ArticleScientific reports2024
Arabic-language digital interventions for depression in German routine health care are acceptable, but intervention adoption remains a challenge.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Leveraging AI for Analysis of Digital Health Information on Cancer Prevention Among Arab Youth and Adults: Content Analysis.JMIR infodemiology · 2026Article
- Digital solutions for migrant and refugee health: a framework for analysis and action.The Lancet regional health. Europe · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Migrants face many barriers to mental health care, such as different cultural concepts of distress, unfamiliar pathways to care, and language. Digital mental health interventions are effective and scalable in multi-language versions. However, their implementation into routine care is in its infancy. Here, we report on the Arabic- and German-language implementation of two digital interventions in Germany: The iFightDepression
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.