Experiences of racial microaggression among immigrant and Canadian-born young adults: Effects of double stigma on mental health and service use
Bibliographic record
Abstract
BACKGROUND: Significant disparities in utilization of mental health services exist among immigrant and Canadian-born populations. These gaps may be associated with a 'double stigma' - stigma related to being from a racialized background exacerbated by mental health stigma. Immigrant young adults may be particularly susceptible to this phenomenon, given developmental and social transitions from adolescence to adulthood. AIMS: To investigate the joint effects of racial microaggression and mental health stigma on mental health and service use among first-generation immigrant and Canadian-born university students. METHOD: = 1.50). RESULTS: Despite no differences in anxiety or depression symptoms, first-generation (foreign-born) immigrants were less likely to have received therapy and to have taken medication for mental health issues compared to Canadian-born participants. First-generation immigrants also reported experiencing higher levels of racial microaggression and stigma toward service use. Results suggest the presence of a double stigma, mental health stigma and racial microaggression, each explained significant additional variance in symptoms of anxiety and depression and medication use. No effects of double stigma for therapy use were found - while higher mental health stigma predicted lower use of therapy, racial microaggression did not predict unique variance in therapy use. CONCLUSIONS: Our findings highlight the joint effects of racial microaggression and stigma toward mental health and service as barriers to help-seeking among immigrant young adults. Mental health intervention and outreach programmes should target overt and covert forms of racial discrimination while incorporating culturally sensitive anti-stigma approaches to help reduce disparities in mental health service use among immigrants in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".