Veterinary Student Knowledge and Attitudes about Swine Change after Lectures and a Farm Visit
Bibliographic record
Abstract
Veterinary schools are facing the challenge of increasing animal welfare (AW) training while also attracting future practitioners to livestock medicine. Both objectives may be better achieved through farm visits early in veterinary training. First year veterinary students at the University of Minnesota (n = 103) were surveyed during the Spring 2019 Professional Development II course to document their knowledge, attitudes, and values relative to pigs, AW, and the industry before and after classroom and online lectures and a visit to a farrow-to-wean farm. Quantitative (Kruskal-Wallis, Kendall tau-c and Chi-Square) and qualitative (content analysis) analyses were used to identify shifts in knowledge and attitudes and associations with demographics and use of the AW values of biological functioning, affective state, and natural living. Most students were female (85.4%), from urban/suburban backgrounds (68.9%), and did not wish to work with livestock (66.0%). Knowledge scores (p <.05) and attitudes toward pigs (p = .0152) improved after visiting the farm. Satisfaction with AW on most commercial farms shifted after the farm visit (p = .0003), with those valuing biological functioning becoming more satisfied (p = .0342). In contrast, students who visited the farm when enrichment was provided were more dissatisfied compared to those who toured the farm without enrichment (p = .0490). Those referencing natural living (p = .0047) rated the toured farm as a poorer steward of welfare. Students' AW concerns included behavioral restriction in individual stalls and injury and lameness in group pens. Farm visits are an important tool in veterinary education, but may result in segmentation in student knowledge and attitudes relative to livestock 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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".