WHAT DO BICYCLE HELMET LAWS DO? EVIDENCE FROM CANADA
Bibliographic record
Abstract
Twenty‐one states and the District of Columbia require youths to wear helmets when riding a bicycle, and there has been a push to extend such laws to adults. We provide new evidence on helmet laws by studying Canada using difference‐in‐differences models and restricted area‐identified public health survey data with information on cycling and helmet use for nearly 800,000 individuals from 1994 to 2014. We first confirm prior patterns from the United States that laws requiring youths to wear helmets significantly increased youth helmet use. We then provide the literature's first comprehensive evidence that “all‐age” bicycle helmet laws significantly increased both adult and youth helmet use by 50%–190% relative to pre‐reform levels, with larger effects for younger adults and less‐educated adults. All‐age helmet laws had modest effects at reducing cycling and increasing in‐home exercise during winter months among adults but did not meaningfully affect weight. Overall, our findings confirm that all‐age helmet laws can be effective at increasing population helmet use without significant unintended adverse health consequences. (JEL I18, I12, K32)
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.
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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".