Veterinary Students’ Perspectives on Their Relationship with Animals Used in Veterinary Education
Bibliographic record
Abstract
Abstract Some veterinary colleges still use live animals to teach veterinary students clinical skills. However, there is a lack of literature understanding veterinary students’ perspectives on the use of animals in their education. Using a qualitative approach, this study aimed to explore (a) how the perceived quality of life of animals used for teaching affected veterinary students’ learning and emotional well-being and (b) how student emotional well-being affected the care they provided for animals used for teaching. We conducted 10 focus groups and 2 interviews with fourth-year veterinary students ( n = 43) participating in clinical rotations at two Canadian veterinary colleges. We analyzed the data inductively using template thematic analysis and identified three themes. First, using animals in students’ education caused ethical and moral conflicts that had a negative impact on their emotional well-being; many of these conflicts arose when there was a lack of transparency about animal use or when the harm to animals outweighed the benefits to student learning or animal welfare. Second, students’ well-being and the welfare of animals used for teaching were intertwined, suggesting that educational practices have implications for both. Third, supervisors and their teaching approaches, including the use of live animals or alternatives, had complex effects on student learning. In general, students preferred realistic and supportive learning experiences in which they could provide a service to animals and their community. This study highlights the nuanced perspectives of veterinary students on the animals used in their education. We encourage veterinary colleges to carefully evaluate their use of institution-owned live animals within the curriculum, improve transparency about how these animals are used, and find effective pedagogical methods that consider implications to both student emotional well-being and animal welfare.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".