Does the DVM–MPH Dual-Degree Program Effectively Prepare Veterinarians for Public Health Roles in the United States? Gaps Identified From a National Survey
Bibliographic record
Abstract
Veterinarians bridge the critical gap between animal and human health and can alert health agencies to potential health risks in cases of emergencies or disease outbreaks, making them a crucial ally in the fight against infectious diseases. Thus, training veterinarians in public health provides them with additional skills, knowledge, and the necessary tools in disease and hazard prevention. We conducted a national survey in the United States to determine the efficacy of doctor of veterinary medicine (DVM)-master of public health (MPH) and veterinariae medicinae doctoris (VMD)-MPH dual degrees, and we identified gaps and shortcomings in the curricula in order to improve the career outcomes of enrolled veterinary students. The survey was sent in 2020 to alumni of all the DVM-MPH and VMD-MPH programs and administered through Qualtrics. Key findings include that most participants were women (83.2%), the predominant age group was 30-39 years (61%), and 50.4% considered the MPH beneficial. The majority of respondents work in veterinary medicine or public health. Qualifications and veterinary networks were identified as the most useful for securing employment. About 31.1% felt their program prepared them moderately well for these jobs, while 68.9% indicated that the MPH did not affect their salary. Respondents also highlighted areas needing improvement in DVM-MPH programs, such as increased practical work-skill opportunities, mentoring, career support, and access to nonveterinary courses and students. These findings can be useful for veterinary and public health schools in developing curricula and opportunities that strengthen the preparation of veterinarians in public health.
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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 | 0.005 | 0.013 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".