A Pilot Randomized Controlled Trial Testing the Effectiveness of a Pedestrian Training Program That Teaches Children Where and How to Cross the Street Safely
Bibliographic record
Abstract
Objective: Pedestrian injury is a leading cause of injury-related mortality for children. This pilot randomized controlled trial tested the efficacy of a training program to teach where and how to cross safely. Methods: Using fully immersive virtual reality technology, 142 children 7-10 years of age were recruited, with 130 completing crossing measures before (pretest) and immediately after (posttest) training. Training comprised 1.5 hr, was tailored to each child's performance over trials, and focused on either where to cross (n = 44 children completed testing) or how to cross safely (n = 43); corresponding control groups comprised 22 and 21 children, respectively. Following training, children in the intervention groups completed additional tasks to test conceptual knowledge and generalization of learning. Children in the control groups spent the same time as those in training groups but played a video game that used the same game controller but provided no training in street crossing. Results: The primary outcomes were errors in crossing at posttest, controlling for pretest error scores. Children in the intervention group made from 75% to 98% fewer errors at posttest than control children for all pedestrian safety variables related to where and how to cross safely, with effect sizes (incidence rate ratios) varying between 0.02 and 0.25. They also showed a generalization of what they had learned and applied this knowledge to novel posttraining situations. Conclusion: Training within a virtual pedestrian environment can successfully improve children's conceptual understanding and crossing behaviors for both where and how to cross streets safely.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".