Correlations between Pre-Veterinary Course Requirements and Academic Performance in the Veterinary Curriculum: Implications for Admissions
Bibliographic record
Abstract
This study addressed how students' undergraduate science courses influence their academic performance in a veterinary program, and examined what implications this may have for the veterinary admissions process. The undergraduate transcripts and veterinary school rankings of current third-year veterinary students at Colorado State University were coded and analyzed. Because the study found no statistically meaningful relationships between the pre-veterinary coursework parameters and class rank, it could be concluded that veterinary schools may be unnecessarily restricting access to the profession by requiring long and complicated lists of prerequisite courses that have a questionable predictive value on performance in veterinary school. If a goal of veterinary schools is to use the admissions process to enhance recruitment and provide the flexibility necessary to admit applicants who have the potential to fill the current and emerging needs of the profession, schools may want to re-evaluate how they view pre-veterinary course requirements. One of the recommendations generated from the results of this study is to create a list of veterinary prerequisite courses common to all schools accredited by the Association of American Veterinary Medical Colleges. It is suggested that this might simplify pre-veterinary advising, enhance recruitment, and provide flexibility for admitting nontraditional but desirable applicants, without impacting the quality of admitted veterinary students.
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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 | 0.007 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".