Racial disparities in the prevalence and determinants of anxiety symptoms among Arab, Asian, Black, Indigenous, White, and Mixed-racial individuals in Canada: The major role of racial discrimination
Bibliographic record
Abstract
Despite the fact that anxiety is one of the most common mental health disorders and that more than one-quarter of Canada's population (26.5 %) identifies as belonging to a racialized group, no studies have compared the prevalence and determinants of anxiety symptoms across different racial groups. This study investigated the prevalence and determinants associated with anxiety symptoms in Canada, comparing Arab, Asian, Black, Indigenous, White, and mixed-race individuals. In this cross-sectional study, 4220 participants (55.88 % women) were recruited. The prevalence of anxiety symptoms was 33.65 %. Prevalence was highest among Indigenous participants (47.58 %), followed by Arab (38.99 %), Asian (35.92 %), Black (30.12 %), White (27.74 %), and mixed-race participants (24.56 %), χ² (6) = 101.66, p < 0.001. Women were 1.47 times more likely to report anxiety symptoms than men, and non-binary individuals were at higher risk (OR = 5.48). Younger age, lower education, being born in Canada, language spoken, employment status, religion, and sexual minority status were associated with a higher prevalence of anxiety symptoms. Results also showed that participants with very high levels of everyday racial discrimination were 6.94 times more likely to experience anxiety symptoms (OR = 6.94, p < 0.001) compared to those with a low experience of racial discrimination, while resilience was negatively associated with anxiety symptoms (OR = 0.67, p < 0.001). Despite the protective role of resilience, results reveal a strong association between racial discrimination and anxiety symptoms across all racialized groups, highlighting the broad impact of racism on mental health. These findings underscore a mental health crisis in Canada and the urgent need for anti-racist mental health care for racialized and Indigenous individuals.
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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 | 0.002 | 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".