Two decades of metacognitive training for psychosis: successes, setbacks, and innovations
Bibliographic record
Abstract
INTRODUCTION: Schizophrenia is among the most debilitating mental health conditions. While antipsychotic medication represents the primary pillar of treatment, guidelines now also recommend psychotherapy. Metacognitive Training (MCT) has emerged over the past 20 years as a novel approach that addresses the cognitive biases involved in the pathogenesis of schizophrenia. MCT seeks to enhance patients' awareness of their cognitive distortions and reduce overconfidence. MCT is available in individual and group formats. AREAS COVERED: This review provides a comprehensive overview of MCT, detailing its theoretical foundations, development, and implementation. The authors present meta-analyses demonstrating its efficacy in improving positive symptoms as well as negative symptoms and self-esteem. Lastly, the review covers the integration of the COGITO app to support MCT. For our narrative review we searched data bases including PubMed, Web of Science, EMBASE, PsycINFO, and MEDLINE. EXPERT OPINION: MCT represents a significant advance in the treatment of schizophrenia, offering a flexible, low-threshold intervention that can be easily implemented in various clinical settings. The training's focus on metacognitive processes provides patients with tools to understand and manage their symptoms. Future research should seek to develop shortened as well as more personalized versions and investigate the long-term sustainability of the effects.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".