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Record W1809552735 · doi:10.3109/09540261.2015.1067598

Using cognitive behaviour therapy with South Asian Muslims: Findings from the culturally sensitive CBT project

2015· article· en· W1809552735 on OpenAlexaff
Farooq Naeem, Peter Phiri, Tariq Munshi, Shanaya Rathod, Muhammad Ayub, Mary Gobbi, David Kingdon

Bibliographic record

VenueInternational Review of Psychiatry · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsQueen's University
Fundersnot available
KeywordsCollectivismPsychotherapistPsychologyAdaptation (eye)CognitionCulturally sensitiveFocus groupPopulationClinical psychologyMedicineIndividualismPsychiatrySocial psychologySociology

Abstract

fetched live from OpenAlex

It has been suggested that cognitive behaviour therapy (CBT) needs adaptation for it to be effective for patients from collectivistic cultures, as currently CBT is underpinned by individualistic values. In prior studies we have demonstrated that CBT could be adapted for Pakistani patients in Southampton, UK, and for local populations in Pakistan. Findings from these studies suggest that CBT can be adapted for patients from collectivistic cultures using a series of steps. In this paper we focus on these steps, and the process of adapting CBT for specific groups. The adaptation process should focus on three major areas of therapy, rather than simple translation of therapy manuals. These include (1) awareness of relevant cultural issues and preparation for therapy, (2) assessment and engagement, and (3) adjustments in therapy. We also discuss the best practice guidelines that evolved from this work to help therapists working with this population. We reiterate that CBT can be adapted effectively for patients from traditional cultures. This is, however, an emerging area in psychotherapy, and further work is required to refine the methodology and to test adapted CBT.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.088
GPT teacher head0.416
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations68
Published2015
Admission routes1
Has abstractyes

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