Off‐Road Vehicle Ridership and Associated Helmet Use in Canadian Youth: An Equity Analysis
Bibliographic record
Abstract
PURPOSE: In North America, the use of off-road vehicles by young people is increasing, as are related injuries and fatalities. We examined the prevalence of off-road ridership and off-road helmet use in different subgroups of Canadian youth in order to better understand possible inequities associated with these health risk behaviors. METHODS: Data came from Cycle 6 (2009-2010) of the WHO Health Behavior in School-Aged Children Study (HBSC). Participants (n = 26,078) were young people from grades 6-10 in 436 Canadian schools. Students were asked, for a 12-mo recall period, how frequently they rode off-road vehicles and how often they wore a helmet while riding. Engagement in off-road ridership and helmet use were estimated by age group, gender, urban-rural geographic location, socioeconomic status, and how long participants had lived in Canada. FINDINGS: About half of the sample reported riding off-road vehicles (12,750; 52%). Among riders, 5,691 (45%) always wore helmets. Riders were more often older students, male and born in Canada. Students in rural areas and small towns were much more likely to ride off-road vehicles than their urban peers (RR, 95% CI: 1.28 [1.23-1.33]). Helmet use was less common among females, new immigrants, older students, and those in lower socioeconomic groups. There was little reported difference in helmet use by urban-rural location. CONCLUSIONS: Risks associated with the use of off-road vehicles and with nonhelmet use are not equitably distributed across Canadian youth. Factors characterizing off-road ridership (notably urban-rural location) are distinct from factors for helmet use. Preventive interventions should target population subgroups.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".