Veterinarians and Public Practice at the Virginia–Maryland Regional College of Veterinary Medicine: Building on a Tradition of Expertise and Partnership
Bibliographic record
Abstract
The Virginia-Maryland Regional College of Veterinary Medicine (VMRCVM), a regional veterinary college for Maryland and Virginia, has a long and unique tradition of encouraging careers in public and corporate veterinary medicine. The VMRCVM is home to the Center for Public and Corporate Veterinary Medicine (CPCVM), and each year approximately 10% of the veterinary students choose the public/corporate veterinary medicine track. The faculty of the CPCVM, and their many partners from the veterinary public practice community, teach in the veterinary curriculum and provide opportunities for students locally, nationally, and internationally during summers and the final clinical year. Graduates of the program work for government organizations, including the US Department of Agriculture, the Food and Drug Administration, and the Centers for Disease Control and Prevention, as well as in research, in industry, and for non-governmental organizations. Recent activities include securing opportunities for students, providing career counseling for graduate veterinarians interested in making a career transition, delivering continuing education, and offering a preparatory course for veterinarians sitting the board examination for the American College of Veterinary Preventive Medicine. As the VMRCVM moves forward in recognition of the changing needs of the veterinary profession, it draws on its tradition of partnership and capitalizes on the excellence of its existing program. Future plans for the CPCVM include possible expansion in the fields of public health, public policy, international veterinary medicine, organizational leadership, and the One Health initiative. Quality assurance and evaluation of the program is ongoing, with recognition that novel evaluation approaches will be useful and informative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 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".