The effects of provincial bicycle helmet legislation on helmet use and bicycle ridership in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Bicycle helmet legislation has been variably implemented in six of 10 Canadian provinces. The objectives of this study were to determine the association between the comprehensiveness of helmet legislation and both helmet use and bicycle ridership. METHODS: Analysis of helmet use was based on data from the 2005 Canadian Community Health Survey (CCHS) and included respondents from three Canadian provinces (Saskatchewan, Ontario, and Nova Scotia). Analysis of bicycle use was based on data from the 2000-01, 2003, 2005, and 2007 cycles of the CCHS and included respondents from all provinces. In the time between the 2000-01 and 2007 cycles, two provinces (Prince Edward Island (PEI) and Alberta) implemented helmet legislation. RESULTS: Helmets were reportedly worn by 73.2% (95% CI 69.3% to 77.0%) of respondents in Nova Scotia, where legislation applies to all ages, by 40.6% (95% CI 39.2% to 42.0%) of respondents in Ontario, where legislation applies to those less than 18 years of age, and by 26.9% (95% CI 23.9% to 29.9%) of respondents in Saskatchewan, where no legislation exists. Though legislation applied to youth in both Ontario and Nova Scotia, helmet use was lower among youth in Ontario than among youth in Nova Scotia (46.7% (95% CI 44.1% to 49.4%) vs 77.5% (95% CI 70.9% to 84.1%)). Following the implementation of legislation in PEI and Alberta, recreational and commuting bicycle use remained unchanged among youth and adults. CONCLUSIONS: Canadian youth and adults are significantly more likely to wear helmets as the comprehensiveness of helmet legislation increases. Helmet legislation is not associated with changes in ridership.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it