A New Approach to Teaching Veterinary Public Health at the Ohio State University
Bibliographic record
Abstract
Public-health practitioners with expertise in the area of veterinary public health are expected to understand the prevention and control of zoonotic infectious diseases in both human and animal populations. This focus on multiple species is what makes the veterinary public health (VPH) official unique. The development of a new VPH specialization within the existing Master of Public Health (MPH) degree program at the Ohio State University represents a significant new collaboration between the College of Veterinary Medicine and the College of Public Health. The main objective of the VPH specialization is to educate and train professionals to provide them with the skills, knowledge, and resources necessary to protect and improve human health using a One Medicine approach. The program targets a population of students who will likely enter the professional veterinary medicine curriculum but have one year available to enhance their preparatory training in health sciences before beginning the program. A core series of VPH courses was initiated to complement the existing MPH course requirements. The program has been successful in attracting students from the primary target population, but it has also attracted students wanting the MPH as a terminal degree and veterinarians returning to school to expand their career options.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.054 | 0.005 |
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".