Helmet use in BIXI cyclists in Toronto, Canada: an observational study
Bibliographic record
Abstract
OBJECTIVE: To investigate the use of helmets for cyclists choosing to use BIXI bikes in comparison to personal bike riders in the City of Toronto. DESIGN: Cross-sectional study design. SETTING: Cyclists were observed in Toronto, Canada. PARTICIPANTS: Of the 6732 sample size, 306 cyclists on BIXI bikes and 6426 personal bike riders were observed. OUTCOME MEASURE: The outcome of interest was helmet use. RESULTS: Overall, 50.3% of cyclists wore helmets. The proportion of BIXI bike riders using helmets was significantly lower than the proportion of helmet users on personal bikes (20.9% vs 51.7%, respectively, p<0.0001). CONCLUSIONS: Although the BIXI bike programme has provided an alternate means for Torontonians to use a bicycle, cyclists using BIXI bikes are much less likely to wear a helmet. Since the prevalence of helmet use in cyclists in general is already low, helmet use should be especially promoted in BIXI bike riders in order to promote a safe and healthy environment for cyclists.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".