Factors affecting Canadian veterinarians' use of analgesics when dehorning beef and dairy calves.
Bibliographic record
Abstract
Data collected through a national, randomized mail survey (response rate 50%) were used to identify reasons why veterinarians were likely (i) to use analgesic drugs when dehorning calves, and (ii) to perceive dehorning without analgesia as very painful. Logistic regression analysis indicated that veterinarians were more likely to be analgesic users the more they perceived that dehorning without analgesia was painful (OR = 1.7, P < 0.001). Other positive influences were if the veterinarian worked in British Columbia or Alberta (OR = 5.9, P = 0.005), and if they were primarily in dairy practice (OR = 3.7, P = 0.012) rather than beef practice. This effect of dairy practice was negated if the veterinarian also perceived that owners were unwilling to pay for analgesia (interaction term: OR = 0.25, P = 0.038). Veterinarians were also less likely to perceive dehorning without analgesia as very painful if they perceived that owners were unwilling to pay (OR = 0.58, P = 0.029). However, this effect on pain perception was offset by concern for personal safety (OR = 2.7, P = 0.015). The results are consistent with the relatively high level of outreach about animal welfare among farmers and veterinarians in the western provinces. The results confirm that many veterinarians' approach to pain management for dehorning is influenced considerably by concern about cost. However, pain management for dehorning is not expensive and there is unequivocal evidence that dehorning calves without pain management causes significant distress. Continuing education of veterinarians should help to increase analgesic usage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".