COVID-19 vaccine confidence among parents of racially diverse children aged 0–12 years old in Canada: The role of major experience of racial discrimination, health literacy, and conspiracy beliefs
Bibliographic record
Abstract
The success of COVID-19 vaccination is linked to trust, hesitancy, and confidence. Racial discrimination impacts vaccine hesitancy and trust, particularly in racialized groups. This study investigates factors influencing COVID-19 vaccine confidence among Canadian parents from different racial groups, addressing a gap in existing research. Data were collected in 2023 (October to November) included 2,528 parents of children aged 0–12. Findings showed significant mean differences between racial groups, F (7, 2520) = 9.92, p < .001, with Arabs presented lower means of confidence (M = 30.26, SD = 9.39) compared to Asian (M = 35.71, SD = 8.14), Black (M = 33.23, SD = 9.50), and Indigenous parents (M = 35.07, SD = 9.45). Multiple linear regression among White participants showed that conspiracy beliefs were negatively associated with COVID-19 vaccine confidence (β = -.60, p < .001), whereas health literacy was positively associated with COVID-19 vaccine confidence (β = .09, p < .001). Results among racialized groups showed that conspiracy beliefs (β = -.23, p < .001) and racial discrimination (β = -.05, p = .049) were negatively associated with COVID-19 vaccine confidence, while health literacy was positively associated with COVID-19 vaccine confidence (β = .31, p < .001). This study highlights the complex factors influencing COVID-19 vaccine confidence among Canadian parents from racialized groups, suggesting that racial discrimination and conspiracy beliefs significantly reduce vaccine confidence, while health literacy plays a crucial role in increasing confidence. These results underscore the importance of addressing misinformation and systemic barriers to trust in vaccination efforts.
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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 | 0.001 |
| Science and technology studies | 0.002 | 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".