Prevalence and Effects of Daily and Major Experiences of Racial Discrimination and Microaggressions among Black Individuals in Canada
Bibliographic record
Abstract
The prevalence and correlates of different forms of racial discrimination among Black Canadians are unknown. This article aims to examine the prevalence of different forms of racial discrimination (daily, major and microaggressions) and their association with self-esteem and satisfaction with life among Black Canadians. A convenience sample of 845 Black Canadians aged 15–40 was recruited. We assessed frequencies of everyday and major racial discrimination, and racial microaggressions against Black Canadians and their association with self-esteem and satisfaction with life, controlling for gender, age, job status, education, and matrimonial status. At least 4 out of 10 participants declared having being victims of everyday racial discrimination at least once per week. Between 46.3% and 64.2% of participants declared having been victims of major racial discrimination in various situations including education, job hiring, job dismissal, health services, housing, bank and loans, and police encounters. Significant gender differences were observed for everyday and major racial discrimination with higher frequencies among female participants. A total of 50.2% to 93.8% of participants declared having been victims of at least one episode of racial microaggressions. Results showed a significant negative association between racial discrimination and satisfaction with life ( b = –0.26, p = .003), and self-esteem ( b = –0.23, p = .009). This study highlights the need to stop colorblind policies in different sectors in Canada, and for a public commitment to combat racism at the municipal, provincial and federal levels. Implications are discussed for prevention, research and public 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 | 0.002 |
| 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".