Mandatory helmet legislation and children's exposure to cycling
Bibliographic record
Abstract
BACKGROUND: Mandatory helmet legislation for cyclists is the subject of much debate. Opponents of helmet legislation suggest that making riders wear helmets will reduce ridership, thus having a negative overall impact on health. Mandatory bicycle helmet legislation for children was introduced in Ontario, Canada in October 1995. The objective of our study was to examine trends in children's cycling rates before and after helmet legislation in one health district. SETTING: Child cyclists were observed at 111 preselected sites (schools, parks, residential streets, and major intersections) in the late spring and summer of 1993-97 and in 1999, in a defined urban community. PARTICIPANTS: Trained observers counted the number of child cyclists. The number of children observed in each area was divided by the number of observation hours, resulting in the calculation of cyclists per hour. MAIN OUTCOME MEASURE: A general linear model, using Tukey's method, compared the mean number of cyclists per hour for each year, and for each type of site. RESULTS: Although the number of child cyclists per hour was significantly different in different years, these differences could not be attributed to legislation. In 1996, the year after legislation came into effect, average cycling levels were higher (6.84 cyclists per hour) than in 1995, the year before legislation (4.33 cyclists per hour). CONCLUSION: Contrary to the findings in Australia, the introduction of helmet legislation did not have a significant negative impact on child cycling in this community.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".