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 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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".