Examining Why Ethics Is Taught to Veterinary Students: A Qualitative Study of Veterinary Educators' Perspectives
Bibliographic record
Abstract
Although it is widely agreed that veterinary students need to be introduced to ethics, there is limited empirical research investigating the reasons why veterinary ethics is being taught. This study presents the first extensive investigation into the reasons for teaching veterinary ethics and reports data collected in semi-structured interviews with educators involved in teaching undergraduate veterinary ethics at three European schools: the University of Copenhagen, the University of Nottingham, and the Technical University of Lisbon (curricular year 2010-2011). The content of the interview transcripts were analyzed using Toulmin's argumentative model. Ten objectives in teaching veterinary ethics were identified, which can be grouped into four overarching themes: ethical awareness, ethical knowledge, ethical skills, and individual and professional qualities. These objectives include recognizing values and ethical viewpoints, identifying norms and regulations, developing skills of communication and decision making, and contributing to a professional identity. Whereas many of the objectives complement each other, there is tension between the view that ethics teaching should promote knowledge of professional rules and the view that ethics teaching should emphasize critical reasoning skills. The wide range of objectives and the possible tensions between them highlight the challenges faced by educators as they attempt to prioritize among these goals of ethics teaching within a crowded 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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".