Comparing children’s driving abilities in physical and virtual environments
Bibliographic record
Abstract
PURPOSE: To compare children's driving abilities in a physical and virtual environment and to validate the McGill Immersive Wheelchair Simulator (MiWe-C) for the use of children with disabilities. MATERIALS AND METHODS: Participants included 30 children (17 males, 13 females; mean age 14 y 1 mo, [SD 3 y 6 mo]; range: 5-18 y) with cerebral palsy, neuromuscular disease and spinal cord injury. All children were proficient drivers with more than 3 months' experience, who had their own powered wheelchairs. Participants drove a 15-minute physical route and high-fidelity simulation of that route in a counterbalanced order. Performance of the two routes was compared using the 32 item Powered Mobility Programme (PMP). Differences between the driving modes were analyzed with the non-parametric Wilcoxon signed-rank test. Significance was set at α = 0.05. RESULTS: = .09). Five out of the 32 PMP tasks showed significant differences between driving modes (narrow corridors, crowded corridors, doorway, sidewalks), with higher scores for the physical driving mode. CONCLUSIONS: Having a validated powered mobility simulator for children provides a viable option for an additional practice mode. The MiWe-C simulator is affordable and a user-friendly simulator that can be used anywhere including at home and in school. Children can be independent when practicing even if they are not yet proficient drivers since continual adult assistance is not needed.Implications for rehabilitationHaving a validated powered mobility simulator for children provides a viable option for an additional practice mode.The MiWe-C is now validated to be used with children 5-18 years with physical disabilities.The MiWe-C is one of the few options for children to practice outside of a research environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".