Economic disparity in bicycle helmet use by children six years after the introduction of legislation
Bibliographic record
Abstract
BACKGROUND: Studies evaluating the effectiveness of bicycle helmet legislation often focus on short term outcomes. The long term effect of helmet legislation on bicycle helmet use is unknown. OBJECTIVE: To examine bicycle helmet use by children six years after the introduction of the law, and the influence of area level family income on helmet use. METHODS: The East York (Toronto) health district (population 107,822) was divided into income areas (designated as low, mid, and high) based on census tract data from Statistics Canada. Child cyclists were observed at 111 preselected sites (schools, parks, residential streets, and major intersections) from April to October in the years 1995-1997, 1999, and 2001. The frequency of helmet use was determined by year, income area, location, and sex. Stratified analysis was used to quantify the relation between income area and helmet use, after controlling for sex and bicycling location. RESULTS: Bicycle helmet use in the study population increased from a pre-legislation level of 45% in 1995 to 68% in 1997, then decreased to 46% by 2001. Helmet use increased in all three income areas from 1995 to 1997, and remained above pre-legislation rates in high income areas (85% in 2001). In 2001, six years post-legislation, the proportion of helmeted cyclists in mid and low income areas had returned to pre-legislation levels (50% and 33%, respectively). After adjusting for sex and location, children riding in high income areas were significantly more likely to ride helmeted than children in low income areas across all years (relative risk = 3.4 (95% confidence interval, 2.7 to 4.3)). CONCLUSION: Over the long term, the effectiveness of bicycle helmet legislation varies by income area. Alternative, concurrent, or ongoing strategies may be necessary to sustain bicycle helmet use among children in mid and low income areas following legislation.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".