Traffic-related pedestrian injuries in school-age children: examining the relationship with characteristics of the roadway environment
Bibliographic record
Abstract
Traffic-related injuries are a leading cause of morbidity and mortality amongst children in Canada. Characteristics of the road environment, including the volume of motor vehicle traffic, have been found to be important determinants of injury occurrence in adults. Studies of the relationship in children, however, are few in number.I undertook an ecological study among children age 5 to 17 years on the Island of Montreal using a database of all traffic-related pedestrian injuries occurring in the period 1999 to 2008. These data on accidents were juxtaposed with census data and characteristics of the road network. I used Poisson regression to model the number of traffic-related pedestrian injuries occurring at intersections and on city street segments and characteristics of the roadway and of neighbourhoods. The number of injuries was positively associated with the average volume of traffic at the location of injury, with the presence of major roads, and with the presence of more than three legs at intersections. Intersections and road segments located in closer proximity to a school, as well as those in neighbourhoods with a higher population density of children, lower household income, and a higher proportion of children walking or biking to school had higher relative number of injuries. Sensitivity analyses, including the use of different statistical models for count data, confirmed the robustness of the main results. These findings suggest that the design of streets and intersections may be an important target for strategies to reduce the number of injuries in school-aged children.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".