Racial discrimination in healthcare services among Black individuals in Canada as a major threat for public health: its association with COVID-19 vaccine mistrust and uptake, conspiracy beliefs, depression, anxiety, stress, and community resilience
Bibliographic record
Abstract
OBJECTIVES: To examine the prevalence of major racial discrimination (MRD) in healthcare services and its association with COVID-19 vaccine mistrust and uptake, conspiracy theories, COVID-19-related stressors, community resilience, anxiety, depression, and stress symptoms. STUDY DESIGN: The study used a population-based cross-sectional design. METHODS: Data from the BlackVax dataset on COVID-19 vaccination in Black individuals in Canada was analyzed (n = 2002, 51.66% women). Logistic regression analyses were performed to examine the association between MRD and independent variables. RESULTS: 32.55% of participants declared having experienced MRD in healthcare services. Participants with MRD were less vaccinated against COVID-19, presented higher scores of vaccine mistrust, conspiracy beliefs, COVID-19 related stressors, depression, anxiety, and stress, and had lower scores of community resilience. They were more likely to experience depression (AOR = 2.13, P < 0.001), anxiety (AOR = 2.00, P < 0.001), and stress symptoms (AOR = 2.15, P < 0.001). Participants who experienced MRD were more likely to be unvaccinated (AOR = 1.35, P = 0.009). CONCLUSIONS: Racial discrimination experienced by Black individuals in health services is a major public health concern and threat to population health in Canada. Federal, provincial, and municipal public health agencies should adapt their programs, strategies, tools, and campaigns to address the mistrust created by racial discrimination.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".