Unhelmeted Injured Cyclists in a Canadian Emergency Department: Cycling Behavior and Attitudes Towards Helmet Use
Bibliographic record
Abstract
INTRODUCTION: We seek to characterize unhelmeted injured cyclists presenting to the emergency department: demographics, cycling behavior, and attitudes towards cycling safety and helmet use. METHODS: This was a prospective case series in a downtown teaching hospital. Injured cyclists presenting to the emergency department were recruited for a standardized survey if not wearing a helmet at time of injury and over age 18. Exclusion criteria included inability to consent (language barrier, cognitive impairment) or admission to hospital. RESULTS: We surveyed 72 UICs (unhelmeted injured cyclists) with mean age of 34.3 years (range 18-68, median 30, IQR 15.8 years). Most UICs cycled daily or most days per week in non-winter months (88.9%, n = 64). Most regarded cycling in Toronto as somewhat dangerous (44.4%, n = 32) or very dangerous (5.9%, n = 4). Almost all (98.6%, n = 71) had planned to cycle when departing home that day. UICs reported rarely (11.1%, n = 8) or never (65.3%, n = 47) wearing a helmet. Reported factors discouraging helmet use included inconvenience (31.9%, n = 23) and lack of ownership (33.3%, n = 24), but few characterized helmets as unnecessary (11.1%, n = 7) or ineffective (1.4%, n = 1). CONCLUSIONS: Unhelmeted injured cyclists were frequent commuter cyclists who generally do not regard cycling as safe yet choose not to wear helmets for reasons largely related to convenience and comfort. Initiatives to increase helmet use should address these perceived barriers, and further explore cyclist perception regarding risk of injury and death.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".