Metacognitive training for schizophrenia patients (MCT): A pilot study on feasibility, treatment adherence, and subjective efficacy.
Bibliographic record
Abstract
Objectives: A plethora of studies has confirmed that several cognitive biases (e.g., attributional style, jumping to conclu-sions, bias against disconfirmatory evidence, theory of mind, over-confidence in errors, need for closure, and low self-esteem) may play a pathogenetic role in the emergence and/or maintenance of the disorder, particularly delusions. The present study explored the safety, acceptance and subjective efficacy of a newly developed intervention program aimed at increasing awareness of, and possibly ameliorating, cognitive and behavioural biases in schizophrenia. Metacognitive training (MCT) builds upon inferences drawn from basic research on cognition and metacognition in schizophrenia. Methods: Forty outpatients were randomized to MCT and a control intervention (cognitive remediation, CogPack). Treatment in either group was performed twice weekly for an entire duration of 4 weeks (i.e., 8 sessions each lasting 45-60 minutes). At the end of the training, participants were asked to evaluate the subjective utility and efficacy of the program. In addition, treatment adherence and adverse events were documented. Results: MCT yielded superior scores relative to CogPack on several subjective parameters. Treatment adherence was comparable and no adverse events were noted during either intervention. Conclusions: The present study underscores the feasibility and acceptance of metacogni-tive training in psychosis. Future trials are warranted to verify the impact of MCT on the amelioration of metacogni-
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".