HELMET USE IN BIXI CYCLISTS IN TORONTO, CANADA: AN OBSERVATIONAL STUDY
Bibliographic record
Abstract
Background In May 2011, the BIXI bikesharing programme was launched in the downtown core of the City of Toronto, Canada, providing public access to bicycle rentals for short-commutes within the city. 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. Methods A prospective cohort study design was used to compare the prevalence of helmet use for male and female cyclists on personal bikes in comparison to those on BIXI bikes during the weekday morning and evening commutes over the summer, 2011. Results Of the 6732 sample size, 306 cyclists on BIXI bikes and 6426 personal bike riders were observed. 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). Significance/Contribution to the Field 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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".