Student Perceptions of an Animal-Welfare and Ethics Course Taught Early in the Veterinary Curriculum
Bibliographic record
Abstract
Animal welfare and veterinary ethics are two subjects that have been acknowledged as necessary for inclusion in the veterinary curriculum. In fact, the American Veterinary Medical Association (AVMA) Council on Education has mandated that veterinary ethics be taught to all students in US veterinary colleges. Animal welfare was recently included in the US veterinarian's oath, and AVMA established a committee to create a model curriculum on the subject. At US veterinary colleges, the number of animal-welfare courses has more than doubled from five in 2004 to more than 10 in 2011. How and what is taught with regard to these two subjects may be as important as whether they are taught at all, and a variety of approaches and varying amounts and types of content are currently being offered on them. At Michigan State University's College of Veterinary Medicine, students were introduced to animal welfare and veterinary ethics during their first semester in a mandatory two-credit course. To assess their perception of the course, students completed an online evaluation at the end of the semester. Most students found the course to be challenging and effective and felt that they improved their ability to identify and discuss ethical dilemmas.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".