individuals and stratified by racialized status. Among immigrants, we also investigated whether age-at-arrival attenuated or strengthened associations. The prevalence of discrimination was higher among racialized compared to white immigrants (18.9% versus 11.8%), and among racialized compared to white non-immigrants (20.0% versus 10.5%). In the adjusted model with immigrants, where white immigrants not reporting discrimination were the referent group, both white (adjusted prevalence odds ratio [aPOR] 6.11, 95% confidence interval [CI] 3.08, 12.12) and racialized immigrants (aPOR 2.28, 95% CI 1.29, 4.04) who experienced discrimination reported poorer SRMH. The associations were weaker among immigrants who immigrated in adulthood. In the adjusted model with non-immigrants, compared to unexposed white respondents, Canadian-born white respondents who experienced discrimination reported poorer SRMH (aPOR 3.62, 95% CI 2.99, 4.40) while no statistically significant association was detected among racialized respondents (aPOR 2.24, 95% CI 0.90, 5.58). Racialized respondents experienced significant levels of discrimination compared to white respondents irrespective of immigrant status. Discrimination was associated with poor SRMH among all immigrants, with some evidence of a stronger association for white immigrants and immigrants who migrated at a younger age. For Canadian-born individuals, discrimination was associated with poor SRMH among white respondents only.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".