A Digital Psychosocial Service (Sui App) for Arabic-Speaking Refugees in Switzerland: Development and Cultural-Contextual Adaptation Using a User-Centered and Participatory Approach
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".