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Record W3097342595 · doi:10.3138/jvme.1018-130r

A Global Veterinary Education Program for North American Veterinary Students: A Description of Purdue University Best Practices

2020· article· en· W3097342595 on OpenAlexfundvenueno aff
William P. Smith, Sandra F. San Miguel

Bibliographic record

VenueJournal of Veterinary Medical Education · 2020
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
FundersDivision of Mathematical SciencesMitacsFields Institute for Research in Mathematical SciencesCanadian Bureau for International Education
KeywordsGlobeGlobal healthVeterinary medicinePublic relationsVeterinary educationMedical educationMedicinePolitical sciencePublic healthNursingCurriculum

Abstract

fetched live from OpenAlex

Worldwide growth in global mobility has transformed the way we communicate, trade, and approach global issues. The rise of global migration and distribution comes with a higher probability of transmitted disease, human-wildlife conflict, and food safety issues. No longer viewed as isolated incidents, the occurrence of global health threats in one part of the globe is now a concern throughout the world. Our society needs globally conscious veterinarians who are dedicated to affecting world change through the improvement of animal and human health; veterinarians who are prepared to collaborate, exchange, and engage with the world around them. Higher education institutions for veterinary medicine have the responsibility to prepare their students to become agents of change within society and rewrite the narrative on global health. This article highlights the intentional approach that Purdue University College of Veterinary Medicine took to address the need for more globally conscious veterinarians. The article provides examples of administrative structures, funding sources, global engagement opportunities, methods to increase student awareness of opportunities, and student support. Finally, we describe the impact of this approach in increasing student participation in global engagement.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.461
GPT teacher head0.573
Teacher spread0.112 · 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.

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

Citations2
Published2020
Admission routes2
Has abstractyes

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