Preparing Students for Careers in Food-Supply Veterinary Medicine: A Review of Educational Programs in the United States
Bibliographic record
Abstract
The real and/or perceived shortage of veterinarians serving food-supply veterinary medicine has been a topic of considerable discussion for decades. Regardless of this debate, there are issues still facing colleges of veterinary medicine (CVMs) about the best process of educating future food-supply veterinarians. Over the past several years, there have been increasing concerns by some that the needs of food-supply veterinary medicine have not adequately been met through veterinary educational institutions. The food-supply veterinary medical curriculum offered by individual CVMs varies depending on individual curricular design, available resident animal population, available food-animal caseload, faculty, and individual teaching efforts of faculty. All of the institutional members of the Association of American Veterinary Medical Colleges (AAVMC) were requested to share their Food Animal Veterinary Career Incentives Programs. The AAVMC asked all member institutions what incentives they used to attract and educate students interested in, or possibly considering, a career in food-supply veterinary medicine (FSVM). The problem arises as to how we continue to educate veterinary students with ever shrinking budgets and how to recruit and retain faculty with expertise to address the needs of society. Several CVMs use innovative training initiatives to help build successful FSVM programs. This article focuses on dairy, beef, and swine food-animal education and does not characterize colleges' educational efforts in poultry and aquaculture. This review highlights the individual strategies used by the CVMs in the United States.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".