Swine Medicine Education: A Survey of North American and Caribbean Veterinary Colleges Curricula
Bibliographic record
Abstract
Numerous demands on the Doctor of Veterinary Medicine training program have the potential to reduce the amount of time allocated to food animal species in general, including swine medicine, despite it being a key component of veterinary education. The objective of this study was to describe swine medicine training opportunities at North American and Caribbean veterinary education institutions. A 21-question survey was developed and distributed to veterinary colleges across North America and the Caribbean. The survey was available from October 2021 to March 2022, and one response was accepted per institution. Seventy-four percent of contacted institutions completed the survey, representing 29 veterinary colleges located in the United States, Canada, or the Caribbean. Responses were aggregated, analyzed, and grouped by topic: institution opportunities, curriculum opportunities, clinical opportunities, and faculty involvement in the swine medicine curricula. There was substantial variation among institutions in the delivery and resources allocated to swine medicine specific curricula. Swine veterinarians help ensure the health and well-being of animals and food safety. More research is required to evaluate the outcomes of the currently available opportunities. Concurrently, veterinary education institutions should prevent the attrition of swine educational programs by investing in the support and development of swine opportunities for students.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".