Predicting Admission and Future Performance of Veterinary School Applicants: Evaluation of Scores of Self-Reported Animal Experience and Rural Versus Urban Background
Bibliographic record
Abstract
Admission to veterinary school is generally based on academic and non-academic measures. Descriptions of animal or veterinary experience and rural versus urban background are often sought from applicants, but little is objectively known about their impact on admission success or future performance. We evaluated scores from written descriptions from 590 veterinary school applicants for the nature and extent of self-reported animal experience. For those admitted to the program, we compared animal experience and rural versus urban background to performance in discipline-based courses, professional skills courses, clinical rotations, and the North American Veterinary Licensing Exam (NAVLE). More than 98% of applicants reported animal experience, with small animal veterinary experience most reported. There was no difference in animal experience or background between successful and unsuccessful applicants, but rural and urban applicants reported different experiences. There was a small correlation between small animal experience and performance in clinical rotations (.21), a small negative correlation between rural background and NAVLE performance (-.23), but otherwise, no significant correlations between animal experience or background and future performance. These findings suggest that scores of self-reported animal experience do not provide predictive information on applicants, or, alternatively, that the nature and extent of animal experience, the methods used to score these experiences, and/or the measures assessed during veterinary school need to be explicitly defined to ensure that we are capturing the appropriate information. More investigation into the scoring and impact of animal experience and background on applicant performance in the DVM program and success in a veterinary career is warranted.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".