Enhancing Veterinary Education Through a Novel Animal Welfare and Behavior Course at a New Veterinary University
Bibliographic record
Abstract
Proficiency in animal welfare is among the core competencies of graduating veterinary students. With growing societal concern surrounding welfare topics, it is imperative that veterinarians are knowledgeable advocates for animals’ welfare. However, animal welfare has not been consistently integrated into veterinary school curricula; some studies suggest that existing courses may not have achieved their intended outcomes. This study aimed to evaluate incoming veterinary students’ perceptions regarding animal welfare before and after completing a newly developed first-semester course in animal welfare and behavior. Perceptions were assessed through an anonymous, voluntary questionnaire containing 40 individual statements gauging students’ concern for animal welfare. A “total welfare concern” (TWC) score, indicative of predilection toward animal welfare, was calculated for each student based on responses collected before (PRE), after (POST), and 2.5 years after (LAST) course completion. A total of 105 students completed the PRE questionnaire, 81 completed the POST, and 59 completed the LAST. The Wilcoxon signed-rank test for matched pairs was used to compare median TWC scores between matched PRE, POST, and LAST data points. Results showed that the median TWC score increased from PRE to POST ( n = 69, p < .001) and from PRE to LAST ( n = 32; p < .001), with no significant difference between POST and LAST ( n = 32; p = .64). These findings suggest that students’ attitudes toward animal welfare and empathy toward animals increased after the course and remained elevated throughout their education. This novel first-semester course appears to have provided students with a foundation and evaluative framework for continued attentiveness to animal welfare.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".