Rx for Recruitment and Retention of Veterinarian Scientists: Money, Marketing, Mentoring
Bibliographic record
Abstract
Veterinary medicine is failing both to sustain its academic base and to meet national needs for research in the fields of comparative medicine (translational research), public health, and food production. The basis for the shortage of veterinarians with research expertise is multi-factorial and related to the substantial commitment of time and money required to obtain both a DVM and advanced training, as well as the lack of motivation among veterinary students to engage in biomedical science. Effective strategies for increasing the number of veterinarian scientists must address these issues using a balanced combination of money, marketing, and mentoring. Success will require not only that we increase and improve opportunities for research training, but also that we create and sustain veterinary college environments that attract, foster, and reward dedication to research. The ''research pipeline'' needs to be transformed into a ''research manifold'' with multiple portals for entry and re-entry of trainees. Age-appropriate educational and mentoring programs should be implemented at K-14, baccalaureate, veterinary college, post-graduate, and junior faculty levels to promote recruitment, training, and retention of veterinarian scientists. New initiatives are especially needed to attract students with primary interests in science and biomedical research to the veterinary profession and to facilitate transition of motivated veterinary graduates from private practice to research careers. Specific examples of such programs are presented and future directions are discussed.
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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 | 0.057 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.057 | 0.023 |
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".