Investigating the relationship between negative symptoms and metacognitive functioning in psychosis: An individual participant data meta‐analysis
Bibliographic record
Abstract
PURPOSE: Negative symptoms are a persistent, yet under-explored problem in psychosis. Disturbances in metacognition are a potential causal factor in negative symptom development and maintenance. This meta-analysis uses individual participant data (IPD) from existing research to assess the relationship between negative symptoms and metacognition treated as summed scores and domains. METHODS: Data sets containing individuals with negative symptoms and metacognition data, aged 16+ with psychosis, were identified according to pre-specific parameters. IPD integrity and completeness were checked and data were synthesized in two-stage meta-analyses of each negative symptoms cluster compared with metacognition in seemingly unrelated regression using restricted maximum likelihood estimation. Planned and exploratory sensitivity analyses were also conducted. RESULTS: Thirty-three eligible data sets were identified with 21 with sufficient similarity and availability to be included in meta-analyses, corresponding to 1301 participants. The strongest relationships observed were between summed scores of negative symptoms and metacognition. Metacognitive domains of self-reflectivity and understanding others' minds, and expressive negative symptoms emerged as significant in some meta-analyses. The uncertainty of several effect estimates increased significantly when controlling for covariates. CONCLUSIONS: This robust meta-analysis highlights the impact of using summed versus domain-specific scores of metacognition and negative symptoms, and relationships are not as clear-cut as once believed. Findings support arguments for further differentiation of negative symptom profiles and continued granular exploration of the relationship between metacognition and negative symptoms.
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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 | 0.024 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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".