317 Survey of veterinary student attitudes toward animal welfare and pain
Bibliographic record
Abstract
Abstract Discrepancies in the background and training of veterinarians regarding the painfulness of procedures across species may impact their decision to use analgesia. The objective of this study was to investigate veterinary student attitudes toward pain and animal welfare. An electronic survey instrument was developed to assess demographic information, perceptions of animal welfare, concern with specific animal welfare issues, and estimation of pain scores on a scale of 1–10 for certain procedures and conditions. A subset of 131 responses from veterinary students were analyzed from an ongoing study involving 14 colleges of veterinary medicine in the United States and Canada. Results suggest that females believed more strongly that an animal welfare and ethics course should be part of the veterinary curriculum than males (P = 0.03). Respondent preparedness to discuss certain welfare topics differed based on background (farm/ranch, rural or urban community) (P ≤ 0.03) and year of veterinary school (P ≤ 0.02). Respondent willingness to administer pain management also differed by background (P = 0.04). Whether respondents had observed a veterinarian in practice properly administer pain medication to a food animal also differed by area of interest, background, and year in veterinary school (P ≤ 0.03). Assigned pain scores for bovine dystocia, bovine acute metritis, canine tail docking and porcine castration also differed by background (P ≤ 0.03). These data show that gender, background and the year of veterinary school should be considered when developing and standardizing the delivery of animal welfare topics across the veterinary curriculum.
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".