Understanding Low Vaccine Uptake in the Context of Public Health in High-Income Countries: A Scoping Review
Bibliographic record
Abstract
Although the COVID-19 pandemic has caused the need for the largest mass vaccination campaign ever undertaken to date, African, Caribbean, and Black (ACB) populations have shown both a disproportionately high degree of negative impacts from the pandemic and the lowest willingness to become vaccinated. This scoping review aims to investigate low vaccine uptake in ACB populations relative to public health in high-income countries. A search was conducted in MEDLINE(R) ALL (OvidSP), Embase (OvidSP), CINAHL (EBSCOHost), APA PsycInfo (OvidSP), the Cochrane Central Register of Controlled Trials (OvidSP), the Cochrane Database of Systematic Reviews (OvidSP), the Allied and Complimentary Medicine Database (Ovid SP), and the Web of Science following the Joanna Briggs Institute (JBI) framework for scoping reviews, supplemented by PRISMA-ScR. Theoretical underpinnings of the intersectionality approach were also used to help interpret the complexities of health inequities in the ACB population. The eligibility criteria were based on the population, concept, context (PCC) framework, and publications from 2020-19 July 2022 which discussed vaccine uptake amongst ACB people in high-income countries were included. Analysis was carried out through thematic mapping and produced four main themes: (1) racism and inequities, (2) sentiments and behaviors, (3) knowledge and communication, and (4) engagement and influence. This study has contributed to the identification and definition of the issue of low vaccine uptake in ACB populations and has illustrated the complexity of the problems, as vaccine access is hampered by knowledge, psychological, socioeconomic, and organizational barriers at the individual, organizational, and systemic levels, leading to structural inequities that have manifested as low vaccine uptake.
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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 | 0.021 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 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".