Undergraduate Rigor Scores: Do They Predict Achievement in Veterinary School?
Bibliographic record
Abstract
The relations between potential indicators of undergraduate rigor and subsequent achievement in professional school are not clear; some studies have shown that greater undergraduate selectivity is associated with greater achievement in medical science programs, while others have not. We sought to determine the extent to which indicators of undergraduate rigor were associated with achievement in veterinary school. Participants were graduates from three cohorts. The predictors were undergraduate GPA (UGPA), plus five rigor scores-degree or number of undergraduate credits, number of honors courses, number of withdrawals from or repeats of prerequisite science courses, number of part-time semesters, and ratio of community college credits to total college credits. The outcomes were the veterinary medicine cumulative GPA (CVM GPA), Qualifying Exam scores, and North American Veterinary Licensing Exam scores. Using correlations corrected for range restriction, we regressed each outcome on the five rigor scores and UGPA for each of the three graduating cohorts. In most cases, indicators of undergraduate rigor did not predict subsequent achievement in veterinary school; however, in two comparisons, number of honors courses taken as an undergraduate predicted subsequent achievement. UGPA, as expected, predicted CVM GPA. Admissions committees may want to reevaluate whether they include undergraduate rigor when considering admission to their programs, with the caveat that our findings are specific to our institution and are not generalizable.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".