New Approaches for Education and Training in Veterinary Public Health: The SAPUVET Projects
Bibliographic record
Abstract
Continued contact between humans and animals, in combination with the ever-increasing movement of human and animal populations that is one effect of globalization, contributes to the spread of diseases, often with detrimental effects on public health. This has led professionals involved in both animal health and public health to recognize veterinary public health (VPH) as a key area for their activities to address the human-animal interface. Veterinarians, a profession with major involvement in this field, are in need of specific knowledge and skills to prevent and control public-health problems. As a result, VPH must be directly integrated into veterinary educational programs. At present, only few veterinary schools have specific VPH programs; in most institutions, VPH does not feature as a specific subject in either undergraduate or post-graduate curricula. SAPUVET and SAPUVETNET II are network projects supported by the ALFA program of the European Union (EU). Their main objectives are to reach a common understanding between European and Latin American universities in the definition of the areas in which VPH is important in their respective countries, and to design a harmonized training program for veterinarians in VPH, by making use of new technological applications and innovative teaching methodologies. The elaboration of educational material in combination with case studies presenting real-life problems provides a basis to apply the knowledge acquired on VPH. It is envisaged that the material and modules developed during the two projects will be integrated into the veterinary curricula of the participating universities, as well as in other partner organizations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".