The current status of culturally adapted mental health interventions: a practice-focused review of meta-analyses
Bibliographic record
Abstract
Shanaya Rathod,1 Lina Gega,2 Amy Degnan,3 Jennifer Pikard,4 Tasneem Khan,5 Nusrat Husain,3 Tariq Munshi,4 Farooq Naeem4 1Southern Health NHS Foundation Trust, Southampton, UK; 2Department of Health Sciences, University of York, York, UK; 3Division of Psychology and Mental Health, University of Manchester, Manchester, UK; 4Department of Psychiatry, Queen’s University, Kingston, ON, Canada; 5Faculty of Applied Health Sciences, School of Public Health and Health Systems, University of Waterloo, Waterloo, ON, Canada Abstract: In recent years, there has been a steadily increasing recognition of the need to improve the cultural competence of services and cultural adaptation of interventions so that every individual can benefit from evidence-based care. There have been attempts at culturally adapting evidence-based interventions for mental health problems, and a few meta-analyses have been published in this area. This is, however, a much debated subject. Furthermore, there is a lack of a comprehensive review of meta-analyses and literature reviews that provide guidance to policy makers and clinicians. This review summarizes the current meta-analysis literature on culturally adapted interventions for mental health disorders to provide a succinct account of the current state of knowledge in this area, limitations, and guidance for the future research. Keywords: cultural adaptation, interventions, mental illness, meta-analysis, minority ethnic, psychotherapies
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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 | 0.053 | 0.160 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.026 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".