Racial disparities related to the perception of COVID-19 vaccine effectiveness among parents of children aged 0–12 years old in Canada
Bibliographic record
Abstract
Vaccine hesitancy, particularly among racialized communities, persists due to misinformation, medical mistrust, and systemic barriers. This study examines racial disparities and key determinants related to perceptions of COVID-19 vaccine effectiveness in Canadian parents from Arab, Asian, Black, Indigenous, White and mixed-race communities. A cross-sectional survey was conducted among a representative sample of 2,528 Canadian parents of children aged 0–12 years (57.5% women). Participants completed a survey assessing conspiracy beliefs, health literacy, major experiences of racial discrimination, and perceptions of COVID-19 vaccine effectiveness. The mean COVID-19 vaccine effectiveness perception score was 19.57 (SD = 5.32). ANOVA showed significant differences by race (F = 5.15, p < .001), with Asian (M = 21.41, SD = 4.12) and Indigenous parents (M = 21.42, SD = 4.85) reporting higher scores than White, Black, and Arab ones. Regression analyses indicated that conspiracy beliefs negatively predicted vaccine effectiveness perception (β = −0.05, p < .001), while health literacy had a positive association (β = .19, p < .001). Major racial discrimination was negatively associated but became non-significant after adjusting for conspiracy beliefs. Vaccine effectiveness perception varies across racial groups. Higher health literacy and prior vaccination enhance perceptions, while conspiracy beliefs undermine them. Addressing misinformation and tailoring public health strategies to diverse parental experiences – particularly among younger parents and marginalized groups – will strengthen vaccine confidence.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".