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

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

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

Bibliographic record

VenueJournal of Veterinary Medical Education · 2015
Typeletter
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCourseworkCurriculumFlexibility (engineering)Medical educationVeterinary medicineVeterinary public healthMedicinePublic healthManagementPsychologyNursingPedagogy

Abstract

fetched live from 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.

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)0.007
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.086
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0020.010
Insufficient payload (model declined to judge)0.0100.001

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 head0.666
GPT teacher head0.583
Teacher spread0.083 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations7
Published2015
Admission routes2
Has abstractyes

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