Understanding Low Vaccine Uptake in African, Caribbean, and Black Populations Relative to Public Health in High-Income Countries: A Scoping Review Protocol
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,097 | 0,125 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,005 |
| Méta-épidémiologie (sens large) | 0,013 | 0,018 |
| Bibliométrie | 0,019 | 0,016 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,009 | 0,008 |
| Science ouverte | 0,006 | 0,007 |
| Intégrité de la recherche | 0,008 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,042 | 0,007 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».