Experiences and Difficulties Encountered during a Course on Veterinary Public Health with Students of Different Nationalities
Bibliographic record
Abstract
Veterinary public health (VPH) issues have received increased attention over the last few years as a result of the rising threat of emerging zoonoses (i.e., those due to globalized trade in animal and animal products and to changes in livestock production systems and the environment). The international dimension of VPH is gradually becoming recognized, and there is a growing need for veterinarians with experience in this field. In order to familiarize (future) veterinarians with the international dimension of VPH, the Department of Public Health and Food Safety of the Faculty of Veterinary Medicine, Utrecht University, has been organizing a course in Veterinary Public Health and Animal Production for over the last 10 years. This course has been intended for Dutch as well as foreign final-year veterinary students and recent veterinary graduates. By bringing together participants from different countries, the course reinforces the international dimension of the issues addressed through the exchange of experiences by the participants themselves. The present article provides information about this course on Veterinary Public Health (VPH): it discusses logistics, didactical approaches, the course program, and the use of information and communication technology (ICT). Special attention is given to the intercultural aspects of higher education, all of which play an important role in the efficient exchange of knowledge between lecturers and students. International courses are an important tool to enable participants to interact in a multicultural environment and address issues that demand international cooperation and a global public health focus.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".