Prevalence and associated factors of injury in bovine practitioners in the United States and Canada
Bibliographic record
Abstract
A cross-sectional online survey was administered to American Association of Bovine Practitioner members to determine the prevalence of injuries to veterinarians due to performing rectal palpation or other common work-related injuries among bovine practitioners. Basic demographic information was collected on veterinarians related to their time in practice, gender, physical attributes that may be risk factors for injury, characteristics of their practices, and geographic information. From the surveys, 1158 responses were analyzed. Seventy-seven percent of respondents experienced pain while rectally palpating cattle, of which 42% reported severe pain. Multiple locations were reported as the source of pain (80% arm/elbow, 70% shoulder). Fifty-two percent reported that pain limited performance slightly, and 7% moderately or severely. On average, pain started 12 years after beginning practice. Years in practice, herd size, and changing palpating arm due to pain were associated with experiencing pain. Surgical treatment of the practitioner was positively correlated with increasing age, a higher average number of beef herds visited daily, increased average number of palpations daily, the use of a stall for palpation, use of analgesia, and predominantly using the left hand. Pain for practitioners during bovine rectal palpation is a common occurrence. Veterinary students as well as practitioners should be educated about ways to mitigate these potential occupational hazards, and further research should be conducted on how practices can be modified to reduce these outcomes.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".