Evaluating Veterinary Ethics Education Programs in South Korea from the Learners’ Perspective
Bibliographic record
Abstract
Ethical competence, the ability to recognize and respond to the ethical dimensions embedded in everyday decision making, is vital for veterinarians’ professional integrity and to ensure the public's trust. This study investigated current veterinary ethics education in South Korea, focusing on curriculum quality and its perceived outcomes. An online survey was conducted with a total of 374 respondents, 192 veterinarians and 182 students (3.7% response rate), to assess their educational experience and self-assessed competencies in veterinary ethics. Although 60% of them had received ethics education, primarily through formal courses, over half of them reported dissatisfaction with instructor expertise and course effectiveness. Only 22% of those surveyed believed that ethics was integrated sufficiently into their curriculum. They expressed a lack of confidence in applying ethical tools and legal knowledge, highlighting the need for required courses, competent instructors, and the integration of ethics with related subjects such as animal welfare, bioethics, veterinary law, and professionalism. Despite these concerns, individuals who completed the ethics coursework reported significantly higher levels of self-assessed ethical competence compared to those who did not receive such training (3.31 vs. 2.96, p < .001), which underscores the necessity and effectiveness of integrating ethics education into veterinary curricula. These results demonstrate the significance of sustained efforts to strengthen ethics education across all stages of veterinary training.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".