Factors related to COVID-19 vaccine effectiveness perception in racially diverse adults in Canada
Bibliographic record
Abstract
While disparities in COVID-19 vaccine confidence, mistrust, hesitancy, and uptake are well documented, the perception of vaccine efficacy remains understudied in Canada. This study investigates racial differences in COVID-19 vaccine efficacy perception and examines associated factors across Arab, Asian, Black, Indigenous, and White populations. A representative sample of 4220 participants (2358 women) aged 16 and older completed measures assessing perception of COVID-19 vaccine efficacy, conspiracy beliefs, health literacy, and racial discrimination in healthcare settings. Data were collected through a randomly selected online panel in October 2023. The overall mean vaccine efficacy perception score was 17.1 (SD = 4.5), with significant variation across racial groups (F(6, 4213) = 8.0, p < .001). Asian participants (M = 18.4; SD = 3.1) reported higher scores compared to Arab (M = 17.0; SD = 4.3), Black (M = 17.2; SD = 4.3), Indigenous (M = 16.4; SD = 5.2), and White (M = 17.1; SD = 4.4) participants. The most important factors associated with vaccine efficacy perception were conspiracy beliefs (β = -0.32, p < .001), health literacy (β = 0.07, p < .001), and the number of vaccine doses in White individuals. Conspiracy beliefs (β = -0.19, p < .001), higher education (β = 0.28, p < .001), health literacy (β = 0.16, p < .001), more vaccine doses (β = 1.61, p < .001), and experiences of racial discrimination in healthcare prior to accounting for conspiracy beliefs (β = -0.10, p < .05) were the most important factors for racialized individuals. This study highlights significant differences in COVID-19 vaccine efficacy perceptions across racial groups. The findings underscore the impact of factors such as conspiracy beliefs, health literacy, education level, age, and racial discrimination in healthcare on vaccine efficacy perceptions. Public health strategies should address misinformation, prioritize health literacy, and promote anti-racist practices in healthcare to improve vaccine confidence and acceptance.
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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 | 0.002 | 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) | 0.002 | 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".