One Health as a Core Component of Veterinary Medicine: Defining Day-1 Public Health Competencies for the Veterinary Workforce
Bibliographic record
Abstract
Animal health and veterinary medicine are integral to One Health, contributing important perspectives on complex challenges arising at the human-animal-environment interface. The published Competency-Based Veterinary Education (CBVE) framework dedicates a domain of competence and three associated sub-competencies to public health (Domain 4). However, a panel of One Health scientists sought to establish additional outcomes believed necessary to support core veterinary curricula related to veterinary public health (VPH)/One Health. We hypothesized that early career veterinarians use knowledge, skills, and abilities consistent with VPH/One Health and that the existing CBVE could incorporate these concepts. We conducted key informant and exploratory interviews with veterinarians across 12 sectors of veterinary medicine and used inductive coding to identify VPH/One Health codes. We then cross-analyzed these codes with the existing CBVE framework to incorporate field-validated VPH/One Health codes into the published framework. Thirty codes emerged which were designated as either adequately represented (5), not represented (6), or represented with sub-competency creation or augmentation recommended (19) in the existing framework. This information was used to cross-map, validate, and update the CBVE sub-competencies so that they accurately reflect the breadth and depth of One Health engagement required for competent veterinarians. This iterative, evidence-based revision process is a model for integrating One Health into medical professional curricula.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".