A bias-oriented treatment approach: The metacognitive training for schizophrenia patients (MCT)
Bibliographic record
Abstract
Until recently, psychological therapy for schizophrenia was considered inefficient or even harmful by many clinicians. The reservation against psychotherapy is partly rooted in the assumption that delusions are not amenable to psychological understanding. However, meta-analyses suggest that cognitive intervention is effective in ameliorating schizophrenia symptoms. In addition, evidence has accumulated that cognitive biases, such as jumping to conclusions (JTC), are involved in the pathogenesis of schizophrenia positive symptoms, particularly delusions. A recently developed group program, called metacognitive training (MCT), is presented targeting cognitive biases. The MCT is a hybrid of psychoeducation, cognitive remediation and cognitive-behavioural therapy. Patients are taught strategies how to identify and defuse “cognitive traps”. The program can be downloaded at no cost at www.uke.de/mkt and is currently available in more than 20 languages. New evidence on the feasibility and efficacy of the MCT is presented. At the end, a novel individualized variant entitled MCT+ is demonstrated targeting individual delusional ideas. A random-controlled study asserts the efficacy of the MCT+ to reduce JTC as well as delusion severity and conviction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".