102 Trends in child pedestrian collision injuries by neighbourhood deprivation in Toronto, Canada
Bibliographic record
Abstract
Background Pedestrian motor vehicle collisions are a leading cause of death and disability among Canadian children (0–19 years). The objective of this study was to examine trends in child pedestrian motor vehicle collision injury rates by neighbourhood deprivation in Toronto, Canada. Methods Police-reported child pedestrian injuries (killed or seriously injured; KSI) from 2000–2019 were mapped onto 140 neighbourhoods in Toronto. Neighbourhood deprivation tertiles (low, medium, and highly deprived) were designated using the 2016 Ontario Marginalization Index. Poisson regression analyses examined KSI rates by deprivation and five-year time interval, controlling for location (urban core versus inner suburbs). Interaction terms (deprivation/location and time interval) were also estimated. Results Between 2000–2019, 523 child pedestrian KSI were reported. Injury rates were inversely associated with deprivation. A decrease in KSI rates (> 50%) was seen across all neighbourhood deprivation tertiles. The steepest decline in KSI rates occurred from 2000–2010. In the multivariate models, deprivation and interaction terms were non-significant. Toronto’s urban core showed higher child KSI rates, and a significantly faster decline in rates, compared with the outer suburbs. Conclusions Toronto child pedestrian KSI rates declined steeply from 2000–2019. Declines were observed uniformly across deprivation tertiles, and steepest in the urban core. Decreases in child pedestrian KSI rates may be attributed to traffic policies implemented in the early 2000s, e.g., city-wide speed limit reductions. Learning Outcomes Child pedestrian motor vehicle collision KSI rates have declined steeply over the last two decades in Toronto. Declines were consistent across deprivation tertiles, and steepest in the urban core.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".