M71. THE INFLUENCE OF METACOGNITIVE CAPACITIES ON SPECIFIC NEGATIVE SYMPTOMS: A SYSTEMATIC REVIEW AND INDIVIDUAL PARTICIPANT META-ANALYSIS OF INTERVIEW-BASED DATA
Bibliographic record
Abstract
Abstract Background Healthy metacognition involves several capacities, including the ability to integrate information about the self and others in order to formulate ways of coping with social challenges and psychological distress. Multiple studies have demonstrated that reduced general metacognitive capacity is predictive of the development and persistence of overall negative symptom burden. However, there have been no published analyses investigating how specific sub-components of metacognition influence the expression of individual negative symptoms. We aggregated individual participant data from studies reporting measures of subtypes of metacognitive functioning and examined the strength of association with specific negative symptoms. Methods PsycINFO, EMBASE, MEDLINE, Cochrane Library and grey literature databases were searched for eligible studies. Forwards and backwards citation searching and contacting of study authors revealed additional datasets not identified in the original search. Included studies assessed negative symptoms and metacognition using interview-based measures in participants aged 16 years or older. Selection was restricted to quantitative research, excluding case studies, and only English language publications were screened. Experimental and observational studies were screened sequentially at title, abstract and full-text level to determine whether they met search criteria. A second reviewer independently screened a proportion of records to check the reliability of inclusion/exclusion judgements (Cohen’s Kappa = 0.74). Participant data and metadata of included studies were extracted and compiled combining original author and report information for all pre-specified outcomes where available. The proposed plan for the systematic review and meta-analyses was also pre-registered on PROPSERO (CRD42019130678). Results 97 unique reports were identified, of which 30 included negative symptom specific hypotheses. Samples overlapped substantially across publications with these 97 reports corresponding to 30 unique datasets. The raw individual participant level data for 23 of the 30 unique datasets was obtained. Preliminary analyses investigated the relationship between components of metacognition measured with the MAS-A (Lysaker et al., 2005), and the original negative symptoms subscale score of the PANSS (Kay et al., 1987) to maximise available data. We will discuss the results, which suggest that there are distinct relationships between subscales of metacognition and negative symptoms. We will also discuss the limitations of these results including a limited scope for analysing covariates due to the computational complexity of the models used, and difficulties in handling the diversity of data present in the meta-analysis. We will also discuss why high heterogeneity might be present, and provide further support for analysis investigating the relationship between individual negative symptoms and components of metacognition. Discussion The data suggest there is complexity in the relationship between components of metacognition and individual negative symptoms. It is for subsequent analyses to determine whether individual negative symptoms have distinct relationships with each metacognitive capacity, and whether the variation in the strength of these associations could explain the high heterogeneity observed.
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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 | 0.049 | 0.126 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".