Understanding Low Vaccine Uptake in African, Caribbean, and Black Populations Relative to Public Health in High-Income Countries: A Scoping Review Protocol
Bibliographic record
Abstract
Abstract Background Vaccination has significantly contributed to reducing once common and even deadly infectious diseases, yet vaccine hesitancy threatens the emergence of vaccine-preventable diseases. The COVID-19 pandemic has caused the need for the largest mass vaccination campaign ever undertaken to date; however, 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. Low vaccination rates in this vulnerable population are a pinnacle concern in public health, as low vaccination rates in ACB communities may both be the result of health inequities, as well as further exacerbate them. Purpose To explore low vaccine uptake in African, Caribbean, and Black (ACB) populations relative to public health in high-income countries. Objectives 1) To identify concepts and boundaries of existing evidence sources on low vaccine uptake in ACB populations; 2) To map out the evidence on the concepts and boundaries and to identify gaps in the research; and 3) To determine existing interventions to improve low vaccine uptake in the study population. Methodology This scoping review follows the Joanna Briggs Institute (JBI) framework for scoping reviews, supplemented by the Preferred Reporting Items for Systematic Reviews extension (PRISMA-ScR). Any deviations from the JBI recommendations are stated. Theoretical underpinnings of the intersectionality approach will be used to help interpret the complexities of health inequities in the ACB population, including those related to the social determinants of health (SDOH). Search strategies were developed by an information specialist (VC) and peer- reviewed using the PRESS guideline. The search was conducted in: MEDLINE(R) ALL (OvidSP), Embase (OvidSP), CINAHL (EBSCOHost), APA PsycInfo (OvidSP), Cochrane Central Register of Controlled Trials (OvidSP), Cochrane Database of Systematic Reviews (OvidSP), Allied & Complimentary Medicine Database (Ovid SP), and Web of Science. Eligibility criteria are based on the Population, Concept, Context (PCC) framework. The inclusion criteria for this study included evidence -sources with a primary focus on African, Caribbean, and Black populations, and other related terms; high-income countries as defined by the World Bank where ACB populations are considered a minority; all service providers; English and French languages; all types of evidence sources; related to low vaccine uptake and alternative terms; all vaccines; and publications from 2020- current (July 19 th , 2022). The screening, selection, and extraction of the evidence sources were completed by the AVA research team. Analysis was done through the process of Thematic Mapping, and summarization and presentation of the findings were done through a narrative description organized using the socioeconomic model (SEM) as a framework. Ethics and dissemination This study used published evidence sources with no human or animal participants; thus, ethical approval and consent to participate are not applicable. Dissemination This will occur through peer-reviewed open-access journals and conferences that target stakeholders in public health, vaccination campaigns and overcoming inequities in healthcare.
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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 | 0.097 | 0.125 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.007 |
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".