Effectiveness of Interventions to Improve Digital Health Literacy in Forced Migrant Populations: Protocol for a Mixed Methods Systematic Review (Preprint)
Notice bibliographique
Résumé
BACKGROUND Digital health literacy is considered a health determinant that can influence improved health and well-being, health equity, and the reduction of social health inequalities. Therefore, it serves as an asset for individuals to promote their health. However, low digital health literacy is a major problem among forced migrant populations. They do not always have the capacity and skills to access digital health resources and use them appropriately. To our knowledge, no studies are currently available to examine effective interventions for improving digital health literacy among forced migrant populations. OBJECTIVE This paper presents the protocol for a systematic review that aims to assess the effectiveness of digital health literacy interventions among forced migrant populations. With this review, our objectives are as follows: (1) identify interventions designed to improve digital health literacy among forced migrant populations, including interventions aimed at creating enabling conditions or environments that cater to the needs and expectations of forced migrants limited by low levels of digital health literacy, with the goal of facilitating their access to and use of eHealth resources; (2) define the categories and describe the characteristics of these interventions, which are designed to enhance the abilities of forced migrants or adapt digital health services to meet the needs and expectations of forced migrant populations. METHODS A mixed methods systematic review will be conducted according to the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) checklist. The research will be conducted in an iterative process among the different authors. With the help of a medical information specialist, a specific search strategy will be formulated for the 6 most relevant databases (ie, MEDLINE, Embase, CINAHL, Web of Science, Academic Search Premier, PsycINFO, and the Google Scholar search engine). A literature search covering studies published between 2000 and 2022 has already been conducted. Two reviewers then proceeded, individually and independently, to conduct a double selection of titles, abstracts, and then full texts. Data extraction will be conducted by a reviewer and validated by a senior researcher. We will use the narrative synthesis method (ie, structured narrative summaries of key themes) to present a comprehensive picture of effective digital health literacy interventions among forced migrant populations and the success factors of these interventions. RESULTS The search strategy and literature search were completed in December 2022. A total of 1232 articles were identified. The first selection was completed in July 2023. The second selection is still in progress. The publication of the systematic review is scheduled for December 2023. CONCLUSIONS This mixed methods systematic review will provide comprehensive knowledge on effective interventions for digital literacy among forced migrant populations. The evidence generated will further inform stakeholders and aid decision makers in promoting equitable access to and use of digital health resources for forced migrant populations and the general population in host countries. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/50798
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 | 0,090 | 0,139 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,006 |
| Méta-épidémiologie (sens large) | 0,020 | 0,023 |
| Bibliométrie | 0,011 | 0,012 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,009 | 0,009 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,009 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,094 | 0,012 |
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 ».