Pilot randomised controlled trial of culturally adapted cognitive behavior therapy for psychosis (CaCBTp) in Pakistan
Bibliographic record
Abstract
BACKGROUND: Evidence for efficacy of cognitive-behavioural therapy (CBT) in treatment of schizophrenia is growing. CBT is effective and cost efficient in treating positive and negative symptoms. To effectively meet the needs of diverse cultural groups, CBT needs to be adapted to the linguistic, cultural and socioeconomic context. We aimed to assess the feasibility, efficacy and acceptability of a culturally adapted CBT for treatment of psychosis (CaCBTp) in a low-income country. METHODS: Rater-blind, randomised, controlled trial of the use of standard duration CBT in patients with psychosis from a low-income country. Participants with a ICD-10 diagnosis of psychosis were assessed using Positive and Negative Syndrome Scale for Schizophrenia (PANSS), Psychotic Symptom Rating Scales (PSYRATS), and the Schedule for Assessment of Insight (SAI) (baseline, 3 months and 6 months). They were randomized into the intervention group (n = 18) and Treatment As Usual (TAU) group (n = 18). The intervention group received 12 weekly sessions of CaCBTp. RESULTS: The CaCBTp group had significantly lower scores on PANSS Positive (p = 0.02), PANSS Negative (p = 0.045), PANSS General Psychopathology (p = 0.008) and Total PANSS (p = 0.05) when compared to TAU at three months. They also had low scores on Delusion Severity Total (p = 0.02) and Hallucination Severity Total (p = 0.04) of PSYRATS, as well as higher scores on SAI (p = 0.01) at the same time point. At six months only the improvement in PANSS positive scores (p = 0.045) met statistical significance.. CONCLUSIONS: It is feasible to offer CaCBTp as an adjunct to TAU in patients with psychosis, presenting to services in a lower middle-income country. TRIAL REGISTRATION: Clinicaltrials.gov identifier NCT02202694 (Retrospectively registered).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".