Adaptation and translation of mental health interventions in Middle Eastern Arab countries: A systematic review of barriers to and strategies for effective treatment implementation
Bibliographic record
Abstract
AIM: All too often, efficacious psychosocial evidence-based interventions fail when adapted from one culture to another. International translation requires a deep understanding of the local culture, nuanced differences within a culture, established service practices, and knowledge of obstacles and promoters to treatment implementation. This research investigated the following objectives to better facilitate cultural adaptation and translation of psychosocial and mental health treatments in Arab countries: (1) identify barriers or obstacles; (2) identify promoting strategies; and (3) provide clinical and research recommendations. METHODS: This systematic review of 22 psychosocial or mental health studies in Middle East Arab countries identified more barriers (68%) than promoters (32%) to effective translation and adaptation of empirically supported psychosocial interventions. RESULTS: Identified barriers include obstacles related to acceptability of the intervention within the cultural context, community and system difficulties, and problems with clinical engagement processes. Whereas identified promoter strategies centre on the importance of partnering and working within the local and cultural context, the need to engage with acceptable and traditional intervention characteristics, and the development of culturally appropriate treatment strategies and techniques. CONCLUSIONS: Although Arab cultures across the Middle East are unique, this article provides a series of core clinical and research recommendations to assist effective treatment adaptation and translation within Arab communities in the Middle East.
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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 | 0.031 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".