Adoption of municipal bylaw legislating mandatory helmet use for cyclists under the age of 18: impact on cycling and helmet use
Bibliographic record
Abstract
INTRODUCTION: Bicycle helmet use is recognized as an effective way to prevent head injuries in cyclists. A number of countries have introduced legislation to make helmets mandatory, but many object to this type of measure for fear that it could discourage people, particularly teenagers, from cycling. In 2011, the City of Sherbrooke adopted a bylaw requiring minors to wear a bicycle helmet. The objective of this study was to assess the impact of this bylaw on cycling and bicycle helmet use. METHODS: The impact of the bylaw was measured by comparing the evolution of bicycle helmet use among youth aged 12 to 17 years in the Sherbrooke area (n = 248) and in three control regions (n = 767), through the use of logistic regression analyses. RESULTS: Cycling rates remained stable in the Sherbrooke area (going from 49.9% to 53.8%) but decreased in the control regions (going from 59.1% to 46.3%). This difference in evolution shows that cycling rates increased in the Sherbrooke area after the adoption of the bylaw, compared to the control regions (odds ratio [OR] of the interaction term: 2.32; 95% confidence interval [CI]: 1.01-5.35). With respect to helmet use, a non-statistically significant upward trend was observed in the Sherbrooke area (going from 43.5% to 60.6%). This figure remained stable in the control regions (going from 41.5% to 41.9%). No significant difference was observed in the evolution of helmet use between the two groups (OR of the interaction term of 2.70; 95% CI: 0.67-10.83). CONCLUSION: After the bylaw was adopted, bicycle use among youth aged 12 to 17 years in the Sherbrooke area remained stable and helmet used increased, though not significantly.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".