“Black People Listen to Black People”: Strategies to Improve COVID-19 Vaccine Confidence Among Black People Living in Canada
Bibliographic record
Abstract
Background: Compared to other groups of Canadians, Black people have been significantly more affected by COVID-19 and appear to be more hesitant to receive the COVID-19 vaccine. This article identifies approaches or strategies to increase vaccine confidence and uptake among Black people in Canada. Methods: Thirty-six Black people of diverse ethnicities, aged 18 years and above, living in six provinces across Canada were interviewed. An inductive thematic approach was employed to analyze the interview data. Results: Building trust was at the center of the strategies identified and spoke to the meaningful and practical ways the sociocultural realities of Black people living in Canada can be used to inform and implement the most effective health interventions. Identified strategies include public education, building trust through Black-led community engagement, and addressing barriers to vaccine convenience focusing on health literacy and communication. Together, these strategies consider the nuance of the message, diversity of messenger(s), and communication channels and call for a move away from generic health promotion messages to tailored communications grounded in community expertise and the experiences of Black people across all levels of healthcare service provision. Conclusions: Health promotion and public health messages must acknowledge difference, tailor approaches to target audiences, and foster lasting collaborations informed by members of the Black community. Government agencies and healthcare service providers should foster the relationships established during the pandemic, document lessons learned, remove systemic barriers to healthcare, and create an emergency preparedness guide for community engagement and health promotion for Black people living in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".