Stigma associated with mental illness among Asian men in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND:: Due to racism, xenophobic nationalism, acculturation pressures and patriarchal social relations, Asian men in Western societies may be particularly susceptible to negative experiences and beliefs regarding mental illness and treatment services. AIMS:: We examine factors associated with stigma toward mental illness among Asian men in Canada. METHODS:: Between 2013 and 2017, 428 self-identified Asian men living in proximity to Vancouver, Canada, were recruited and completed self-administered questionnaires assessing social stigma and self-stigma. The degree to which these variables were associated with the men's sociodemographic characteristics was analyzed. RESULTS:: Multivariable regression revealed that social stigma was significantly predicted by age, immigration, employment status and experience with mental illness. Together, these variables accounted for 12.36% of variance in social stigma. Interaction terms were added to the regression models to examine whether the effects of immigration on social stigma varied by age and experience with mental illness, but none of the interaction terms were statistically significant. Among the 94 Asian men identified as living with mental illness, self-stigma was predicted by age, immigration and employment status, which together accounted for 14.97% of variance in self-stigma. CONCLUSION:: These results offer new knowledge about the factors predicting stigma toward mental illness among Asian men in Western societies.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 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".