Factors associated with bicycle helmet use among young adolescents in a multinational sample
Bibliographic record
Abstract
OBJECTIVE: To determine factors associated with variation in bicycle helmet use by youth of different industrialized countries. DESIGN: A multinational cross sectional nationally representative survey of health behaviors including symptoms, risk taking, school setting, and family context. SETTING: School based survey of 26 countries. SUBJECTS: School students, ages 11, 13, and 15 years totaling 112,843. OUTCOME MEASURES: Reported frequency of bicycle helmet use among bicycle riders. RESULTS: Reported helmet use varied greatly by country from 39.2% to 1.9%, with 12 countries reporting less than 10% of the bicycle riders as frequent helmet users and 14 countries more than 10%. Reported helmet use was highest at 11 years and decreased as children's age increased. Use was positively associated with other healthy behaviors, with parental involvement, and with per capita gross domestic product of the country. It is negatively associated with risk taking behaviors. Countries reported to have interventions promoting helmet use, exemplified by helmet giveaway programmes, had greater frequency of reported helmet use than those without programmes. CONCLUSIONS: Bicycle helmet use among young adolescents varies greatly between countries; however, helmet use does not reach 50% in any country. Age is the most significant individual factor associated with helmet for helmet using countries. The observation that some helmet promotion programmes are reported for countries with relatively higher student helmet use and no programmes reported for the lowest helmet use countries, suggests the possibility of a relation and the need for objective evaluation of programme effectiveness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".