The Young Movers Project: A Case Series Describing Modified Toy Car Use as an Early Movement Option for Young Children With Mobility Limitations
Bibliographic record
Abstract
Modified toy cars for have gained popularity as a tool for early exposure to power mobility. Aims: to (1) determine modifications required, (2) describe frequency of home and community use, (3) describe assistance and encouragement provided, child’s motivation and enjoyment of the car, and (4) explore therapist and parent experiences with the cars. Methods: This mixed-methods case series included children aged 13–58 months (n = 5) with cerebral palsy (n = 4) and arthrogryposis and hypotonia (n = 1). Four children received cars and follow-up visits from therapists in their homes. Quantitative data were collected using a family driving record. Qualitative interviews were conducted with parents (n = 5) and therapists (n = 2). The data management strategy described by Knafl (1988) facilitated qualitative data analysis. Results: One child could not be adequately supported; she did not receive a car. Driving frequency ranged from 1.3 to 2.9 days per week, 12–63 min in duration. Qualitative analysis resulted in four themes: (1) A gentle introduction to power mobility, (2) It’s more than just mobility, (3) You just need to try it, and (4) Cars are simple tools. Conclusions: Modified toy cars are feasible for early exposure to power mobility with young children with physical disabilities who do not require extensive seating modifications.
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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 | 0.005 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".