COVID-19 Vaccine Hesitancy: A Cross-Sectional Study of Visible Minority Canadian Communities
Bibliographic record
Abstract
The World Health Organization (WHO) defines vaccine hesitancy as reluctance or refusal to vaccinate despite availability. Contributing factors in visible minority populations include vaccine safety, effectiveness, mistrust, socioeconomic characteristics, vaccine development, information circulation, knowledge, perceived risk of COVID-19, and perceived benefit. Objectives: This study aimed to examine vaccine hesitancy in visible minority populations across Canadian regions. Methods: A survey was conducted among visible minority populations in Canadian regions, using 21 questions from the available literature via the Delphi method. The Canadian Hub for Applied and Social Research (CHASR) administered the survey to individuals 18 years or older who resided in Canada at the time of the survey and identified as visible minorities such as Asian, Black, and Latin American. After recruiting 511 participants, data analysis used Chi-square tests of association and 95% confidence intervals (CIs) to identify regional differences in vaccine choices, side effects, information sources, and reasons for vaccination. A weighted analysis extended the results to represent the visible minorities across provinces. Results: Higher rates of Pfizer were administered to participants in Ontario (73%), the Prairies (72%), British Columbia (71%), and Quebec (70%). British Columbia had the highest Moderna rate (59%). The most common side effect was pain at the injection site in Quebec (62%), Ontario (62%), BC (62%), and in the Atlantic (61%). Healthcare professionals and government sources were the most trusted information sources, with healthcare professionals trusted particularly in the Prairies (70%) and government sources similarly trusted in Quebec (65%) and Ontario (65%). In the Atlantic, 86% of refusals were due to side effects and 69% were due to prior negative vaccine experiences. Conclusions: Leveraging healthcare professionals’ trust, community engagement, and flexible policies can help policymakers improve pandemic preparedness and boost vaccine acceptance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".