“I Had No Idea That Other People in the World Thought Differently to Me”: Ethical Challenges in Small Animal Veterinary Practice and Implications for Ethics Support and Education
Bibliographic record
Abstract
Although veterinarians encounter ethical challenges in their everyday practice, few studies have examined how they make sense of and respond to them. This research used semi-structured interviews and a qualitative methodology (phenomenological and constructivist/interpretivist approaches) to explore ethical challenges experienced by seven small animal city veterinarians and their ethical decision-making strategies. Thematic analysis of the interview transcripts identified four broad ethical issues: The first concerned disagreements about the best interests of the animal; the second centered on clinical uncertainty about the most appropriate treatment for the animal; the third involved factors influencing ethical reasoning and decision making; and the fourth concerned how ethics education might prepare veterinary students for future ethical decision making. An overarching theme identified in the analysis was one of enormous personal distress. Furthermore, a sense of veterinarians being interested in how others might think and feel about ethical challenges came through in the data. The results give insight into how veterinarians experience and respond to ethical challenges. The research also provides empirical information about everyday practice to inform future education in ethics and ethical decision making for veterinary students.
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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 | 0.040 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.041 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.012 |
| 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".