Assessing Veterinary Students’ Ethical Sensitivity to Farm Animal Welfare Issues
Bibliographic record
Abstract
Ethical sensitivity has been identified as one of the four necessary components of moral action, yet little has been done to assess ethical sensitivity to animal issues in animal-related professions. The aim of this study was to develop assessment tools to measure and enhance ethical sensitivity to animal issues, and determine relationships between ethical sensitivity and moral reasoning. Of a cohort of 115 third-year veterinary students from the University of Queensland, Australia, 104 students gave permission to use their responses to written ethical sensitivity and moral judgment tests, and 51 to use their video role-plays to demonstrate ethical sensitivity to current animal farming issues. Inter-rater reliability of scoring by an expert panel was moderate to substantial for the written assessment, but only slight to moderate for the video response. In the written test, students' mean scores for recognition of animals' emotions, expression of empathy and recognition of alternative actions and their impacts improved after teaching. Scores did not increase for identification of their own emotions, moral conflicts between stakeholders, and conflicts between legal, organizational and ethical responsibilities as a professional. There was no overall relationship between ethical sensitivity and moral reasoning scores. However, high scores for reasoning using universal principles were correlated with high scores for recognition of moral conflicts between stakeholders and between legal, organizational, and ethical responsibilities as a professional. Further development of these ethical sensitivity assessment tools is encouraged to enable veterinary and animal science students to raise and address animal ethics issues and alleviate moral distress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".