The state of COVID-19 vaccine confidence and need in Black individuals in Canada: Understanding the role of sociodemographic factors, health literacy, conspiracy theories, traumatic stressors and racial discrimination
Bibliographic record
Abstract
BACKGROUND: Black communities in Canada have been among the most affected by the COVID-19 pandemic, in terms of number of infections and deaths. They are also among those most hesitant about vaccination against COVID-19. However, while a few studies have documented the factors associated with COVID-19 vaccine hesitancy, those related to vaccine confidence remain unknown. To respond to this gap, this study aims to investigate factors associated to vaccine confidence in Black individuals in Canada. METHODS: A total of 2002 participants (1034 women) aged 14 to 89 years old (Mean age = 29.34, SD = 10.13) completed questionnaires assessing sociodemographic information, COVID-19 vaccine confidence and need, health literacy, conspiracy beliefs, major racial discrimination, and traumatic stressors related to COVID-19. RESULTS: Results showed an average score of COVID-19 vaccine confidence and need of 33.27 (SD = 7.24), with no significant difference between men (33.48; SD = 7.24) and women (33.08; SD = 7.91), t (1999) = 1.19, p = 0.234. However, there were significant differences according to employment status, migration status, age, inhabited province, spoken language, education, marital status, religion, and income. The linear regression model explained 25.8 % of the variance and showed that health literacy (B = 0.12, p < 0.001) and traumatic stressors related to COVID-19 (B = 0.21, p < .001) predicted COVID-19 vaccine confidence and need positively, while conspiracy beliefs (B = -1.14, p < 0.001) and major racial discrimination (B = -0.20, p = 0.044) predicted it negatively. CONCLUSIONS: This study showed that building the confidence of Black communities in vaccines requires health education, elimination of racial discrimination in the Canadian society and a focus on certain groups (e.g., young people, those living in Quebec and Ontario). The results also argue in favor of involving community leaders and organizations in the development and implementation of vaccination-related tools, strategies and programs by city, provincial and federal public health agencies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".