One Health–One Education: Medical and Veterinary Inter-Professional Training
Bibliographic record
Abstract
Physicians and veterinarians are increasingly expected to collaborate across disciplines; however, in most cases their education and training remain isolated within their respective professions. Medical and veterinary students are rarely provided with opportunities for inter-professional learning during their coursework and clinical training. One Health serves as an ideal framework for developing problem-focused curricula that promote inter-professional teamwork. One Health issues (e.g., zoonotic diseases, water pollution, toxic waste, impact of climate change, and food safety and security) not only engage students across disciplines, but require faculty and senior leadership across various health-related fields to share knowledge and balance perspectives throughout curriculum development and implementation. In this article, we report on one of several interactive, small-group, case-based One Health curricular exercises developed collaboratively by students and faculty in our Schools of Medicine and Veterinary Medicine to ensure that all students, regardless of background or intended specialty, would receive a basic introduction to inter-professional collaboration in the context of a One Health clinical problem of the sort they might encounter in their future practice. Toxoplasmosis ( Toxoplasma gondii infection) was selected as the first case because of the potentially different perspectives that medical and veterinary practitioners may have on advising a pregnant woman with regard to risk factors, prevention, testing, and treatment. Our goal was to develop an evidence-based approach to this clinical case that could be used by both professions to assess environmental and zoonotic risk factors for T. gondii in human pregnancies.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".