Prevalence and correlates of depression among Black individuals in Canada: The major role of everyday racial discrimination
Bibliographic record
Abstract
BACKGROUND: Depression is a common mental health problem causing significant disability globally, including in Canada. Prevalence estimates for depression within Black communities in Canada are unknown. This study determined the prevalence of depression in a sample of Black Canadians and the association between everyday racial discrimination experiences and depression. METHODS: We analyzed data collected from the Black Community Mental Health project in Canada. Participants provided sociodemographic information and completed measures assessing depressive symptomology, everyday racial discrimination, and social support. The prevalence of depressive symptomatology was computed across sociodemographic variables and categories of everyday racial discrimination. Different regression models were conducted to examine the relationship between depressive symptoms and everyday racism controlling for sociodemographic factors. RESULTS: In total, 65.87% of participants reported severe depressive symptoms, with higher rates among women, those who are employed, and those born in Canada. The linear regression models showed that everyday racial discrimination is the best predictor of depressive symptoms, with a final model explaining 25% of the variance. A logistic regression model demonstrated that those experiencing a high level of racial discrimination are 36.4 more likely to present severe depressive symptoms when compared to those reporting a low level of discrimination. CONCLUSIONS: Rates of depressive symptoms among Black individuals are nearly six times the 12-month prevalence reported for the general population in Canada. Racial discrimination, which significantly predicts greater depressive symptomatology, is consistent with earlier studies in the United States and suggests that Canadian colorblind policies may inadvertently reinforce racial discrimination with detrimental effects on mental health.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".