Educators' Perspectives on Animal Welfare and Ethics in the Australian and New Zealand Veterinary Curricula
Bibliographic record
Abstract
The current study was designed to explore the importance that veterinary science educators in Australian and New Zealand universities assign to animal welfare and ethics (AWE) topics as Day One/Initial Competences for new graduates. An online questionnaire was deployed in parallel with an equivalent study of veterinary science students at these educators' schools. Responses were received from 142 educators (51% females n=72 and 49% males n=70), representing an overall participation rate of 25%. Questions were clustered according to seven areas of veterinary employment: general practice, production animals, companion animals, wild animals, aquatic animals, animals kept for scientific purposes, and animals used in sport and recreation. The most highly rated topics for each of these clusters were: professional ethics in general practice, euthanasia in companion animals, strategies to address painful husbandry procedures in production animals, veterinarians' duties to wild animals in animals in the wild, aquatic animal health and welfare issues in aquatic animals; competence in the 3Rs (replacement, refinement and reduction) in animals kept for scientific purposes, and responsibilities of ownership in sport and recreation. Female educators rated many of the topics as significantly more important than did their male counterparts. Educators teaching one or more ethics-related subjects were less likely to rate neutering and euthanasia as important as those not teaching these subjects. The educators' focus on practical issues clashes with a perceived need for veterinarians to actively embrace animal ethics. Overall, the perspectives of these educators should be carefully considered as they are likely to influence student attitudes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".