Reporting Cultural Adaptation in Psychological Trials – The RECAPT criteria
Bibliographic record
Abstract
Background: There is a lack of empirical evidence on the level of cultural adaptation required for psychological interventions developed in Western, Educated, Industrialized, Rich, and Democratic (WEIRD) societies to be effective for the treatment of common mental disorders among culturally and ethnically diverse groups. This lack of evidence is partly due to insufficient documentation of cultural adaptation in psychological trials. Standardised documentation is needed in order to enhance empirical and meta-analytic evidence. Process: A "Task force for cultural adaptation of mental health interventions for refugees" was established to harmonise and document the cultural adaptation process across several randomised controlled trials testing psychological interventions for mental health among refugee populations in Germany. Based on the collected experiences, a sub-group of the task force developed the reporting criteria presented in this paper. Thereafter, an online survey with international experts in cultural adaptation of psychological interventions was conducted, including two rounds of feedback. Results: The consolidation process resulted in eleven reporting criteria to guide and document the process of cultural adaptation of psychological interventions in clinical trials. A template for documenting this process is provided. The eleven criteria are structured along A) Set-up; B) Formative research methods; C) Intervention adaptation; D) Measuring outcomes and implementation. Conclusions: Reporting on cultural adaptation more consistently in future psychological trials will hopefully improve the quality of evidence and contribute to examining the effect of cultural adaptation on treatment efficacy, feasibility, and acceptability.
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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 | 0.656 | 0.793 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.016 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".