COVID‐19 vaccine uptake, conspiracy theories, and health literacy among Black individuals in Canada: Racial discrimination, confidence in health, and COVID‐19 stress as mediators
Bibliographic record
Abstract
Factors influencing vaccine uptake in Black individuals remain insufficiently documented. Understanding the role of COVID-19 related stress, conspiracy theories, health literacy, racial discrimination experiences, and confidence in health authorities can inform programs to increase vaccination coverage. We sought to analyze these factors and vaccine uptake among Black individuals in Canada. A representative sample of 2002 Black individuals from Ontario, Quebec, Alberta, Nova Scotia, New Brunswick, British Columbia, and Manitoba, aged 14 years or older completed questionnaires assessing vaccine uptake, health literacy, conspiracy theories, racial discrimination experiences, COVID-19-related stress, and confidence in health authorities. Mediation analyses were conducted to assess (1) the effect of health literacy on COVID-19 vaccination uptake through confidence and need, COVID-19 related traumatic stress, and racial discrimination, and (2) the effect of conspiracy beliefs on COVID-19 vaccination uptake through the same factors. Overall, 69.57% (95% confidence interval, 67.55%-71.59%) of the participants were vaccinated and 83.48% of them received two or more doses. Those aged 55 years and older were less likely to be vaccinated, as well as those residing in British Columbia and Manitoba. Mediation models showed that the association between health literacy and COVID-19 vaccine uptake was mediated by confidence in health authorities (B = 0.02, p < 0.001), COVID-19-related stress (B = -0.02, p < 0.001), and racial discrimination (B = -0.01, p = 0.032), but both direct and total effects were nonsignificant. Lastly, conspiracy beliefs were found to have a partial mediation effect through the same mediators (B = 0.02, p < 0.001, B = -0.02, p < 0.001, B = -0.01, p = 0.011, respectively). These findings highlight the need for targeted interventions to address vaccine hesitancy and inform approaches to improve access to vaccinations among Black 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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".