Bridging the gap: A mixed-methods analysis of Canadian and U.S. immunization programs for enhancing racial equity in childhood vaccinations
Bibliographic record
Abstract
OBJECTIVES: Building on a recent U.S.-based study, we compare racial disparities in childhood immunization between Canada and the United States over the past decade to identify resilient, cross-context interventions that promote racial equity and strengthen public health practices. DESIGN: Using a comparative mixed methods approach adapted from our U.S. study, we analyze the Canadian context to explore key similarities and differences. Methods included a review of vaccine coverage data, ecological analysis of racial demographics and immunization rates, and key informant interviews. SETTING: The study focused on Canada's three most populous provinces: British Columbia, Ontario, and Quebec. PARTICIPANTS: Quantitative data drew from the Canadian Census, national immunization surveys, and provincial surveys and registries. Qualitative analysis involved 33 interviews with provincial and national informants across various sectors. MAIN OUTCOME MEASURE: Routine childhood vaccine coverage, racial equity, and barriers and interventions to improve rates among Black, Indigenous, and newcomer communities. RESULTS: Quantitative analysis revealed challenges in monitoring racial disparities due to limited disaggregated race and ethnicity data. Despite this, childhood vaccination rates were lower than the U.S., with Black and Indigenous children showing the lowest coverage. Qualitative interviews identified individual and social-environmental barriers to vaccine equity and confidence, including limited healthcare access exacerbated by social determinants of health, distrust, and fragmented healthcare systems. Effective interventions focused on building trust, reducing barriers, engaging communities, and strengthening data systems. Comparisons with the U.S. underscored the limitations of decentralized healthcare models and highlighted the need for stronger regional and multisectoral collaboration, enhanced data collection, and culturally relevant interventions to improve vaccine confidence and accessibility. CONCLUSIONS: Racial inequities in childhood vaccination coverage persist in both Canada and the U.S., despite efforts to reduce cost barriers. Addressing these disparities requires strategies that engage communities, foster agency, and address both systemic and individual barriers to vaccination.
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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 | 0.037 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".