Effectiveness of Interventions to Improve Digital Health Literacy in Forced Migrant Populations: Mixed Methods Systematic Review
Notice bibliographique
Résumé
BACKGROUND: Digital health literacy (DHL), recognized as a key determinant of health, can influence health and well-being, improve health equity, and reduce health disparities. However, DHL is often limited among forced migrant populations, who usually lack the skills to understand and evaluate health information or to access and use digital health resources appropriately. OBJECTIVE: We aimed to (1) identify effective interventions designed to improve DHL among forced migrant populations and (2) categorize and describe the characteristics of interventions that aim to improve the abilities of forced migrants or adapt digital health services to meet the needs and expectations of forced migrant populations limited by low levels of DHL. METHODS: We conducted a mixed methods systematic review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, involving an iterative process among the authors. A medical information specialist assisted in developing a search strategy for the 6 most relevant databases (MEDLINE, Embase, CINAHL, Web of Science, Academic Search Premier, and PsycINFO) and the Google Scholar search engine, covering studies published between 2000 and 2022. Pairs of reviewers selected, individually and independently, titles, abstracts, and then full texts. Data extraction and quality assessment were performed by 2 reviewers and validated by a senior researcher. We used narrative synthesis to provide a comprehensive overview of effective DHL interventions for forced migrant populations, highlighting their success factors. RESULTS: We identified 1845 studies, of which only 6 (0.33%) were finally selected for narrative synthesis. Studies were excluded due to irrelevance, lack of primary data, or low methodological quality. The analysis revealed a diverse methodological landscape with a predominance of qualitative approaches aimed at understanding the challenges and needs of forced migrants concerning DHL. The main challenges were associated with cultural, linguistic, and practical contexts. Interventions targeted various groups, including older adults, individuals with low literacy or education, and those with limited digital experience. We identified 4 effective educational intervention categories to enhance DHL among forced migrants: education and training; education and social support; enabling and education; and social, educational, technological, and infrastructural support. Overall, most of the studies (5/6, 83%) reported positive results in terms of improving DHL among forced migrants. CONCLUSIONS: This systematic review highlights the importance of improving DHL among forced migrant populations to promote their health and well-being. In addition, it provides comprehensive knowledge about effective interventions conducted with these groups. These findings can inform stakeholders, particularly policy makers, of the need to address low DHL among forced migrant populations. Going forward, these stakeholders need to develop innovative initiatives that rely on holistic approaches and are based on the specific needs of forced migrants to improve equity and health outcomes. TRIAL REGISTRATION: PROSPERO CRD42022373448; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022373448. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/50798.
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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 | 0,047 | 0,157 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,019 | 0,021 |
| Bibliométrie | 0,012 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
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 ».