A Feasibility Randomized Trial Evaluating <i>Safe Peds</i>: A Virtual Reality Training Program to Teach Children When to Cross Streets Safely
Bibliographic record
Abstract
OBJECTIVE: Injury as pedestrians is a leading contributor to childhood deaths. This study evaluated the effectiveness of Safe Peds, a fully immersive virtual reality training program to teach children when to cross street safely, with the focus on a number of foundational skills and practicing these in traffic situations of varying complexity. METHODS: Children 7-10 years old were randomly assigned to a control (N = 31) or intervention (N = 26) group. Eligibility criteria included English speaking and typically developing. Testing took place on campus. All children completed pre- and post-testing measures, with those in the intervention group receiving training in between. Training comprised 1 session with 3 phases for a total of up to 1.5 hr and was tailored to each child's performance over trials. On each trial, children decided when to cross and fully executed this crossing, with measures automatically taken by the system as they did so. RESULTS: Negative binomial regression and analysis of covariance tests were applied, predicting post-test scores while controlling for pre-test scores, age, and sex. The intervention was effective in improving children's street crossing skills, including stopping and checking skills (stop at the curb, look left/right/left, check for traffic before crossing the yellow line), and choosing safe inter-vehicle gaps. Children in the control group did not show significant improvements in any crossing skills. CONCLUSIONS: The Safe Peds program effectively teaches children skills to support their deciding when to safely cross in a variety of traffic situations. Implications for pedestrian injury 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".