Relationships between Admissions Requirements and Pre-clinical and Clinical Performance in a Distributed Veterinary Curriculum
Bibliographic record
Abstract
The purpose of this study was twofold: first, to assess the relationships between knowledge-based admission requirements and pre-clinical and clinical performance in a distributed model of veterinary education that uses problem-based learning as the main instruction method in the first two years of the curriculum; second, to compare pre-clinical and clinical performance with performance on the Program for the Assessment of Veterinary Education Equivalence (PAVE) exam. Admissions data including overall GPA, prerequisite GPA, Graduate Record Examination (GRE) score on the Analytical, Analytical Writing, Quantitative, and Verbal sections), veterinary school performance data (GPA for pre-clinical and clinical years), and performance PAVE (taken at the end of second year) were analyzed for two classes (N = 155, 85.8% women and 14.2% men). Overall GPA, prerequisite GPA, and GRE Quantitative and Analytical scores were the best predictors for pre-clinical (years 1 and 2) performance (R = 0.49, 23.5% of the variance), GRE Analytical score was the best predictor for year 3 (pre-clinical and clinical) performance (R = 0.25, 6.3% of the variance), GRE Quantitative score was the best predictor for PAVE performance (R = 0.27, 7.5% of the variance), and GRE Analytical score was the best predictor for clinical performance (year 4; R = 0.21, 4.4% of the variance). PAVE scores correlated with GRE Quantitative scores (r = 0.27, p <.01) and veterinary school performance, with higher correlations in the pre-clinical years (rs = 0.67-0.36, p < .01), providing evidence of convergent validity for the PAVE exam.
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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 | 0.003 | 0.030 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".