Metacognitive training in schizophrenia: from basic research to knowledge translation and intervention
Bibliographic record
Abstract
PURPOSE OF REVIEW: There has been a marked increase in the study of cognitive biases in schizophrenia, which has in part been stimulated by encouraging results with cognitive-behavioral interventions in the disorder. We summarize new evidence on cognitive biases thought to trigger or maintain positive symptoms in schizophrenia and present a new therapeutic intervention. RECENT FINDINGS: Recent studies indicate that patients with paranoid schizophrenia jump to conclusions, show attributional biases, share a bias against disconfirmatory evidence, are overconfident in errors, and display problems with theory of mind. Many of these biases precede the psychotic episode and may represent cognitive traits. Building upon this literature, we developed a metacognitive training program that aims to convey scientific knowledge on cognitive biases to patients and provides corrective experiences in an engaging and supportive manner. Two new studies provide preliminary evidence for the feasibility and efficacy of this approach. SUMMARY: The gap between our advanced understanding of cognitive processes in schizophrenia and its application in clinical treatment is increasingly being narrowed. Despite emerging evidence for the feasibility and efficacy of metacognitive training as a stand-alone program, its most powerful application may be in combination with individual cognitive-behavioral therapy.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| 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".