COVID‐19 Vaccine Uptake Rates and Associated Factors in Racially Diverse Parents in Canada: The Threat From Conspiracy Beliefs and Racial Discrimination
Bibliographic record
Abstract
ABSTRACT Although vaccination is a well‐recognized illness prevention strategy in medicine, vaccine uptake remains inconsistent in racialized and Indigenous communities in North America. Given that vaccination decisions in children are largely mediated by their parents' beliefs and values, this study aimed to examine vaccination rates and associated factors among parents of children aged 0–12 years from various racial groups in Canada. A cross‐sectional study was conducted in 2023, involving a nationally representative sample of 2528 parents of children aged 0–12 years across Canada (57.52% women). Vaccine uptake prevalence rate was 86.39% among the participants. We observed significant differences in vaccination prevalence for racialized parents, χ2 (6) = 32.65, p < 0.001. Overall, Asian parents had the highest vaccine uptake (95.49%), compared to Arab (89.29%), White (85.59%), Other (84.33%), Black (84.33%) and Indigenous (83.13%) parents. Lowest COVID‐19 vaccination rates were found for mixed racial group parents (77.01%). Conspiracy beliefs (β = −0.19, p < 0.001) and everyday experiences of racial discrimination (White vs. Racialized groups, β = −0.09, p = 0.037) were negatively associated with vaccine uptake. This study offers a novel regard on parent beliefs and behaviors regarding vaccination, disaggregated by various racial groups. While vaccination remains a cornerstone of public health, our findings reveal disparities that extend beyond access, reflecting deeper societal influences. Variations in uptake across racial groups, shaped by trust and lived experiences, underscore the need for inclusive strategies that foster confidence and engagement in immunization efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".