Digital Health Interventions to Improve Vaccination Rates and Awareness Among Immigrant Populations: Barriers, Facilitators, and Outcomes - A Scoping Review
Notice bibliographique
Résumé
Background: Digital health interventions are suggested to improve vaccine coverage and awareness in the general population. However, given the scarcity of information within migrant populations, this scoping review aims to identify existing evidence on digital health interventions designed to improve vaccination and health outcomes among this social group. Methods: In this scoping review, we searched CENTRAL, PubMed, and Scopus for observational studies and randomized controlled trials (RCT). Two independent reviewers screened articles, performed data extraction and synthesis, and assessed bias risk using CovidenceⓇ. Bias was evaluated with the Cochrane RoB 2, Newcastle-Ottawa Scale (NOS), or JBI tool. We analyzed digital health interventions aiming to boost vaccination rates and awareness among immigrant populations, evaluating barriers and facilitators. The focus was on vaccines such as COVID 19, HPV, Hepatitis B, Influenza, childhood vaccines. Targeting immigrants, primarily from South and East Asia, the Middle East, and Hispanic/Latinx populations. The interventions of interest included digital appointment reminders, mobile applications, messaging platforms, and digital storytelling. Findings: Out of the 673 studies initially identified, 19 met the criteria for data extraction and synthesis. Published between 2012 and 2024, these included six quasi experimental studies, five cross-sectional studies, four randomized control trials, three qualitative studies and one survey. Research spanned several continents and countries such as North America, Germany, China, Jordan,Turkey and Uganda. The role of digital tools in increasing the vaccination rate must be reinforced, with particular emphasis on the personalized content of the message for recipients. The measurement tools influencing vaccination rates are not only diverse but also complex. They encompass a wide range of factors, from knowledge about immunization to emotions and vaccine intention, highlighting the multifaceted nature of the issue. Several factors interfere with vaccination rates (e.g., language barriers, costs, long wait times, scheduling difficulties, lack of transportation, and child support). Some confounders may impact the effectiveness and uptake of vaccination programs in undocumented immigrants from seeking vaccination services, such as socioeconomic status, education level, language barriers, cultural beliefs and practices, distrust in the healthcare system, legal status and fear of deportation. Interpretation: Digital health interventions show promise in enhancing vaccination awareness among migrant populations. Findings from this scoping review suggest that these interventions should be customized for specific populations, taking into account barriers, facilitators, and cultural beliefs.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,028 | 0,196 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».