Development of a Moral Judgment Measure for Veterinary Education
Bibliographic record
Abstract
Veterinarians increasingly face animal ethics issues, conflicts, and dilemmas, both in practice and in policy, such as the tension between clients' and animals' interests. Little has been done to measure the capacity of veterinarians to make ethical judgments to prevent and address these issues or to identify the effectiveness of strategies to build this capacity. The objectives of this study were, first, to develop a test to identify the capacity of veterinarians to make ethical decisions in relation to animal ethics issues and, second, to assess students' perceptions of the usefulness of three methods for the development of ethical decision making. The Veterinary Defining Issues Test (VetDIT) was piloted with 88 first-year veterinary students at an Australian university. The veterinary students were at a variety of reasoning stages in their use of the Personal Interest (PI), Maintaining Norms (MN), and Universal Principles (UP) reasoning methods in relation to both human ethics and animal ethics issues and operated at a higher level of reasoning for animal than human ethics. Thirty-eight students assessed three methods for developing ethical decision-making skills and identified these as being helpful in clarifying their positions, clarifying others' positions, increasing awareness of the complexity of making ethical decisions, using ethical frameworks and principles, and improving moral reasoning skills, with two methods identified as most helpful. These methods and the VetDIT have the potential to be used as tools for development and assessment of moral judgment in veterinary education to address animal ethics issues.
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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 | 0.025 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".