A Mobile-Based Preventive Intervention for Young, Arabic-Speaking Asylum Seekers During the COVID-19 Pandemic in Germany: Design and Implementation
Notice bibliographique
Résumé
BACKGROUND: Most individuals seeking asylum in Germany live in collective housing and are thus exposed to a higher risk of contagion during the COVID-19 pandemic. OBJECTIVE: In this study, we aimed to test the feasibility and efficacy of a culture-sensitive approach combining mobile app-based interventions and a face-to-face group intervention to improve knowledge about COVID-19 and promote vaccination readiness among collectively accommodated Arabic-speaking adolescents and young adults. METHODS: We developed a mobile app that consisted of short video clips to explain the biological basis of COVID-19, demonstrate behavior to prevent transmission, and combat misconceptions and myths about vaccination. The explanations were provided in a YouTube-like interview setting by a native Arabic-speaking physician. Elements of gamification (quizzes and rewards for solving the test items) were also used. Consecutive videos and quizzes were presented over an intervention period of 6 weeks, and the group intervention was scheduled as an add-on for half of the participants in week 6. The manual of the group intervention was designed to provide actual behavioral planning based on the health action process approach. Sociodemographic information, mental health status, knowledge about COVID-19, and available vaccines were assessed using questionnaire-based interviews at baseline and after 6 weeks. Interpreters assisted with the interviews in all cases. RESULTS: Enrollment in the study proved to be very challenging. In addition, owing to tightened contact restrictions, face-to-face group interventions could not be conducted as planned. A total of 88 participants from 8 collective housing institutions were included in the study. A total of 65 participants completed the full-intake interview. Most participants (50/65, 77%) had already been vaccinated at study enrollment. They also claimed to comply with preventive measures to a very high extent (eg, "always wearing masks" was indicated by 43/65, 66% of participants), but practicing behavior that was not considered as effective against COVID-19 transmission was also frequently reported as a preventive measure (eg, mouth rinsing). By contrast, factual knowledge of COVID-19 was limited. Preoccupation with the information materials presented in the app steeply declined after study enrollment (eg, 12/61, 20% of participants watched the videos scheduled for week 3). Of the 61 participants, only 18 (30%) participants could be reached for the follow-up interviews. Their COVID-19 knowledge did not increase after the intervention period (P=.56). CONCLUSIONS: The results indicated that vaccine uptake was high and seemed to depend on organizational determinants for the target group. The current mobile app-based intervention demonstrated low feasibility, which might have been related to various obstacles faced during the delivery. Therefore, in the case of future pandemics, transmission prevention in a specific target group should rely more on structural aspects rather than sophisticated psychological interventions.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».