PW 1341 A systematic review of the impact of bicycle helmet legislation on cycling exposure
Bibliographic record
Abstract
Background Cycling is a physical activity with many health and environmental benefits. There are inherent risks while cycling and bicycle helmets have been proposed as a means to mitigate head injury along with crash avoidance strategies such as separated cycling infrastructure. Twenty-seven countries around the world have enacted bicycle helmet legislation (BHL) to increase helmet usage among cyclists. Critics of BHL often claim legislation deters cycling uptake. Methods Five electronic databases (MEDLINE, EMBASE, COMPENDEX, SCOPUS, WEB OF SCIENCE) were searched to identify relevant studies. Two authors independently assessed records retrieved in adherence to the PRISMA statement. The included studies reported data on cycling exposure pre- and post-legislation. Results 22 studies with data from 6 countries covering 16 jurisdictions were identified from the peer-reviewed and grey literature. The methods used to measure cycling include direct observation at fixed locations, self-reported surveys, hospital data, police-reported crashes and movement counters. Most studies had a single pre-legislation observation making it impossible to estimate existing trends. Although BHL exists in 27 countries, our systematic review identified studies from Australia, Canada, New Zealand, Spain, Sweden and the United States. Conclusions In our preliminary results, most studies found no or conflicting evidence of reductions in cycling following BHL. In the few studies reporting reductions in cycling following BHL, these could be due to existing trends or a general shift from active transport modes to personal motor vehicle travel. Due to the lack of data across most jurisdictions with BHL, caution should be exercised when interpreting these results.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".