Inter-Institutional Partnerships to Develop Veterinarian–Investigators through the NIH Comparative Biomedical Scientist Training Program Benefit One Health Goals
Bibliographic record
Abstract
Limitations in workforce size and access to resources remain perennial challenges to greater progress in academic veterinary medicine and engagement between human and veterinary medicine (One Health). Ongoing resource constraints occur in part due to limited public understanding of the role veterinarians play in improving human health. One Health interactions, particularly through interdisciplinary collaborations in biomedical research, present constructive opportunities to inform resource policies and advance health care. To this end, inter-institutional partnerships between individual veterinary medical education programs (VMEPs) and several National Institutes of Health (NIH) intramural research programs have created synergies beyond those provided by individual programs. In the NIH Comparative Biomedical Scientist Training Program (CBSTP), interdisciplinary cross-training of veterinarians consisting of specialty veterinary medicine coupled with training in human disease research leading to a PhD, occurs collaboratively on both VMEP and NIH campuses. Pre-doctoral veterinary student research opportunities have also been made available. Through the CBSTP, NIH investigators and national biomedical science policy makers gain access to veterinary perspective and expertise, while veterinarians obtain additional opportunities for NIH-funded research training. CBSTP Fellows serve as de facto ambassadors enhancing visibility for the profession while in residence at NIH, and subsequently through a variety of university, industry, and government research appointments, as graduates. Thus, the CBSTP represents an inter-institutional opportunity that not only addresses critical needs for veterinarian-scientists in the biomedical workforce, but also simultaneously exposes national policy makers to veterinarian-scientists' specialized training, leading to more effective realization of One Health goals to benefit human and animal 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 | 0.055 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.006 | 0.041 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.043 | 0.010 |
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".