Vaccine hesitancy among racially diverse parents in Canada: The important role of health literacy, conspiracy beliefs and racial discrimination
Bibliographic record
Abstract
Parental vaccine hesitancy is a global public health issue that leads to lower immunization coverage among children. While vaccine mistrust is increased among racialized adults, whether parental vaccine hesitancy differs by ethnicity in the era of COVID-19 is unknown. Addressing these gaps in the literature, this study explores the factors influencing vaccine hesitancy among a racially diverse and representative sample of Canadian parents of children aged 0 to 12, comparing perspectives across different racial groups. An online survey was administered to a nationally representative sample of Arab, Asian, Black, Indigenous, White, and Mixed-race parents from October to November 2023. Data were collected on demographics, COVID-19 vaccine hesitancy, experience of major racial discrimination, conspiracy beliefs and health literacy. A total of 2528 parents (57.52 % women, 42.29 % men, and 0.20 % identified as non-binary gender) completed the survey. Significant mean differences in vaccine hesitancy were observed among racialized groups, F(7, 2520) = 3.89, p < .001, with Arab parents (M = 23.73, SD = 7.46) reporting higher hesitancy than White parents (M = 21.28, SD = 8.59). Younger participants (14-24 years) showed greater hesitancy (M = 23.98, SD = 8.22) than those aged 55+ (M = 20.26, SD = 7.83), F(4, 2523) = 2.84, p = .023. Regression analyses indicated that conspiracy beliefs (β = 0.48, p < .001) and racial discrimination (β = 0.09, p = .012) are key predictors of vaccine hesitancy. A significant interaction between conspiracy beliefs and discrimination was found among racialized groups (β = 0.24, p < .001). Based on these results, addressing vaccine hesitancy requires nuanced, participatory approaches that foster trust, counter misinformation, and acknowledge systemic racial inequities. As, health literacy, conspiracy beliefs, and racial discrimination significantly shape parental decisions, future policies must integrate culturally and racially tailored strategies to promote vaccination, ensuring that every child in Canada is protected.
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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 | 0.005 | 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) | 0.002 | 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".