Vaccine mistrust among Black individuals in Canada: The major role of health literacy, conspiracy theories, and racial discrimination in the healthcare system
Bibliographic record
Abstract
The COVID-19 pandemic disproportionately affected Black communities in Canada in terms of infection and mortality rates compared to the general population. Despite these facts, Black communities are among those with the highest level of COVID-19 vaccine mistrust (COVID-19 VM). We collected novel data to analyze the sociodemographic characteristics and factors associated with COVID-19 VM among Black communities in Canada. A survey was conducted among a representative sample of 2002 Black individuals (51.66% women) aged 14-94 years (M = 29.34; SD = 10.13) across Canada. Vaccine mistrust was assessed as the dependent variable and conspiracy theories, health literacy, major racial discrimination in healthcare settings, and sociodemographic characteristics of participants were assessed as independent variables. Those with a history of COVID-19 infection had higher COVID-19 VM score (M = 11.92, SD = 3.88) compared to those with no history of infection (M = 11.25, SD = 3.83), t (1999) = -3.85, p < 0.001. Participants who reported having experienced major racial discrimination in healthcare settings were more likely to report COVID-19 VM (M = 11.92, SD = 4.03) than those who were not (M = 11.36, SD = 3.77), t (1999) = -3.05, p = 0.002. Results also showed significant differences for age, education level, income, marital status, provinces, language, employment status, and religion. The final hierarchical linear regression showed that conspiracy beliefs (B = 0.69, p < 0.001) were positively associated with COVID-19 VM, while health literacy (B = -0.05, p = 0.002) was negatively associated with it. The mediated moderation model showed that conspiracy theories completely mediated the association between racial discrimination and vaccine mistrust (B = 1.71, p < 0.001). This association was also completely moderated by the interaction between racial discrimination and health literacy (B = 0.42, p = 0.008), indicating that despite having a high level of health literacy, those who experienced major racial discrimination in health services developed vaccine mistrust. This first study on COVID-19 VM exclusively among Black individuals in Canada provides data that can significantly impact the development of tools, trainings, strategies, and programs to make the health systems free of racism and increase their confidence in vaccination for COVID-19 and other infectious diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".