Self-reported quad bike use, safety behaviors, and safety awareness among young adults in U.S. and Canadian agriculture
Bibliographic record
Abstract
The number of agricultural fatalities and injuries related to agricultural quad bike use has risen substantially in the last two decades. Safe engineering design features such as crush protection and roll bars have proven potential to lessen the burden of injury but have traditionally not been included in many quad bike safety training programs. The aim of this study was to survey more than 700 young adults working in U.S. and Canadian agriculture to examine self-reported quad bike safety behaviors and awareness of quad bike safety design engineering features. We found that U.S. males continue to be at higher risk for quad bike-rollover incidents when compared to other groups. Even when accounting for other factors such as age and country, we found that participants who reported youth occupational quad bike use (≤14 years old) were up to 200% more likely to allow extra riders and up to 489% more likely to not wear a helmet when compared to participants who reported beginning occupational quad bike use in adulthood. These findings support the Agricultural Youth Work Guideline (AYWG) for occupational quad bike use at age 16. Less than 20% of young adults working in agriculture were aware of safe design features such as wide frames, stability ratings, crush protection devices, and accessories made by the original equipment manufacturer. There is tremendous need to educate the future agricultural workforce about the importance of choosing quad bikes with safer design features.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".