Bicycling injuries in children and the role of the built environment: a case-crossover study
Bibliographic record
Abstract
BACKGROUND: Bicycling has many health benefits for children but can result in injuries. The built environment is associated with the risk of bicycling injury in adults, yet less is known about risks for children. This study sought to explore associations between built environment characteristics and child bicyclist injuries in three Canadian municipalities. METHODS: A case-crossover design where injured child bicyclists provided their injury site and two control sites along their route was used. Participants were recruited from three paediatric emergency departments in Vancouver, Calgary and Toronto. We recruited 333 injured child bicyclists (ages 5-17) from May 2018 to October 2021. Participants completed interviews that captured details concerning their injury location and route. We conducted built environment audits along the child's bicycling route at the injury and two control sites. We compared the odds of injury across built environment characteristics of the injury and control sites using a mixed-effects logistic regression model. RESULTS: The median route distance to the injury location was 742 m. Most participants were injured while bicycling on local streets and on sidewalks/paths next to the road. Compared with sidewalks/paths next to the road, unpaved off-road locations (adjusted OR (aOR): 2.45; 95% CI 1.33 to 4.51) were associated with higher odds of injury. Other risk factors included locations with debris (aOR: 1.52; 95% CI 1.05 to 2.21), surfaces with bumps/holes (aOR 2.14, 95% CI 1.25 to 3.66) and construction (aOR: 2.31; 95% CI 1.37, 3.91). CONCLUSION: This study adds to evidence suggesting built environment supports are important for increasing bicycling safety for Canadian 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".