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Record W2064636773 · doi:10.3138/jvme.35.2.166

A Flexible Approach to Training Veterinarians in Public Health: An Overview and Early Assessment of the DVM/MPH Dual-Degree Program at the University of Minnesota

2008· article· en· W2064636773 on OpenAlexvenueno aff
Larissa Minicucci, Kate A. Hanson, Debra Olson, William D. Hueston

Bibliographic record

VenueJournal of Veterinary Medical Education · 2008
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedical educationMultidisciplinary approachFlexibility (engineering)Public healthVeterinary public healthEducational programProgram evaluationProgram Design LanguageMedicinePsychologyNursingEngineeringPolitical scienceManagement

Abstract

fetched live from OpenAlex

As a result of the growing need for public-health veterinarians, novel educational programs are essential to train future public-health professionals. The University of Minnesota School of Public Health, in collaboration with the College of Veterinary Medicine, initiated a dual DVM/MPH program in 2002. This program provides flexibility by combining distance learning and on-campus courses offered through a summer public-health institute. MPH requirements are completed through core courses, elective courses in a focus area, and an MPH project and field experience. Currently, more than 100 students representing 13 veterinary schools are enrolled in the program. The majority of initial program graduates have pursued public-practice careers upon completion of the program. Strengths of the Minnesota program design include accessibility and an environment to support multidisciplinary training. Continued assessment of program graduates will allow for evaluation and adjustment of the program in the coming years.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.747
GPT teacher head0.559
Teacher spread0.188 · 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 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

Citations8
Published2008
Admission routes1
Has abstractyes

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