Effectiveness of Virtual Reality for Teaching Pedestrian Safety
Bibliographic record
Abstract
Sixty percent to 70% of pedestrian injuries in children under the age of 10 years are the result of the child either improperly crossing intersections or dashing out in the street between intersections. The purpose of this injury prevention research study was to evaluate a desktop virtual reality (VR) program that was designed to educate and train children to safely cross intersections. Specifically, the objectives were to determine whether children can learn pedestrian safety skills while working in a virtual environment and whether pedestrian safety learning in VR transfers to real world behavior. Following focus groups with a number of key experts, a virtual city with eight interactive intersections was developed. Ninety-five children participated in a community trial from two schools (urban and suburban). Approximately half were assigned to a control group who received an unrelated VR program, and half received the pedestrian safety VR intervention. Children were identified by group and grade by colored tags on their backpacks, and actual street crossing behavior of all children was observed 1 week before and 1 week after the interventions. There was a significant change in performance after three trials with the VR intervention. Children learned safe street crossing within the virtual environment. Learning, identified as improved street-crossing behavior, transferred to real world behavior in the suburban school children but not in the urban school. The results are discussed in relation to possibilities for future VR interventions for injury prevention.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".