An Admissions System to Select Veterinary Medical Students with an Interest in Food Animals and Veterinary Public Health
Bibliographic record
Abstract
Interest in the areas of food animals (FA) and veterinary public health (VPH) appears to be declining among prospective students of veterinary medicine. To address the expected shortage of veterinarians in these areas, the Utrecht Faculty of Veterinary Medicine has developed an admissions procedure to select undergraduates whose aptitude and interests are suited to these areas. A study using expert meetings, open interviews, and document analysis identified personal characteristics that distinguished veterinarians working in the areas of FA and VPH from their colleagues who specialized in companion animals (CA) and equine medicine (E). The outcomes were used to create a written selection tool. We validated this tool in a study among undergraduate veterinary students in their final (sixth) year before graduation. The applicability of the tool was verified in a study among first-year students who had opted to pursue either FA/VPH or CA/E. The tool revealed statistically significant differences with acceptable effect sizes between the two student groups. Because the written selection tool did not cover all of the differences between the veterinarians who specialized in FA/VPH and those who specialized in CA/E, we developed a prestructured panel interview and added it to the questionnaire. The evaluation of the written component showed that it was suitable for selecting those students who were most likely to succeed in the FA/VPH track.
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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 | 0.026 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".