European Veterinary Public Health Specialization: Post-graduate Training and Expectations of Potential Employers
Bibliographic record
Abstract
Residents of the European College of Veterinary Public Health (ECVPH) carried out a survey to explore the expectations and needs of potential employers of ECVPH diplomates and to assess the extent to which the ECVPH post-graduate training program meets those requirements. An online questionnaire was sent to 707 individuals working for universities, government organizations, and private companies active in the field of public health in 16 countries. Details on the structure and activities of the participants' organizations, their current knowledge of the ECVPH, and potential interest in employing veterinary public health (VPH) experts or hosting internships were collected. Participants were requested to rate 22 relevant competencies according to their importance for VPH professionals exiting the ECVPH training. A total of 138 completed questionnaires were included in the analysis. While generic skills such as "problem solving" and "broad horizon and inter-/multidisciplinary thinking" were consistently given high grades by all participants, the importance ascribed to more specialized skills was less homogeneous. The current ECVPH training more closely complies with the profile sought in academia, which may partly explain the lower employment rate of residents and diplomates within government and industry sectors. The study revealed a lack of awareness of the ECVPH among public health institutions and demonstrated the need for greater promotion of this veterinary specialization within Europe, both in terms of its training capacity and the professional skill-set of its diplomates. This study provides input for a critical revision of the ECVPH curriculum and the design of post-graduate training programs in VPH.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".