Racial disparities in the rates of COVID-19 vaccine uptake among children from Arab, Asian, Black, Indigenous, White and Mixed racial families in Canada
Bibliographic record
Abstract
Pediatric COVID-19 vaccination reduces symptomatic infections and hospitalizations. However, race-based disparities in pediatric COVID-19 vaccine uptake in Canada remain underexplored, despite well-documented inequities among adults. This study examines racial disparities in pediatric COVID-19 vaccine coverage in Canada and identifies key predictors, including structural and individual-level factors. A cross-sectional survey was conducted among a nationally representative and ethnically diverse sample of 2528 parents of 4386 children aged 0-12 years in Canada. Parents completed measures assessing child's COVID-19 vaccine uptake, COVID-19 vaccine mistrust, health literacy, experiences of racial discrimination, and child's prior COVID-19 infection. Adjusted odd ratios was performed to identify factors associated with vaccine uptake. Overall pediatric vaccine coverage was 56.8 %. Coverage was lowest among children of Black (39.4 %, 95 % CI: 35.4, 43.5) and Arab (42.9 %, 95 % CI: 38.4, 47.5) parents, compared to White (59.5 %, 95 % CI: 57.4, 61.5) and Asian (71.6 %, 95 % CI: 67.3, 75.6) parents (p < .001). Logistic regression with Generalized Estimating Equations (GEE) analyses, child's age, prior COVID-19 infection, and parental vaccine mistrust were determinants of vaccine coverage. Among racialized parents, experiences of major racial discrimination and lower health literacy were additional predictors of lower vaccine uptake. We observed racial disparities in pediatric COVID-19 vaccination, with significantly lower coverage among Black and Arab communities. Beyond individual and family level factors, structural experiences such as racial discrimination and limited health literacy contribute to inequities in vaccine uptake. Public health interventions must be culturally sensitive and equity-driven, addressing mistrust and systemic barriers to improve vaccine coverage and pandemic resilience in diverse populations.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".