COVID‐19 vaccine mistrust, health literacy, conspiracy theories, and racial discrimination among a representative ethnically diverse sample in Canada: The vulnerability of Arab, Asian, Black, and Indigenous peoples
Bibliographic record
Abstract
Despite increased risk of severe acute respiratory syndrome coronavirus 2 infections and higher rates of COVID-19-related complications, racialized and Indigenous communities in Canada have lower immunization uptake compared to White individuals. However, there is woeful lack of data on predictors of COVID-19 vaccine mistrust (VM) that accounts for diverse social and cultural contexts within specific racialized and Indigenous communities. Therefore, we sought to characterize COVID-19 VM among Arab, Asian, Black, and Indigenous communities in Canada. An online survey was administered to a nationally representative, ethnically diverse panel of participants in October 2023. Arabic, Asian, Indigenous, and Black respondents were enriched in the sampling panel. Data were collected on demographics, COVID-19 VM, experience of racial discrimination, health literacy, and conspiracy beliefs. We used descriptive and regression analyses to determine the extent and predictors of COVID-19 VM among racialized and Indigenous individuals. All racialized respondents had higher VM score compared to White participants. Among 4220 respondents, we observed highest VM among Black individuals (12.18; ±4.24), followed by Arabic (12.12; ±4.60), Indigenous (11.84; ±5.18), Asian (10.61; ±4.28), and White (9.58; ±5.00) participants. In the hierarchical linear regression analyses, Black participants, women, everyday racial discrimination, and major experience of discrimination were positively associated with COVID-19 VM. Effects of racial discrimination were mediated by addition of conspiracy beliefs to the model. Racialized and Indigenous communities experience varying levels of COVID-19 VM and carry specific predictors and mediators to development of VM. This underscores the intricate interaction between race, gender, discrimination, and VM that need to be considered in future vaccination campaigns.
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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 | 0.004 | 0.010 |
| 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 | 0.001 |
| 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".