Assessing Veterinary and Animal Science Students' Moral Judgment Development on Animal Ethics Issues
Bibliographic record
Abstract
Little has been done to assess veterinarians' moral judgment in relation to animal ethics issues. Following development of the VetDIT, a new moral judgment measure for animal ethics issues, this study aimed to refine and further validate the VetDIT, and to identify effects of teaching interventions on moral judgment and changes in moral judgment over time. VetDIT-V1 was refined into VetDIT-V2, and V3 was developed as a post-intervention test to prevent repetition. To test these versions for comparability, veterinary and animal science students (n=271) were randomly assigned to complete different versions. The VetDIT discriminates between stages of moral judgment, condensed into three schemas: Personal Interest (PI), Maintaining Norms (MN), and Universal Principles (UP). There were no differences in the scores for MN and UP between the versions, and we equated PI scores to account for differences between versions. Veterinary science students (n=130) who completed a three-hour small-group workshop on moral development theory and ethical decision making increased their use of UP in moral reasoning, whereas students (n=271) who received similar information in a 50-minute lecture did not. A longitudinal comparison of matched first- and third-year students (n=39) revealed no moral judgment development toward greater use of UP. The VetDIT is therefore useful for assessing moral judgment of animal and human ethics issues in veterinary and other animal-related professions. Intensive small-group workshops using moral development knowledge and skills, rather than lectures, are conducive to developing veterinary students' moral judgment.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".