Veterinary Student Knowledge, Attitudes, and Perspectives on Differing Viewpoints Regarding Pain Management After a Role-Play Case Study on Piglet Castration
Bibliographic record
Abstract
Case studies can be valuable tools in veterinary curricula to help students develop critical thinking skills. Little research has investigated how cases can affect student attitudes on complex animal welfare issues. The objectives were to determine if a role-play case on piglet castration would affect veterinary students’ (a) ability and confidence in recalling scientific concepts regarding identification and management of pain in animals; (b) attitudes toward pigs, animal pain, and pain management; and (c) self-reflection on different viewpoints on pain and pain management in animals. First-year veterinary students enrolled in a core animal welfare course participated in a 4-week piglet castration case that included group assignments and role-playing. Students completed pre- and post-case quizzes and surveys ( n = 128), as well as a written reflection after the case ( n = 133). The effects of the case were assessed using quantitative (paired t-tests and Cochran's Q tests for quiz and survey responses) and qualitative (thematic analysis of written reflections) analyses. Students scored 8.6% higher on their post-case quiz and had slightly more positive attitudes toward pigs and the practicality of providing pain management for pigs after the case. Qualitative analysis of the written reflections revealed four main themes, including student preconceptions about pain in animals, flexibility or resistance to change their views on pain management, challenges associated with navigating different perspectives, and key takeaways from the case. Results indicate that role-play cases may help veterinary students learn about, and reflect on, complex animal welfare issues such as pain management.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".