Comparing pedestrian and cyclist injuries from falls and collisions in British Columbia, Canada: Frequencies and population characteristics
Bibliographic record
Abstract
Walking and cycling offer health benefits but carry injury risks. Traditional road safety datasets often exclude pedestrian and cyclist falls, despite emerging evidence that injuries from falls occur more frequently than collisions with motor vehicles. This research compared the frequency of pedestrian and cyclist injuries from falls versus collisions using hospital admissions data from a linked database of road traffic injuries in British Columbia, Canada, which combined hospital admissions, and sociodemographic information from 2015 to 2019. Additionally, we examined differences in injury severity and population characteristics between those injured in falls versus collisions. Of 6807 pedestrian hospital admissions, 68.8 % were from falls—2.3 times higher than motor vehicle collisions (29.2 %). Among 2409 cyclist admissions, falls accounted for 48.6 %–1.8 times higher than motor vehicle collisions (27.6 %). More severe injuries (MAIS3+) occurred less frequently in falls (25.0 % pedestrians, 17.9 % cyclists) than in collisions with motor vehicles (39.7 %, 27.4 %). We also found that falls disproportionately happen to older adults, females, higher-income individuals, and rural residents with more pronounced differences in pedestrians. Our analysis revealed that pedestrian and cyclist falls are major contributors to the burden of road traffic injury and emphasizes the need for their inclusion in road safety surveillance and research. Reliance on datasets that exclude falls, or failing to consider falls as a road safety issue, can potentially hinder the development of infrastructure and built environment design solutions aimed at reducing the frequency and severity of fall injuries to pedestrians and cyclists. • We counted pedestrian and cyclist injuries from falls compared to collisions. • Pedestrian falls were 2.3 times more frequent than collisions with motor vehicles. • Cyclist falls were 1.8 times more frequent than collisions with motor vehicles. • Older adults, women, higher-income, and rural residents fall more often. • Road traffic injury definitions should include falls for surveillance and research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".