Effects of service dogs on children with ASD’s symptoms and parents’ well-being: On the importance of considering those effects with a more systemic perspective
Bibliographic record
Abstract
The integration of a service dog can have numerous benefits for children with Autism Spectrum Disorder (ASD). However, although integration takes place within a family, little is known about the dynamics of these benefits on the family microsystem. Thus, the aim of our study was to propose a more systemic perspective, not only by investigating the benefits of SD integration, but also by exploring the relationships between improvements in children with ASD, parents' well-being, parenting strategies and the quality of the child-dog relationship. Twenty parent-child with ASD dyads were followed before, as well as 3 and 6 months after service dog integration. At each stage, parents completed an online survey which included: the Autism Behavior Inventory (ABI-S), the State-Trait Anxiety Inventory (STAI-Y), the Parenting Stress Index Short Version (PSI-SF), the Monash Dog Owner Relationship Scale (MDORS) and the Parenting Styles and Dimensions Questionnaire (PSDQ). First, repeated measure one-way ANOVAs revealed that both children's ASD symptoms and parents' anxiety decreased significantly after service dog integration. Additionally, Spearman correlations revealed that the more ASD symptoms decreased, the more parent's anxiety and parenting stress also decreased. Second, the quality of the child-dog relationship appeared to contribute to those benefits on both children's ASD symptoms and parents' well-being. Interestingly, parenting strategies seemed to adapt according to these benefits and to the quality of the child-dog relationship. Through a more systemic perspective, this study highlighted that the integration of a service dog involved reciprocal and dynamic effects for children with ASD and their parents, and shed new light on the processes that may underlie the effects of a service dog 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".