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Record W3134088023 · doi:10.3138/jvme-2020-0019

One Health Interdisciplinary Collaboration in Veterinary Education Establishments in Europe: Mapping Implementation and Reflecting on Promotion

2021· article· en· W3134088023 on OpenAlexvenueno aff
Despoina Iatridou, Ana Bravo, Jimmy Saunders

Bibliographic record

VenueJournal of Veterinary Medical Education · 2021
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumAccreditationMultidisciplinary approachEuropean unionMedical educationLegislationPromotion (chess)Political sciencePublic relationsEngineering ethicsMedicineSociologyPedagogyEngineeringBusiness

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.003
Scholarly communication0.0050.005
Open science0.0010.007
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.149
GPT teacher head0.527
Teacher spread0.378 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations16
Published2021
Admission routes1
Has abstractyes

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