Understanding Sex Differences in Children’s Injury Risk as Pedestrians
Bibliographic record
Abstract
OBJECTIVE: Boys experience more injuries as pedestrians than girls. The aim of this study was to compare how boys and girls cross streets in order to identify factors that differentially influence their injury risk as pedestrians. METHODS: Using a fully immersive virtual reality (VR) system interfaced with a 3D movement measurement system, various measures of children's street-crossing behaviors were taken. RESULTS: At the start of the crossing, boys selected smaller (riskier) inter-vehicle gaps to cross into than girls. Subsequently, as they crossed, they showed greater attention to traffic, shorter start delay, and more evasive action than girls, which are strategies that could reduce risk as a pedestrian. Despite these efforts, however, boys experienced more hits and close calls than girls. CONCLUSION: To enhance their safety as pedestrians, girls adopt a proactive approach and select larger inter-vehicle gaps to cross into, whereas boys apply a reactive approach aimed at managing the risk created by having selected smaller (riskier) gaps. Girls' proactive approach yielded safer outcomes than boys' reactive strategy.
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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 | 0.003 |
| 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) | 0.007 | 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".