Do school crossing guards make crossing roads safer? A quasi-experimental study of pedestrian-motor vehicle collisions in Toronto, Canada
Bibliographic record
Abstract
BACKGROUND: The presence of school crossing guards has been associated with more walking and more pedestrian-motor vehicle collisions (PMVCs) in area-level cross-sectional analyses. The objectives of the study were to (1) Determine the effect on PMVC rates of newly implemented crossing guards in Toronto, Canada (2) Determine where collisions were located in relation to crossing guards throughout the city, and whether they occurred during school travel times. METHODS: School crossing guards with 50 m buffers were mapped along with police-reported child PMVCs from 2000-2011. (1) A quasi-experimental study identified all age collision counts near newly implemented guards before and after implementation, modeled using repeated measures Poisson regression adjusted for season and built environment variables. (2) A retrospective cohort study of all child PMVCS throughout the city to determine the proportions of child PMVCs which occurred during school travel times and at guard locations. RESULTS: There were 27,827 PMVCs, with 260 PMVCs at the locations of 58 newly implemented guards. Repeated measures adjusted Poisson regression found PMVCs rates remained unchanged at guard locations after implementation (IRR 1.02, 95 % CI 0.74, 1.39). There were 568 guards citywide with 1850 child PMVCs that occurred at guard locations. The majority of child PMVCs occurred outside school travel times (n = 1155, 62 %) and of those that occurred during school travel times, only 95 (13.7 %) were at a guard location. CONCLUSIONS: School crossing guards are a simple roadway modification to increase walking to school without apparent detrimental safety effects. Other more permanent interventions are necessary to address the frequency of child PMVCs occurring away from the location of crossing guards, and outside of school travel times.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".