Public Practice Opportunities for Veterinary Students to Enhance Veterinary Public Health Education
Bibliographic record
Abstract
Veterinarians have a long history of contributing to animal and human health; simultaneously, the veterinary medical profession has held the tenet of protecting public health. Veterinary education has shifted with societal needs over time and currently has curricula at US colleges of veterinary medicine (CVMs) largely focused on clinical practice and basic sciences. The focus of many veterinary curricula produces a veterinarian who meets the needs of the US pet owner. A void often exists in the knowledge and understanding of new veterinary graduates in the field of public practice and, in particular, public health. Students need to be able to find other learning environments and opportunities that help bridge this void. This article captures possible opportunities as best practices. Advising US veterinary students interested in public health and public health policy while considering these opportunities will help to enhance the likely experiences students have during their formal veterinary education. While no list of opportunities can be inclusive of all possibilities, the experiences listed here provide a solid foundation of options for students to include in the individualized aspects of their veterinary education.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".