Public Health Education in Veterinary Schools: A Student Perspective
Bibliographic record
Abstract
INTRODUCTION The veterinary medical profession is evolving rapidly in the face of societal changes, technological advancements, economic limitations, and a greater recognition of its application in the public health sector. As emphasized during the conference An Agenda for Action: Veterinary Medicine’s Crucial Role in Biodefense and Public Health, veterinary professionals are ideally suited to address many of the headlining issues in public health, such as bioterrorist and agro-terrorist threats, emerging diseases, and food safety hazards. The critical question to be addressed is whether or not colleges and schools of veterinary medicine have programs sufficiently in place to ensure that these needs will be met by their graduates. Participants in the Summer Fellowship Program in Science, Technology, and Public Policy: Implications for Veterinary Medicine, hosted by the Virginia–Maryland Regional College of Veterinary Medicine in summer 2003, were encouraged to provide a ‘‘student perspective’’ on this issue. Eleven fellows, secondto fourth-year veterinary students from six veterinary schools, participated in the Summer Fellowship, which focused on bioterrorism, agro-terrorism, and homeland security. Students had the opportunity to meet with experts in the field of bioterrorism; veterinarians serving as fellows in the executive and legislative branches in Washington, DC; and veterinarians working for the Department of Defense, the American Veterinary Medical Association, the Association of American Veterinary Medical Colleges, industry, the US Department of Agriculture, the Food and Drug Administration, the Library of Congress, the Homeland Security Council, the Department of Health and Human Services, and the Maryland Department of Agriculture. This experience provided the fellows a unique bridge of perception and insight between their own ongoing veterinary education and these critical needs and obligations of veterinary medical education; it also serves as the basis of the ‘‘student perspective’’ described in this article.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".