52: A Safe Community-Based Campaign to Promote the use of Bicycle Helmets in Children
Bibliographic record
Abstract
Bicycle-related head injuries are an important cause of death and disability, despite the availability of helmets. It has long been recognized that individual education aimed at changing behavior is not enough to effectively prevent injury. Programs need to address environmental factors that are some of the largest determinants of health status. Actively involving the community in the planning and implementation of activities fosters enduring change and program. To determine the association between bicycle helmet legislation, wide-scale community promotional effort and the use of bicycle helmets by children. Design: Over two years, a Safe Community committee coordinated a range of activities including municipal helmet legislation, school based intervention, parent information, social media campaign, peer educators, free helmet distribution and police enforcement to promote helmet use. SETTING: Cyclists were observed by two independent observers in July 2011 (postcampaign) in Sherbrooke, Canada. The data were compared with a similar survey completed at the same locations and days in July 2006, 2008 (precampaign). Data were collected for 319 cyclists in 2006, 212 in 2008 and 183 cyclists in 2011. Helmet use increased from 57% in 2006, to 43% on 2008 to 88% in 2011 for children cyclist (estimated five to nine years old). The greatest increase was observed among older children and young teens (10 to 15 years of age) (38% in 2006 and 32% in 2008 to 92% in 2011). Helmet used also increased in older teens and young adults (estimated 16 to 24 years of age) also increased (12% in 2006, 28% in 2008, 57% in 2011). Wide-scale, long-term community promotion appears to be effective in increasing the use of bicycle helmets
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".