Identifying Veterinary Students' Capacity for Moral Behavior Concerning Animal Ethics Issues
Bibliographic record
Abstract
Veterinarians face unique animal ethics challenges as practitioners and policy advisors to government and industry. Changing societal attitudes, cultural diversity, and the often conflicting needs and interests of patients and clients contribute to moral distress. Yet little has been done to identify veterinarians' capacity to address these animal ethics issues. In this study, first-year and final-year veterinary students in an Australian university were surveyed to explore moral sensitivity, moral motivation, and moral character and their relationship with moral reasoning. The majority of students were concerned about animal ethics issues and had experienced moral distress in relation to the treatment of animals. Most believed that veterinarians should address the wider social issues of animal protection and that veterinary medicine should require a commitment to animals' interests over owners'/caregivers' interests. There was less agreement that the veterinary profession was sufficiently involved in addressing animal ethics issues. The principal motivators for studying veterinary medicine were, in declining importance, enjoyment in working with animals, helping sick and injured animals, and improving the way animals are treated. However, most students had taken little or no action to address animal ethics issues. These results suggest that both first- and fifth-year veterinary students are sensitive to animal ethics issues and are motivated to prioritize the interests of animals but have little experience in taking action to address these issues. Further research is needed to determine ways to identify and assess these moral behavior components in veterinary education to develop veterinarians' capacity to address animal ethics issues.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".