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Record W2023282147 · doi:10.3138/jvme.0711.078r

Toward Harmonization of the European Food Hygiene/Veterinary Public Health Curriculum

2012· article· en· W2023282147 on OpenAlexvenueno aff
Frans J.M. Smulders, S. Bunčić, K. Fehlhaber, Robert Huey, Hannu Korkeala, Iva Steinhauserová

Bibliographic record

VenueJournal of Veterinary Medical Education · 2012
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationVeterinary public healthEuropean unionCurriculumMedicineCompetence (human resources)Public healthHarmonizationPopulationMedical educationVeterinary medicinePolitical scienceBusinessEnvironmental healthNursingManagement

Abstract

fetched live from OpenAlex

Prompted by developments in the agri-food industry and associated recent changes in European legislation, the responsibilities of veterinarians professionally active in veterinary public health (VPH), and particularly in food hygiene (FH), have increasingly shifted from the traditional end-product control toward longitudinally integrated safety assurance. This necessitates the restructuring of university training programs to provide starting competence in this area for veterinary graduates or a sub-population of them. To date, there are substantial differences in Europe in the way in which graduate programs in FH/VPH are structured and in the time allocated to this important curricular group of subjects. Having recognized this, the European Association of Establishments for Veterinary Education (EAEVE) recently instituted a working group to analyze the current situation, with a view to produce standard operating procedures allowing fair and transparent evaluations of universities/faculties constituting its membership and in concurrence with explicit European legislation on the professional qualifications deemed necessary for this veterinary discipline. This article summarizes the main conclusions and recommendations of the working group and seeks to contribute to the international efforts to optimize veterinary training in FH/VPH.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.513
GPT teacher head0.520
Teacher spread0.007 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2012
Admission routes1
Has abstractyes

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