Status Report on Education in the Economics of Animal Health: Results from a European Survey
Bibliographic record
Abstract
Education on the use of economics applied to animal health (EAH) has been offered since the 1980s. However, it has never been institutionalized within veterinary curricula, and there is no systematic information on current teaching and education activities in Europe. Nevertheless, the need for economic skills in animal health has never been greater. Economics can add value to disease impact assessments; improve understanding of people's incentives to participate in animal health measures; and help refine resource allocation for public animal health budgets. The use of economics should improve animal health decision making. An online questionnaire was conducted in European countries to assess current and future needs and expectations of people using EAH. The main conclusion from the survey is that education in economics appears to be offered inconsistently in Europe, and information about the availability of training opportunities in this field is scarce. There is a lack of harmonization of EAH education and significant gaps exist in the veterinary curricula of many countries. Depending on whether respondents belonged to educational institutions, public bodies, or private organizations, they expressed concerns regarding the limited education on decision making and impact assessment for animal diseases or on the use of economics for general management. Both public and private organizations recognized the increasing importance of EAH in the future. This should motivate the development of teaching methods and materials that aim at developing the understanding of animal health problems for the benefit of students and professional veterinarians.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".