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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 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) | 0.001 | 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".