Cultural adaptations of CBT: a summary and discussion of the Special Issue on Cultural Adaptation of CBT
Bibliographic record
Abstract
Abstract Cognitive behavioural therapy (CBT) in its current form might not be acceptable to service users from a variety of backgrounds. Therefore, it makes sense to adapt CBT when working with diverse populations. Contributors to this special issue of theCognitive Behaviour Therapisthave tackled the issues around the cultural adaptation of CBT from various perspectives, using a variety of methods, and have addressed topics ranging from cultural adaptation to improving access to CBT. Here, I briefly summarize and discuss the papers in this special issue. I start with a systematic review of CBT for social anxiety across cultures. Seven articles cover aspects of adaptation of therapies for people from different backgrounds. Three papers discuss the issues of gender and sexuality when using CBT, while another three papers focus on refugees, asylum seekers and the homeless, and two papers describe the application of CBT with religious populations. Finally, there are seven papers on issues related to service delivery, practice and training and supervision when working with a diverse population. Collectively, papers in this special issue provide us with sufficient evidence that cultural considerations play a vital role when using CBT, offer practical suggestions for improving cultural competence and most importantly, can catalyse future research. However, the full potential of culturally adapted interventions will not be realized until and unless access to CBT is improved. Therefore, there is a need to build robust evidence to convince funders, policy makers and service managers.
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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 | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".