Metacognitive Functioning in Individuals at Clinical High Risk for Psychosis
Bibliographic record
Abstract
BACKGROUND: Metacognition has been described as the knowledge of our own cognitive processes. Metacognitive deficits are common in schizophrenia, but little is known about metacognition before the onset of full-blown psychosis. AIMS: This study aimed to longitudinally characterize metacognition in a sample of individuals at clinical high risk (CHR) for psychosis, and to determine if metacognition was related to later conversion to psychosis. METHOD: Participants (153 CHR individuals; 68 help seeking controls, HSC) were part of the large multi-site PREDICT study, which sought to determine predictors of conversion to psychosis. They were tested at baseline and 6 months using the Meta-Cognitions Questionnaire (MCQ) that has five sub-scales assessing different domains of metacognition. RESULTS: RESULTS of the mixed-effect models demonstrated significantly poorer scores at baseline for the CHR group compared to the HSC group in Negative beliefs about uncontrollability, Negative beliefs and the overall MCQ score. At the 6-month assessment, no difference was observed in metacognition between the two groups, but both groups showed improvement in metacognition over time. Those who later converted to psychosis had poorer performance on metacognitive beliefs at baseline. CONCLUSIONS: A poorer performance in metacognition can be seen as a marker of developing a full blown psychotic illness and confirms the potential value of assessing metacognitive beliefs in individuals vulnerable for psychosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".