Can Teaching Veterinary and Animal-Science Students about Animal Welfare Affect Their Attitude toward Animals and Human-Related Empathy?
Bibliographic record
Abstract
Attitudes toward animals are important in influencing how animals are treated. Few studies have investigated attitudes toward animals in veterinary or animal-science students, and no studies have compared attitudes to animals before and after a course teaching animal welfare and ethics. In this study, students enrolled in veterinary (first-year) or animal-science (first- and third-year) programs completed a questionnaire on attitudes toward different categories of animals before and after the course. Higher attitude scores suggest a person more concerned about how an animal is treated. Normally distributed data were compared using parametric statistics, and non-normally distributed data were compared using non-parametric tests, with significance p < .05. Attitudes toward pets (45.5-47.6) were higher than those toward pests (34.2-38.4) or profit animals (30.3-32.1). Attitude scores increased from before to after the course in the veterinary cohort on the Pest (36.9 vs. 38.4, respectively, n = 27, p < .05) and Profit (30.3 vs. 32.1, respectively, n = 28, p < .05) subscales, but not in the animal-science cohorts. Attitude scores in all categories were higher for women than for men. Currently having an animal was associated with higher pet scores (46.8 vs. 43.8, ns = 120 and 13, respectively, p < .05), and having an animal as a child was associated with higher profit scores (31.0 vs. 26.6, ns = 129 and 8, respectively, p < .05). Students electing to work with livestock had lower scores on the Pest and Profit subscales, and students wanting to work with wildlife had significantly higher scores on the Pest and Profit subscales. This study demonstrates attitudinal changes after an animal-welfare course, with significant increases in veterinary but not animal-science students.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".