Interventions employed to address vaccine hesitancy among Black populations outside of African and Caribbean countries: a scoping review
Bibliographic record
Abstract
BACKGROUND: Black people are disproportionately affected by structural and social determinants of health, resulting in greater risks of exposure to and deaths from COVID-19. Structural and social determinants of health feed vaccine hesitancy and worsen health disparities. OBJECTIVE: This scoping review explored interventions that have been employed to address vaccine hesitancy among Black population outside of African and Caribbean countries. This review provides several strategies for addressing this deep-rooted public health problem. METHODS: The scoping review followed the five-step framework outlined by Arksey and O'Malley. It complies with reporting guidelines from the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). Research studies that examined interventions utilized to promote vaccine confidence within Black populations living outside of African and Caribbean countries were reviewed. FINDINGS: A total of 20 articles met the inclusion criteria for this study: 17 were quantitative studies and three were mixed-method studies. This scoping review highlighted six themes: educational advancement, messaging, multi-component approaches, outreach efforts, enhancing healthcare access, and healthcare provider leadership. CONCLUSION: The review identified effective interventions for addressing vaccine hesitancy among Black populations outside Africa and the Caribbean, emphasizing education, multidimensional approaches, and healthcare provider recommendations. It calls for more qualitative research and interventions in countries like Canada and the UK to enhance vaccine confidence and reduce mistrust.
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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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".