One Health Interdisciplinary Collaboration in Veterinary Education Establishments in Europe: Mapping Implementation and Reflecting on Promotion
Bibliographic record
Abstract
One Health recognizes the interconnection of people, animals, and the environment and encourages a multidisciplinary approach. Several high-level European and global policy recommendations call for close intersectoral collaboration to better understand and manage health challenges faced today. Academic education has a fundamental role in preparing all health professionals in that respect. Our research investigates whether, and how, European Veterinary Education Establishments (VEEs) implement interdisciplinarity. We collected data on undergraduate education, post-graduate programs, and academic research through a pan-European survey. Our aim was to identify policy actions that could encourage cross-sectoral education and a culture of One Health at universities. Input from 41 VEEs showed that interdisciplinary education for undergraduates is still in the early stages. The models of academic structure, undergraduate curricula, and education policies established so far hinder interdisciplinarity. One Health interdisciplinary post-graduate education is easier, at least in some countries, while research successfully integrates multidisciplinary interdisciplinary and transdisciplinary approaches . To conclude, we propose five recommendations to promote interdisciplinary education in veterinary and other curricula and to further encourage the intersectoral cooperation in research: (1) the need for the development of One Health transdisciplinary competencies across different discipline curricula in the European Union (EU); (2) the need for an integrated strategy of university structures and policies (for undergraduates and post-graduates) to encourage and support interdisciplinarity; (3) the need for a harmonized approach to academic education via accreditation; (4) the need for appropriate legislation to facilitate interdisciplinary training; and (5) the need to encourage One Health research.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".