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Enregistrement W2268635295 · doi:10.3138/jvme.0615-093

Combined Veterinary–Human Medical Education: A Complete One Health Degree?

2015· letter· en· W2268635295 sur OpenAlexvenueaboutno aff
P. Eyre

Notice bibliographique

RevueJournal of Veterinary Medical Education · 2015
Typeletter
Langueen
DomaineHealth Professions
ThématiqueVeterinary Practice and Education Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCourseworkCurriculumFlexibility (engineering)Medical educationVeterinary medicineVeterinary public healthMedicinePublic healthManagementPsychologyNursingPedagogy

Résumé

récupéré en direct d'OpenAlex

Virchow’s concept, which has become known as One Health, currently depicts collaboration among multiple institutions, professions, and disciplines—working locally, nationally, and globally—to protect the health of people, animals, plants, and the environment.1 Comprehensive integration of health education, research, and public service seems inevitable. The American Veterinary Medical Association (AVMA)’s One Health Initiative1 has already inspired several important actions.2–4 The first recorded DVM-MD dual degree was created by William Osler, McGill University physician, and Duncan McEachran, Montreal Veterinary College dean, in the 1880s.5 Veterinary graduates could qualify as physicians simply by completing the final year of the McGill medical school curriculum. Unfortunately, this extraordinary collaboration ended following Osler’s move to the University of Pennsylvania.5 Presently, a few veterinary graduates also hold human medical degrees, although each diploma was earned independently. Dual DVMa-PhD, and dual DVMa-Master’s degrees are widely available; however, as far as can be ascertained, there are no integrated veterinary–human medical degree programs. Yet, combining veterinary and human medical education is intuitively simple: a veterinary graduatea would become a physician by completing the final two years of medical school.b Or a medical graduateb would qualify as a veterinarian after completing the third and fourth years of veterinary college.a Such arrangements would demand great flexibility and cooperation to ensure reciprocity between veterinary and human preclinical science curricula, which are similar but not identical. Supplementary coursework might be needed in disciplines such as comparative anatomy, physiology, and pathobiology, which could be given during pre-clinical summer terms at no cost to students. Student selection and enrollment would be straightforward. Upon admission to a participating veterinary college or medical school, students would be informed of available veterinary–human medical dual degree programs, and their interest sought. Also, potential applicants could be recruited during the pre-clinical years. An admissions committee representing both veterinary and human medicine would pick candidates with genuine commitment to inter-professional human–animal–environmental health and interest in research and investigative medicine. To achieve these same goals in the curriculum, the dual syllabus would incorporate specially designed series (tracks) of vertically integrated One Health electives—particularly ecology, environmental biology, and public health—and require the completion of a One Health research project of publishable quality. Tuition should be proportionally the same as charged for the separate degrees. However, for inducement, students could receive privately funded tuition reimbursements and scholarships to finance the additional two years of clinical education. Collaborating colleges need not be located at the same university campus. The One Health model offers valuable opportunities to veterinary and human medicine, although success will require new ways of thinking and behaving. Dual veterinary–human medical graduates would be influential thought leaders for unifying the two health professions for the benefit of society.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,008
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesIntégrité de la recherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,086
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0070,008
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0020,001
Intégrité de la recherche0,0020,010
Charge utile insuffisante (le modèle a refusé de juger)0,0100,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,666
Tête enseignante GPT0,583
Écart entre enseignants0,083 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations7
Publié2015
Routes d'admission2
Résumé présentoui

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