Determinants of Equitable Data Governance for African, Caribbean, and Black Communities in Health Research in High-Income Countries: Protocol for a Scoping Review
Notice bibliographique
Résumé
BACKGROUND: African, Caribbean, and Black (ACB) communities in high-income countries continue to experience persistent health inequities, driven by systemic anti-Black racism, socioeconomic disadvantage, and exclusion from health decision-making. Historically, data have been extracted from ACB communities without transparency, accountability, or community ownership. These inequitable practices have produced data systems that reinforce harm rather than promote equity. Equitable data governance, which promotes community ownership over data collection, access, and use, is increasingly recognized as a critical but underresearched determinant of health equity. OBJECTIVE: This protocol outlines the methodology of a scoping review to identify and synthesize evidence on the determinants of equitable data governance in health research involving ACB communities in high-income countries. METHODS: The review follows the 6-stage Arksey and O'Malley methodological framework, supplemented with updated guidance from the Joanna Briggs Institute. The searches were conducted in the Ovid MEDLINE, Ovid Embase, EBSCO CINAHL, APA PsycInfo, and Scopus databases. Peer-reviewed articles are considered, with no limits placed on study design, publication type, or date. Multiple reviewers will independently extract data by using a standardized form. A 3-phase thematic mapping process, conceptually informed by critical race theory, intersectionality, and community-based participatory research principles, will be conducted to analyze the data, generate themes, and interpret findings. RESULTS: The final comprehensive database searches were completed on December 17, 2024. The search strategy targeted literature on data management, governance, sharing, security, and ethical principles in relation to ACB populations in high-income countries. A total of 4365 records were screened at the title and abstract level, after deduplication, of which 247 studies were deemed potentially relevant and advanced to full-text screening. Following full-text screening and reference list searching a total of 15 articles were deemed eligible for analysis. The data extraction stage is scheduled to overlap and occur between November 2025 and February 2026. The thematic mapping and stakeholder consultations processes are scheduled between December 2025 and February 2026. The final review and manuscript submission are expected by March 2026, with dissemination activities planned for mid-2026. CONCLUSIONS: This review will synthesize existing information on key pillars, barriers, facilitators, promising data governance policies and practices, and recommendations relevant to ACB communities. The findings may inform the expansion of Ontario's Engagement, Governance, Access, and Protection guidelines and support tailored research and national data governance frameworks. The review is expected to contribute to policy, research, and community-led data initiatives. Dissemination will occur through academic publications, conferences, and community-based knowledge-sharing events. As the review relies solely on publicly available data, ethics approval is not required. TRIAL REGISTRATION: OSF Registries 10.17605/OSF.IO/Z82AY; https://osf.io/z82ay. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/82403.
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 | 0,147 | 0,151 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,006 |
| Méta-épidémiologie (sens large) | 0,012 | 0,018 |
| Bibliométrie | 0,022 | 0,021 |
| Études des sciences et des technologies | 0,007 | 0,007 |
| Communication savante | 0,010 | 0,011 |
| Science ouverte | 0,007 | 0,010 |
| Intégrité de la recherche | 0,011 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,059 | 0,011 |
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 ».