Veterinary Public Health in a Problem-Based Learning Curriculum at the Western University of Health Sciences
Bibliographic record
Abstract
As detailed in the Association of Schools of Public Health / Association of American Veterinary Medical Colleges 2007 Joint Symposium on Veterinary Public Health, veterinary public health (VPH) can no longer be viewed as a unique sub-specialty of veterinary medicine. Rather, its practice pervades nearly every aspect of the veterinary profession, regardless of whether the practitioner is engaged in small-animal, large-animal, research, corporate, or military practice. In congruence with the practice of VPH, the teaching of VPH should also pervade nearly every aspect of veterinary education. Accordingly, at Western University of Health Sciences, College of Veterinary Medicine (WU-CVM), public health is not simply taught as an individual course but, rather, is interwoven into almost every aspect of the curriculum, continually emphasizing the relevance of this discipline to the practice of veterinary medicine. This article outlines the teaching philosophy of WU-CVM, provides an overview of the curriculum, and describes the integral nature of public health throughout all four years of the educational program.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".