Cultural adaptation of cognitive–behavioural therapy
Bibliographic record
Abstract
SUMMARY The study of cultural factors in the application of psychotherapy across cultures – ethnopsychotherapy – is an emerging field. It has been argued that Western cultural values underpin cognitive–behavioural therapy (CBT) as they do other modern psychosocial interventions developed in the West. Therefore, attempts have been made to culturally adapt CBT for ethnic minority patients in the West and local populations outside the West. Some frameworks have been proposed based on therapists’ individual experiences, but this article describes a framework that evolved from a series of qualitative studies to culturally adapt CBT and that was field tested in randomised controlled trials. We describe the process of adaptation, details of methods used and the areas that need to be focused on to adapt CBT to a given culture. Further research is required to move the field forward, but cultural adaptation alone cannot improve outcomes. Access to evidence-based psychosocial interventions, including CBT, needs to be improved for culturally adapted interventions to achieve their full potential. LEARNING OBJECTIVES After reading this article you will be able to: • recognise the link between cultural factors and the need to adapt psychosocial interventions • identify the necessary steps to culturally adapt CBT • understand the modifications required to deliver therapy to individuals from diverse cultural backgrounds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".