M71. THE INFLUENCE OF METACOGNITIVE CAPACITIES ON SPECIFIC NEGATIVE SYMPTOMS: A SYSTEMATIC REVIEW AND INDIVIDUAL PARTICIPANT META-ANALYSIS OF INTERVIEW-BASED DATA
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,049 | 0,126 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,018 | 0,037 |
| Bibliométrie | 0,014 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».