Mental health benefits of a robot-mediated emotional ability training for children with autism: An exploratory study.
Notice bibliographique
Résumé
Background: Children with Autism Spectrum Disorder (ASD) have a high prevalence of mental health problems that are linked to reduced emotional abilities. Therefore, interventions that teach emotional abilities are fundamental for their development. However, existing interventions are costly, of difficult access, or inefficient for children with ASD. Furthermore, children with ASD have a preference for sameness and routines that makes technology, and especially robots, an ideal medium to convey interventions that are suitable to their needs. Objectives: The aim of the present exploratory study is to evaluate whether a robot-mediated emotional ability training is effective in enhancing the emotional ability and the mental health of children with ASD. Methods: Using a pre-post training design, 12 children with ASD (all boys) aged between 8 and 14 years (M = 10.93; SD = 2.46) undertook a 7 week long emotional ability training mediated by a robot. Sessions took place weekly and lasted 1h each. Children were compared before (T1) and after (T2) the training on their emotional ability and their mental health. Emotional ability was measured through the parent-report measures Emotion Regulation Checklist (ERC; Shields & Cicchetti, 1997), Emotion Regulation Rating Scale (ERRS; Carlson & Wang, 2007), Self-Control Rating Scale (SCRS; Kendall & Wilcox, 1979), and the Alexithymia Questionnaire for Children (Rieffe et al., 2006); as well through a direct measure of children’s use of emotion regulation strategies using the Reactive and Regulation Situation Tasks (Carthy et al., 2010). Mental health was measured through the parent-report measures Children Behavior Checklist (CBCL; Achenbach & Rescorla, 2001), Strengths and Difficulties Questionnaire (SDQ; Goodman, 1997), and the Social Responsiveness Scale-2 (SRS-2; Constantino, 2002). Results: It was found that regarding emotional ability, children’s use of emotion regulation strategies in the Reactive and Regulation Situation Task, improved significantly after the training (t(10) = 2.81, p < .01) but no significant improvements were found on the parent-reported measures (ERC: t(10) = 0.43, p = .34; SCRS: t(10) = 1.26, p = .12), except for a marginally significant effect on children’s emotional control (ERRS: t(10) = 1.79, p = .05). Regarding mental health, the training significantly reduced internalizing problems (CBCL: t(11) = 1.91, p < .05; SDQ: t(11) = 3.19, p < .01) and autism-related symptomatology (SRS-2: t(11) = 3.24, p < .01), but did not have an effect on externalizing problems (CBCL: t(11) = 0.41, p = 34; SDQ: t(11) = 3.13, p = 07). Discussion: Overall, the results of the present study are to be interpreted cautiously, they provide restricted evidence of positive effects of the robot-mediated emotional ability training in children’s use of adaptive emotional abilities and in mental health issues such as depressive symptomatology and anxiety as well as autism-related social communication difficulties. This exploratory study contributes to the research progress in the domain of robot-mediated interventions for children with ASD.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».