A Digital Psychosocial Service (Sui App) for Arabic-Speaking Refugees in Switzerland: Development and Cultural-Contextual Adaptation Using a User-Centered and Participatory Approach
Notice bibliographique
Résumé
BACKGROUND: Upon arriving in host countries, forcibly displaced people face psychological, cultural, as well as sociostructural challenges. Access to mental health and psychosocial support (MHPSS) remains limited, affecting both refugees and host country structures. Digital services offer promise in addressing these challenges, given their potential for scalability and accessibility. Despite the increasing use of digital MHPSS, cultural and contextual adaptation remains insufficiently documented, requiring systematic documentation. OBJECTIVE: This study aims (1) to assess the psychosocial needs of newly arrived refugees in Switzerland and identify potential digital support solutions and (2) to participatorily develop and culturally adapt the Sui app to address those needs for Arabic-speaking refugees. METHODS: We used a 2-phase adaptation process, structured using the RECAPT (Reporting Cultural Adaptation in Psychological Trials) framework to ensure systematic documentation. The preparation phase (2019-2021) included a desk review, needs assessment interviews, and conceptualization discussions. The development phase (2021-2022) included iterative development loops with a user advisory board (UAB) and a beta test, followed by final adjustments. RESULTS: The desk review provided 5 key insights guiding development: mental health stigma, health literacy, sociostructural aspects, adherence to digital interventions, and task-shifting approaches. Findings from the needs assessment with 22 asylum care interviewees, 2 intercultural interpreters, and 4 target group interviewees confirmed the importance of integrating sociostructural and psychological factors. Through conceptualization discussions with a consultancy agency and 4 UAB (n=9) meetings, the scope of the Sui app was drafted: 9 relevant, everyday life topics (eg, asylum process, housing, and work), 5 psychological topics (eg, stress, sleep, and emotion regulation), and 1 peer-guided chat support feature. A multilevel translation process, involving 5 Arabic speakers from different countries, ensured linguistic accessibility. This allowed the UAB to focus on surface, content, and delivery during 19 development loops. The app's content was delivered in various formats, including text, illustrations, video testimonies, and audio exercises. The beta test revealed high acceptability, with most users engaging with the app several times a week. However, technical challenges such as slow loading times hindered the full exploration of the features. Participants valued the peer support function, highlighting the importance of faster response times and personalized messages. Based on the beta test, technical and content refinements were made to prepare the app for a future quantitative evaluation. CONCLUSIONS: Through engagement with stakeholders and adherence to the RECAPT framework, we carefully considered the cultural and contextual circumstances of Arabic-speaking refugees newly arrived in Switzerland. The iterative participatory development process, spanning a preparation and development phase, ensured usability, accessibility, and relevance, while highlighting challenges in technical implementation and peer support. This study contributes to the existing knowledge of mental health and the needs of refugees and provides insights for future cultural adaptation of MHPSS 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 | 0,012 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,007 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».