Trends in child pedestrian motor vehicle collision injury rates by neighborhood deprivation score in Toronto, Canada
Bibliographic record
Abstract
We examined trends from 2000 to 2019 in child pedestrian motor vehicle collision (PMVC) injury rates in Toronto, Canada, to see if injury trends varied by neighbourhood deprivation. This 20-year period was associated with major road safety policy changes in the City. A Poisson regression analysis examined police-reported data on children (age 1-19 years), killed or seriously injured (KSI) PMVC rates, by deprivation status (using the Ontario Marginalization Index), over the period 2000-2019. Models controlled for location (urban core v. inner suburbs) and evaluated potential interactions. There were 523 child pedestrian KSI collisions from 2000 to 2019. Over this period, KSI rates decreased by more than 50 % across all neighbourhood deprivation levels. Steep declines from 2000 to 2010 were followed by level or increasing child PMVC rates from 2010 to 2019. Higher deprivation was associated with slightly elevated KSI rates; although not statistically significant. It is important to learn from road safety policy "successes" and ensure that future road safety interventions are applied equitably across areas, accounting for deprivation and location.
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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 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".