Producing Interdisciplinary Competent Professionals: Integrating One Health Core Competencies into the Veterinary Curriculum at the University of Rwanda
Bibliographic record
Abstract
Infectious diseases of grave concern to human health are emerging from wildlife and livestock populations in multiple regions of the world. Responding effectively to these emerging pandemics requires engagement of multidisciplinary groups of professionals. Using a One Health approach, One Health Central and Eastern Africa (OHCEA), a network of seven schools of public health and seven veterinary schools, with the support of the United States Agency for International Development (USAID), has engaged in curriculum review with the aim of building the skills of multidisciplinary groups of professionals to improve their capacity to respond to emerging infectious diseases. Through stakeholder analysis and curriculum development workshops, the University of Rwanda's School of Veterinary Medicine, in association with Tufts University, revised its curriculum to incorporate One Health competencies to be better prepared to respond to any infectious disease outbreak in Africa. The revised curriculum aimed to build cross-sectoral skills and knowledge; transform students' ways of thinking about infectious disease outbreak response; link human, veterinary, and wildlife health training opportunities; and strengthen community frontline responder training. Eight different disciplines engaged in the curriculum review process: Veterinary Medicine, Livestock Production, Wildlife and Aquatic Resources, Environmental Health and Epidemiology, Communication Technology, Engineering, Agriculture, and Public Health. One Health competencies such as communication, collaboration, leadership, and advocacy were added to the new curriculum, helping ensure that each professional be appropriately equipped with skills to recognize and respond effectively to any emerging infections.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".