Incorporating Public Health Competencies Into Veterinary Medical Education
Bibliographic record
Abstract
This study evaluates the success of secondary public health education in enhancing a professional degree in veterinary medicine. Dual-degree programs promote multidisciplinary skill attainment crucial to succeed in today's One Health-centered veterinary workforce. Participant demographics were collected including academic background, dual-degree enrollment status, and intended course of study. Survey data were collected from both Master of Public Health students and dual Doctor of Veterinary Medicine/Master of Public Health students. To measure knowledge attainment, students over a 10-year period were provided core competency and program perception-based surveys upon entering and exiting the public health program. Participants were asked to rate their knowledge of competencies based on a scale of having "no knowledge" to being "very knowledgeable." Program perceptions were reported through multiple response types. Open-ended response questions evaluated participants' perceived program success in aiding the development of professional veterinary public health knowledge. The dual nature of this degree program is hypothesized to enhance interprofessional capabilities for those entering the field of veterinary medicine. A qualitative thematic comparison of participants' entrance and exit survey responses indicated increased levels of concern for career preparation services in dual-degree students. By coursework completion, students' most valued competencies were related to epidemiology, biostatistics, and behavioral health. Quantitative analysis revealed that students concurrently enrolled in a veterinary and public health program demonstrate significantly higher levels of self-reported knowledge relating to disease measurement, ethical and legal principles, and epidemiological data interpretation. Students with educational backgrounds in veterinary and animal sciences demonstrated significantly higher levels of program satisfaction.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".