Cognitive–behavioural therapy for the symptoms of schizophrenia: systematic review and meta-analysis with examination of potential bias
Bibliographic record
Abstract
BACKGROUND: Cognitive-behavioural therapy (CBT) is considered to be effective for the symptoms of schizophrenia. However, this view is based mainly on meta-analysis, whose findings can be influenced by failure to consider sources of bias. AIMS: To conduct a systematic review and meta-analysis of the effectiveness of CBT for schizophrenic symptoms that includes an examination of potential sources of bias. METHOD: Data were pooled from randomised trials providing end-of-study data on overall, positive and negative symptoms. The moderating effects of randomisation, masking of outcome assessments, incompleteness of outcome data and use of a control intervention were examined. Publication bias was also investigated. RESULTS: Pooled effect sizes were -0.33 (95% CI -0.47 to -0.19) in 34 studies of overall symptoms, -0.25 (95% CI -0.37 to -0.13) in 33 studies of positive symptoms and -0.13 (95% CI -0.25 to -0.01) in 34 studies of negative symptoms. Masking significantly moderated effect size in the meta-analyses of overall symptoms (effect sizes -0.62 (95% CI -0.88 to -0.35) v. -0.15 (95% CI -0.27 to -0.03), P = 0.001) and positive symptoms (effect sizes -0.57 (95% CI -0.76 to -0.39) v. -0.08 (95% CI -0.18 to 0.03), P<0.001). Use of a control intervention did not moderate effect size in any of the analyses. There was no consistent evidence of publication bias across different analyses. CONCLUSIONS: Cognitive-behavioural therapy has a therapeutic effect on schizophrenic symptoms in the 'small' range. This reduces further when sources of bias, particularly masking, are controlled for.
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 | 0.046 | 0.102 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.055 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".