COVID-19 Vaccine Willingness among African, Caribbean, and Black People in Ottawa, Ontario
Bibliographic record
Abstract
Vaccines have been identified as a crucial strategy to control the spread of COVID-19 and reduce its impact. However, there are concerns about the acceptance of vaccines within African, Caribbean, and Black (ACB) communities. Based on a community sample of ACB people in Ottawa, Ontario (n = 375), the current study aimed to use logistic regression analysis and identify factors associated with COVID-19 vaccine willingness. A multivariate analysis shows that ACB people who believed that the ACB population is at a higher risk for COVID-19 were more likely to be willing to receive the vaccine compared to those who did not (OR = 1.79, p < 0.05). ACB people who had received at least one dose of the COVID-19 vaccine were more likely to be willing to receive it in the future (OR = 2.75, p < 0.05), and trust in government COVID-19 information was also positively associated with vaccine willingness (OR = 3.73, p < 0.01). In addition, English-speaking respondents were more willing to receive the vaccine compared to French-speaking respondents (OR = 3.21, p < 0.01). In terms of socioeconomic status, ACB people with a post-graduate degree (OR = 2.21, p < 0.05) were more likely to report vaccine willingness compared to those without a bachelor’s degree. Based on these findings, we discuss implications for policymakers and directions for future research.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".