Metacognitive training (MCT) for psychosis: a systematic review and grade recommendations
Bibliographic record
Abstract
BACKGROUND: Recent meta-analyses support the inclusion of cognitive behavioral therapy (CBT) in schizophrenia treatment. Metacognitive Training (MCT) for psychosis is a psychoeducational program derived from CBT, with most meta-analyses showing favorable results. Although meta-analyses are commonly used in clinical practice to guide evidence-based decision-making, the grading system provides complementary results by offering a structured approach for assessing the strength and reliability of evidence and deriving grades of recommendations accordingly. METHODS: = 1942) and 10 meta-analyses. The primary outcome was positive symptoms, with secondary measures including negative symptoms, general psychopathology, self-esteem, functioning, insight, and cognitive function. RESULTS: Our findings are primarily based on the risks of bias attributed to RCTs (11 high, 19 moderate, 6 low) and, when necessary, on the overall confidence attributed to meta-analyses (3 low, 7 critically low). According to the WFSBP guidelines, strong recommendations should be made for using MCT for psychosis to improve post-treatment positive symptoms, delusions, and total psychotic symptoms (WFSBP-grade 1). Limited recommendations (WFSBP-grade 2) could be made for using MCT to improve post-treatment visuospatial abilities and to maintain benefits over time in psychopathology, functioning, self-esteem, episodic memory, and attention. CONCLUSIONS: MCT for psychosis is an evidence-based program, especially for positive symptoms, with long-lasting clinical benefits. These recommendations should be interpreted with caution given potential residual biases and heterogeneity among studies.
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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 | 0.034 | 0.095 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.021 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".