Investigating the relationship between specific negative symptoms and metacognitive functioning in psychosis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Disrupted metacognition is implicated in development and maintenance of negative symptoms, but more fine-grained analyses would inform precise treatment targeting for individual negative symptoms. AIMS: This systematic review identifies and examines datasets that test whether specific metacognitive capacities distinctly influence negative symptoms. MATERIALS & METHODS: PsycINFO, EMBASE, Medline and Cochrane Library databases plus hand searching of relevant articles, journals and grey literature identified quantitative research investigating negative symptoms and metacognition in adults aged 16+ with psychosis. Authors of included articles were contacted to identify unique datasets and missing information. Data were extracted for a risk of bias assessment using the Quality in Prognostic Studies tool. RESULTS: 85 published reports met criteria and are estimated to reflect 32 distinct datasets and 1623 unique participants. The data indicated uncertainty about the relationship between summed scores of negative symptoms and domains of metacognition, with significant findings indicating correlation coefficients from 0.88 to -0.23. Only eight studies investigated the relationship between metacognition and individual negative symptoms, with mixed findings. Studies were mostly moderate-to-low risk of bias. DISCUSSION: The relationship between negative symptoms and metacognition is rarely the focus of studies reviewed here, and negative symptom scores are often summed. This approach may obscure relationships between metacognitive domains and individual negative symptoms which may be important for understanding how negative symptoms are developed and maintained. CONLCLUSION: Methodological challenges around overlapping participants, variation in aggregation of negative symptom items and types of analyses used, make a strong case for use of Individual Participant Data Meta-Analysis to further elucidate these relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".