An exploratory study of children’s pretend play when using a switch-controlled assistive robot to manipulate toys
Bibliographic record
Abstract
Introduction Assistive robots could be a means for children with physical disabilities to manipulate toys and for occupational therapists to track children’s play development. This study aimed to (a) establish if free play set-ups without and with a robot would elicit a developmental sequence of play in typically developing children, (b) determine if the robot affected children’s play and (c) observe the play schemes that children performed. Method An experimental crossover design was conducted. Thirty typically developing children between the ages of 3 and 8 years old performed free play activities with conventional toys or unstructured materials without and with a switch-controlled Lego Mindstorms robot. Children’s pretend and functional play was analyzed using a coding scheme developed for the present study. Results There was a trend, increasing with age, for pretend play without the robot with unstructured materials ( p = .002), and with the robot, for conventional toys ( p = 0.015) and unstructured materials ( p = 0.027). Younger children exhibited more pretend play without the robot than with it. Conclusion Assistive robots and appropriate play set-ups can provide a method to measure the play development level of children with disabilities, and support pretend play. Suggestions to support pretend play when children with disabilities use assistive robots are discussed.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".