The impact of pedestrian countdown signals on pedestrian–motor vehicle collisions: a quasi-experimental study
Bibliographic record
Abstract
OBJECTIVE: To determine whether pedestrian countdown signals (PCS) reduce pedestrian-motor vehicle collisions in the city of Toronto, Canada. METHODS: A quasi-experimental study design was used to evaluate the effect of PCS on the number of pedestrian-motor vehicle collisions in the city of Toronto, from January 2000 to December 2009. Each intersection acted as its own control. We compared the number of pedestrian-motor vehicle collisions per intersection-month before and after the intervention. Stratified models were used to evaluate effect modification by pedestrian age, injury severity and location (urban vs inner suburbs). Poisson regression analysis with repeated measures (generalised estimating equations) was used to estimate the RR and 95% CI. RESULTS: The analysis included 9262 pedestrian-motor vehicle collisions at 1965 intersections. The RR of collisions after PCS installation was 1.014 (95% CI 0.958 to 1.073), indicating no statistically significant effect of PCS on collisions. There was no evidence to suggest effect modification between PCS and collisions by age, injury severity or location. CONCLUSION: The installation of PCS at 1965 signalised intersections in Toronto did not reduce the number of pedestrian-motor vehicle collisions at these intersections.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".