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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".