Metacognitive training for psychosis (MCT): past, present, and future
Bibliographic record
Abstract
This article provides an overview and retrospective on metacognitive training for psychosis (MCT), which first appeared approximately 2 decades ago. We recount how our empirical understanding of psychosis at that time led to the first preliminary version of the program. We describe setbacks and challenges that led to major changes, including revisions to existing modules (e.g., more focus on metacognitive variables, particularly on decision confidence as one of the primary targets of treatment) and the creation of new modules addressing mood, as well as attempts to improve sustainability of effects via homework exercises and a smartphone app ( www.uke.de/mct_app ). We have also enhanced dissemination efforts by creating new culturally sensitive language versions and facilitating low-threshold training through e-learning courses ( www.uke.de/e-mct ). Finally, we discuss several meta-analyses on the efficacy of MCT that have been published over the last decade. While reviews were initially inconsistent, possibly reflecting the insufficient statistical power and lower design quality of the first MCT studies, more recent meta-analyses have confirmed the efficacy of MCT on positive symptoms, insight, and cognitive biases, which has led to the inclusion of MCT in some national treatment guidelines for schizophrenia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".