Case study assessing the feasibility of using a wearable haptic device or humanoid robot to facilitate transitions in occupational therapy sessions for children with autism spectrum disorder
Bibliographic record
Abstract
INTRODUCTION: Some children with autism spectrum disorder (ASD) have difficulties with transitions that may lead to problem behaviours. Although the use of technologies with children with ASD is receiving increasing attention, no study has looked at their effect on transitions in activities of daily living. This study aimed to document the feasibility of (1) using two intervention technologies (NAO humanoid robot or wearable haptic device) separately to facilitate transitions in occupational therapy sessions for children with ASD and (2) the method used to document changes. METHODS: Using a single case reversal (ABA) design, two children with ASD were randomly assigned to one of the intervention technologies (humanoid robot or haptic bracelet). Each technology was used as an antecedent to stimulate the start of transitions in eight intervention sessions at a private occupational therapy clinic. Data concerning the time required for transitions, child's behaviours during transitions at the clinic and mother's perception of the child's performance in transitions at home were analysed graphically. RESULTS: When using technology, both children's behaviours were appropriate, quick and relatively stable. Also, both mothers reported improved perceptions of their child's performance in transitions. CONCLUSIONS: This exploratory study suggests no detrimental effect of using these technologies.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".