Factors associated with incorrect bicycle helmet use
Bibliographic record
Abstract
BACKGROUND: Incorrect bicycle helmet use increases head injury risk. OBJECTIVE: To evaluate the patterns of incorrect helmet use based on unobtrusive field observations. METHODS: Two observational surveys conducted in Alberta in 2000 and 2006 captured information on cyclist characteristics, including correct helmet use. Prevalence of correct helmet use was compared across multiple factors: age, gender, riding companionship, and environmental factors such as riding location, neighbourhood median family income, and region. Poisson regression analysis was used to relate predictor variables to the prevalence of incorrect helmet use, adjusting for clustering by site of observation. RESULTS: Among helmeted cyclists (n=5862), 15.3% were wearing their helmet incorrectly or were using a non-bicycle helmet. Children (53%) and adults (51%) tended to wear their helmet too far back, while adolescents tended not have their straps fastened (48%). Incorrect helmet use declined approximately 50% over the study period for children and adolescents, but 76% (95% CI 68% to 82%) in adults. Children were 1.8 times more likely to use their helmets incorrectly in 2000 compared with adults, but this effect increased to 3.9 (95% CI 2.9 to 5.4) in 2006. Adolescents were more likely to use their helmets incorrectly in 2006 compared with adults (prevalence ratio 2.76; 95% CI 1.9 to 4.02). Children and adolescents cycling alone, compared with adults cycling alone, cycling at non-school sites and cycling in Edmonton, was associated with incorrect helmet use. CONCLUSIONS: Important factors not previously identified were associated with incorrect bicycle helmet use. This information can be used to target interventions to increase correct use.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".