Human Injuries Associated with the Transport of Horses by Road
Bibliographic record
Abstract
There is an increased understanding of shared human-animal risk in terms of "one welfare", whereby when animals are at risk, so are people, so preventing injury to one species may also prevent injury to the other. Because transport-related injuries to horses are common, the authors considered this paradigm to study road equine transport-related injuries to humans in New Zealand. The aim was to determine their frequency and associated factors by distributing a survey to horse industry participants through industry organisations asking about their horse activities, road transport experiences, and any related self-injury. There were 112/1067 (10.5%) handlers injured while preparing (13/112), loading (39/112), traveling (6/112), or unloading (33/112). Of these, 40% had multiple injury types, and 33% had several body regions affected. Hand injuries were most common (46%), followed by the foot (25%), arm (17%), and head or face (15%). Median recovery time was 7 days. Injuries were associated with the responder's industry educational background, years of driving experience, and reporting a horse injured during road transport in the past two years. Wearing helmets and gloves, and adopting strategies designed to eliminate equine injuries associated with the road transport of horses to reduce the risk of injury to their handlers are recommended.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".