Improving COVID-19 Vaccine Uptake among Black Populations: A Systematic Review of Strategies
Bibliographic record
Abstract
Given the growing body of evidence on COVID-19 vaccine hesitancy among Black populations, the aim of this systematic review was to identify the interventions and strategies used to improve COVID-19 vaccine confidence and uptake among Black populations globally. To identify relevant studies, we conducted a systematic review of the literature based on a systematic search of 10 electronic databases: MEDLINE, Embase, PsycINFO, CINAHL, Scopus, Cochrane Library, Web of Science, Sociological Abstracts, Dissertations and Theses Global, and SocINDEX. We screened a total of 1728 records and included 14 peer-reviewed interventional studies that were conducted to address COVID-19 vaccine hesitancy among Black populations. A critical appraisal of the included studies was performed using the Newcastle-Ottawa Quality Assessment Scale. The intervention strategies for increasing COVID-19 vaccine uptake were synthesized into three major categories: communication and information-based interventions, mandate-based interventions, and incentive-based interventions. Interventions that incorporated communication, community engagement, and culturally inclusive resources significantly improved vaccine uptake among Black populations, while incentive- and mandate-based interventions had less impact. Overall, this systematic review revealed that consideration of the sociocultural, historical, and political contexts of Black populations is important, but tailored interventions that integrate culture-affirming strategies are more likely to decrease COVID-19 vaccine hesitancy and increase uptake among Black populations.
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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 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".