A Critical Review of Measures of Mentalization from Peter Fonagy’s Conceptual Framework
Notice bibliographique
Résumé
Background: Mentalization is an expansive and multifaceted construct with important implications for psychological research as well as the etiology and treatment of psychological disorders. It is defined as the process through which an individual infers their own and others’ mental states are intentional and lead to meaningful actions (Bateman & Fonagy, 2004). The theoretical framework in which mentalization is situated has evolved over the years and is widely accepted however, the empirical work surrounding the measurement and operationalization of mentalization is not as well developed and merits further investigation. Methods: The authors examined the psychometric soundness and construct validity of the gold standard, interview-based measure of mentalizing as well as five self-report scales. To assess the convergent validity of these measures, Canadian university students (N = 247) completed three self-report measures of mentalization as well as one task-based tool, the Movie for the Assessment of Social Cognition (MASC; Dziobek et al., 2006). To investigate whether self-report measures predict performance on the MASC, twenty linear regressions were estimated. Exploratory factor analysis was conducted to identify the common latent factors underlying all five self-report measures at the subscale level. Results: Certain self-report measures were strongly linked and common content included items about emotion recognition and regulation, understanding one’s motivations for their behaviors and making accurate inferences about others’ thoughts. Other measures were weakly correlated and dissimilar in item content. All self-report measures were weakly correlated with the MASC. Most of the regression models were non-significant. Of the four models that emerged as significant and had significant direct effects, a classical suppression effect was observed, which merits replication. Exploratory factor analysis revealed a one factor solution fit the subscale level data well. Conclusions: This study provides preliminary evidence that there is some convergence among self-report measures. Researchers should reflect on their choice of instrument and should not use different tools interchangeably. The lack of convergence between task-based and self-report measures is disconcerting and warrants further research in this area. Last, whether the latent construct of mentalization is indeed unidimensional or has a more complex factor structure is yet to be determined.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,025 | 0,000 |
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 tête enseignante, 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 ».