Prevalence of bicycle helmet use among elementary school students in four Canadian cities
Bibliographic record
Abstract
Introduction Bicycle-related injuries are an important cause of hospitalisation among Canadian children. There is variation in the nature and enforcement of bicycle helmet laws in Canada. The Canadian Injury Indicators Development Team was established to define and evaluate key indicators, including helmet use and bicycle helmet laws. Purpose To assess the prevalence of helmet use in elementary school aged children in four Canadian cities representing varied geographic and socioeconomic settings. Methods Observations were made at schools in Halifax, Toronto, Barrie and Vancouver. Schools were identified using school board websites and DMTI, a company that partners with universities to disseminate spatial data. Schools were classified into quintiles based on the after-tax income of their census tract (2006 census). Trained observers attended each site at the beginning of the school day in May/June 2009. Information on the sex, helmet use, correct helmet use and group riding were documented. Results 397 observations were made at 91 schools. The helmet use rate was 83% across the four cities. 68% of those riding bicycles were males and 47% of children rode in a group. Group influence on helmet use varied by province. The influence of socioeconomic status (SES) on helmet use also varied. Vancouver's poorest neighbourhoods had the most riders (42%), whereas Toronto had the most of riders in the richest neighbourhoods (39%). Conclusion There is variation in helmet use across Canadian cities generally, and by SES specifically. Helmet use rates were higher in the medium sized city (Barrie) compared to the other, larger cities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".