Correlation of Pre-Veterinary Admissions Criteria, Intra-Professional Curriculum Measures, AVMA-COE Professional Competency Scores, and the NAVLE
Bibliographic record
Abstract
Data consisting of preadmission criteria scores, annual and final cumulative grade point averages (GPAs), grades from individual professional courses, American Veterinary Medical Association Council on Education (AVMA-COE) Competency scores, annual class rank, and North American Veterinary Licensing Exam (NAVLE) scores were collected on all graduating DVM students at Kansas State University in 2009 and 2010. Associations among the collected data were compared by Pearson correlation. Pre-veterinary admissions criteria infrequently correlated with annual GPAs of Years 1-3, rarely correlated with the AVMA-COE Competencies, and never correlated with the annual GPA of Year 4. Low positive correlations occurred between the NAVLE and the Verbal Graduate Record Examination (GRE) (r=.214), Total GRE (r=.171), and the mean GPA of pre-professional science courses (SGPA) (r=.236). Annual GPAs strongly correlated with didactic course scores. Annual GPAs and final class rank strongly correlated (mean r=-.849), and both strongly correlated with the NAVLE score (NAVLE: GPAs mean r=.628, NAVLE: final class rank r=-.714). Annual GPAs at the end of Years 1-4 weakly correlated or did not correlate with the AVMA-COE Competencies. The AVMA-COE Competencies weakly correlated with scores earned in didactic courses of Years 1-3. AVMA-COE Competencies were internally consistent (mean r=.796) but only moderately correlated with performance on the NAVLE (mean r=.319). Low correlations between admissions criteria and outcomes indicate a need to reevaluate admission criteria as predictors of school success. If the NAVLE remains the primary discriminator for veterinary licensure (and the gateway to professional activity), then the AVMA-COE Competencies should be refined to better improve and reflect the NAVLE, or the NAVLE examination should change to reflect AVMA-COE Competencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.000 |
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 teacher head, 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".