Detecting and defusing cognitive traps: metacognitive intervention in schizophrenia
Bibliographic record
Abstract
PURPOSE OF REVIEW: Until recently, psychological therapy for schizophrenia was considered harmful or inefficient by many clinicians. The reservation against psychotherapy is partly rooted in the assumption that delusions in particular and schizophrenia in general are not amenable to psychological understanding and represent 'utter madness'. 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, are involved in the pathogenesis of schizophrenia positive symptoms, particularly delusions. A recently developed group program, called metacognitive training (MCT), is presented targeting these biases. MCT is a hybrid of psychoeducation, cognitive remediation and cognitive-behavioural therapy. RECENT FINDINGS: This review introduces new evidence on cognitive biases involved in the pathogenesis of schizophrenia and demonstrates how the MCT raises the patients' (metacognitive) awareness to detect and defuse such 'cognitive traps'. At the end, a new individualized variant entitled MCT+ is presented targeting individual delusional ideas. Finally, empirical results are summarized that speak in favour of the feasibility and efficacy of MCT. SUMMARY: Recent studies assert marked cognitive biases in schizophrenia. MCT has evolved as a feasible and effective complement of standard psychiatric treatment.
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".