Restoring public trust in COVID-19 vaccine among Africans, Caribbean, and Black Canadians using Community based participatory research (CBPR)
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic had a significant global impact predominantly among Africans, Caribbean and Black (ACB) Canadians. ACB people experienced higher infection and recovery rates, greater health risks, and access to care. Evidence shows that public mistrust, due to anti-Black racism and historical trauma contributed to low vaccine uptake among ACB populations. Our study examined multi-level and intersectional issues impacting vaccine uptake and acceptance in Ottawa. This paper will focus on the theme of agency. Methods A mixed methods research (MMR) approach guided this study using the socio-ecological model (SEM), intersectionality lens, and community-based participatory research principles to generate qualitative results. The sample included 49 ACB participants involved in community focus group discussions (FGDs) and 27 participants from our World Cafe. The sample population from the FGDs included service providers (20), social workers (15), doctors (3), and nurses (3) and others (8) who were predominantly Black African (70%), mostly female (80%), age range was 35–44 (33%) with high school diplomas (40%). The sample for the World Cafe included ACB members from community organizations, public health services, and peer-equity navigators (PENs). Data were recorded digitally, transcribed verbatim, analyzed inductively. Data analysis methods were thematic analysis including inductive coding using NVivo software and rigor methods were member-checking and external audits. Results Our data showed rebuilding public trust is possible by bridging knowledge gaps on vaccine information, increasing service providers’ cultural competence capacity, and providing governance/leadership opportunities for ACB communities. Conclusion Restoring public trust will require dismantling racism by prioritizing ACB community’ needs, proactive and accessible culturally appropriate messages, and opportunities to develop policies to improve health outcomes.
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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 | 0.041 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".