The relationship between child maltreatment subtypes and components of emotional competence in emerging adults
Notice bibliographique
Résumé
Child maltreatment is a pervasive public health issue that affects one in four children each year on a global scale. While not everyone who experiences child maltreatment has poor mental or physical health outcomes, many do suffer from emotional difficulties such as difficulty with emotion regulation and emotion recognition. Though child maltreatment is often studied as a single cumulative category, it comprises of different subtypes including physical and sexual abuse, emotional maltreatment, physical neglect, and exposure to domestic violence. Each of these subtypes have been linked with emotion regulation and emotion recognition problems, however, maltreatment subtypes frequently co-occur. Due to this overlap, when different maltreatment subtypes are not taken into consideration, effects may be overestimated or misattributed. There is currently a dearth of literature that examines the differential effects of childhood maltreatment subtypes on emotion regulation dimensions and the recognition of specific emotions. Additionally, while both emotion regulation and emotion recognition are core components of emotional competence, they are conceptualized to be distinct domains. Despite this, child maltreatment has been robustly associated with both emotion regulation and emotion recognition deficits suggesting that they may be related processes. Currently, without a theoretical basis on the relationship between different components of emotional competence, the relationship between emotion regulation and emotion recognition is unclear. As such, this dissertation aims to take a fine-grained approach to better understand the differential effects of child maltreatment subtypes on emotion regulation dimensions and the recognition of specific emotions in Study 1. Study 2 aims to empirically assess the relationship between emotion regulation and emotion recognition by examining the moderating role of emotion regulation in the relationship between child maltreatment and emotion recognition. Considering how emerging adulthood (18 – 25 years) is a developmental period where psychopathology often emerges, there is an impetus to better understand how child maltreatment impacts emotional functioning in this understudied population within child maltreatment research. A sample of 573 emerging adults were recruited across Canada to complete an online survey that asked about child maltreatment history, difficulty with emotion regulation, and involved an emotion recognition task. Path analyses in Study 1 indicated that emotional maltreatment had a global effect on emotion regulation difficulties and the recognition of negatively valanced emotions (anger, fear, and sadness). Neglect predicted difficulties with managing impulsive behaviour; sexual abuse predicted difficulties engaging in goal-directed behaviour. Physical abuse was associated with poorer recognition of fear. Multigroup analysis revealed that patterns did not differ between clinically distressed and non-distressed participants. In Study 2, moderation analysis revealed that child maltreatment was associated with poorer recognition of negatively valanced emotions, but only in the context of poor emotion regulation, however, exploratory analyses examining differential patterns revealed more nuanced relationships. While most maltreatment subtypes significantly interacted with emotion regulation (impulse control difficulties and limited access to emotion regulation strategies), the moderation was only significant for the recognition of disgust. Together, the results from both studies provide insight into the significant impact of emotional maltreatment on both emotion regulation and emotion recognition and how these patterns change when emotion regulation is examined as a moderator. These findings highlight the presence of common and disparate elements between child maltreatment subtypes which provide a basis for more targeted approaches to intervention for survivors of child maltreatment
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».