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 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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".