An Interdisciplinary Approach to One Health: Course Design, Development, and Delivery
Bibliographic record
Abstract
One Health is an approach to studying health by recognizing the interconnections between people, animals, plants, and their shared environment. This article describes the process of designing a new course on One Health at the University of Illinois at Urbana-Champaign (UIUC). We brought together faculty and students from across campus to develop a multidisciplinary course dedicated to One Health and infectious diseases. This group met over 9 months to brainstorm course goals, objectives, and ideas. The group also organized a workshop to explore One Health's existing knowledge and ongoing work on the UIUC campus. We solicited the help of experts throughout the university to co-teach the course. The course curriculum and course materials included 13 unique case studies. The course was offered in fall 2019, and its goals were to add to the existing training and coursework on One Health at the University of Illinois campus, offer a course that would be suitable for students from all fields of study, and develop helpful case studies to be made available to other educators. Student feedback highlights the course's successes as well as areas for future improvement. This article describes this entire process of course development, provides recommendations to guide improvements in the next offering of the course, and details our contributions to the field of One Health 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 | 0.019 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".