Mental health benefits of a robot-mediated emotional ability training for children with autism: An exploratory study.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".