COVID‐19 Vaccine Mistrust, Racial Discrimination, and Conspiracy Beliefs Among Parents in Canada: Implications for Public Health
Bibliographic record
Abstract
Despite studies documenting the evolution and factors related to COVID-19 vaccination in Canadian children, gaps in research on COVID-19 vaccine mistrust (VM) across ethnocultural groups hinder tailored public health responses. This study examines the sociodemographic characteristics and factors related to COVID-19 VM among racially diverse Canadian parents with children aged 0-12 years from Arab, Asian, Black, Indigenous, and White communities. A cross-sectional study was conducted among a representative sample of 2528 parents of children aged 0-12 years across Canada. They completed measures assessing COVID-19 VM, conspiracy beliefs, health literacy, everyday racial discrimination and sociodemographic data. Descriptive statistics, ANOVA, and multiple regression analyses explored factors associated with VM, with subgroup analyses for racialized and Indigenous groups. Significant mean differences in VM were observed across racial groups (p < 0.001). Post hoc analyses showed that Black individuals scored higher on COVID VM (M = 12.4; 95% CI: 11.9-12.9) compared to White individuals (M = 11.1; 95% CI: 10.8-11.3; p < 0.001). Indigenous participants scored higher on COVID VM (M = 14.4; 95% CI: 13.7-15.1) compared to White (p < 0.001), Black (p < 0.001), Arab (M = 12.9; 95% CI: 12.4-13.5; p = 0.017), and Asian participants (M = 11.8; 95% CI: 11.3-12.3; p < 0.001). Regression analyses revealed that conspiracy beliefs were strongly associated with VM in both White (β = 0.61, p < 0.001, R² = 42.1%) and racialized individuals (β = 0.51, p < 0.001, R² = 34.0%). Everyday racial discrimination significantly predicted VM among racialized groups (β = 0.10, p = 0.001) but not White participants (β = -0.02, p = 0.313). Significant racial, gender, and age-based disparities in COVID-19 VM among Canadian parents underscore the urgent need for targeted, community-driven, and antiracist public health strategies to enhance vaccine confidence, addressing unique barriers faced by racialized and Indigenous communities.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 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".