Distinguishing between primary and secondary callous-unemotional features in youth: The role of emotion regulation
Notice bibliographique
Résumé
Background: Research on youth with callous-unemotional features (CU features; e.g., lack of empathy) has historically categorized these behaviors as biologically-driven and homogeneous across development. However, an early model proposed that two subtypes of CU features exist with different etiological factors. The first, or ‘primary’ group, has a genetically based deficit in emotion processing, resulting in a diminished sensitivity to others’ emotional cues. The ‘secondary’ CU group is conceptualized as an adaptation to environmental factors such as maltreatment and are characterized by an affective deficit produced by these powerful environmental factors. Secondary youth are typically classified or grouped based on the presence of co-occurring anxiety symptoms. Understanding the presentation of regulation strategies among CU variants may give us further insight into the different pathways to their development. In addition, due to the high number of samples that have relied on justice-involved males, there is a paucity of research on gender differences across the variants. Purpose: The aim of the current study was to evaluate whether distinct groups of youth may be identified in a clinical sample by using measures of affect dysregulation and suppression, anxiety symptoms and experience of maltreatment. It was also to examine whether these distinct groups were consistent across males and females. Method: Participants (N = 418; 56.7% female) ranged in age from 12 to 19 (M = 15.04, SD = 1.85) and were drawn from the baseline of a large clinical sample. Results: A Latent Profile Analysis (LPA) was conducted using five indicators including affect regulation, suppression, anxiety, CU features, and maltreatment. The best fitting model, a 4-class solution, had a significant Lo-Mendell-Rubin (p= .003), an acceptable entropy score (.78), and classification probabilities that suggested accuracy and good separation. The four groups to emerge included a low, anxious, primary CU, and secondary CU group. Gendered LPAs found a 4-class model fit for both males and females (entropy= .866). Gender did not moderate other outcomes of interest. Discussion: This study extends previous literature by including the underlying process of dysregulated affect to the model in identifying primary and secondary subgroups and examining gender. Clinical implications are discussed.
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Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».