Global One Health Case Competition: Building Capacity for Addressing Infectious Threats
Bibliographic record
Abstract
Extracurricular inter-professional activities advance pre-service student skills and confidence before joining the workforce. This article describes an extracurricular model, whereby students engaged in experiential learning, and had the opportunity to challenge themselves in interprofessional groups guided by faculty and inspired by professionals in their respective fields. The Global One Health Case Competition involved students from the University of Rwanda in collaboration with the University of Minnesota, and required students in teams to address an Ebola outbreak containment and response scenario. Forty students, seven faculty coaches, and five judges participated in this event. Students gained collaborative teamwork skills as they developed comprehensive strategies for managing a response to a zoonotic disease outbreak, considering political, financial, logistical, and other factors. Faculty strengthened skills in writing complex case studies for a competition model, and in mentoring of multidisciplinary student groups. Case competition is an effective educational mechanism for building the outbreak response capacity of our future workforce before they are in their real-world professional roles responding to actual zoonotic and other infectious disease threats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.029 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".