Racial disparities in COVID-19 vaccination in Canada: results from the cross-sectional Canadian Community Health Survey
Bibliographic record
Abstract
BACKGROUND: Racial and ethnic disparities in COVID-19 vaccination coverage have been observed in Canada and in other countries. We aimed to compare vaccination coverage for at least 1 dose of a COVID-19 vaccine between First Nations people living off reserve and Métis, Black, Arab, Chinese, South Asian and White people. METHODS: We used data collected between June 2021 and June 2022 by Statistics Canada's Canadian Community Health Survey, a large, nationally representative cross-sectional study. The analysis included 64 722 participants aged 18 years or older from the 10 provinces. We used a multiple logistic regression model to determine associations between vaccination status and race, controlling for collection period, region of residence, age, gender and education. RESULTS: Nonvaccination against COVID-19 was more frequent in off-reserve First Nations people (adjusted odds ratio [OR] 1.8, 95% confidence interval [CI] 1.2-2.7) and Black people (adjusted OR 1.7, 95% CI 1.1-2.6), and less frequent among South Asian people (adjusted OR 0.3, 95% CI 0.1-0.7) compared to White people. INTERPRETATION: This analysis showed significant inequalities in COVID-19 vaccine uptake between racial/ethnic populations in Canada. Further research is needed to understand the sociocultural, structural and systemic facilitators of and barriers to vaccination across racial groups, and to identify strategies that may improve vaccination uptake among First Nations and Black people.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| 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".