Remediation of Pre-Clinical Course Failures in a DVM Program and Its Impact on Program Outcomes: A 10-Year Descriptive Study
Bibliographic record
Abstract
Remediation provides academically struggling students reasonable opportunities to correct deficiencies in knowledge or skills, achieve competence, and potentially reverse failures. At Purdue University College of Veterinary Medicine, a remediation policy in the pre-clinical years of the DVM program was implemented beginning with the class of 2014. We evaluated its impact on our DVM program and student outcomes. Using data from DVM classes of 2011 to 2023, we compared academic outcomes between remediating and non-remediating class cohorts and, within remediating cohorts, between students with and without academic difficulties. Despite changes in class size and admissions criteria, 4-year graduation and relative attrition rates were similar in remediating (92.2% and 4.2%) and non-remediating (92.3% and 4.8%) cohorts. Success at the North American Veterinary Licensing Examination (NAVLE) prior to graduation was lower in remediating than in non-remediating cohorts (94.5% vs. 97.0%). Among 815 students in remediating cohorts, 157 (19.3%) failed ≥1 courses. Of the 157 students, 134 (85.4%) attempted remediation of ≥1 failed courses, 125 (79.6%) successfully remediated ≥1 failed courses, and 96 (61.1%) successfully remediated all their failed courses. Remediation occurred more often in first-year than in second- or third-year courses. While 99% of the 96 successfully remediated students graduated in 4 years, 13.5% failed ≥1 clinical blocks and 18.7% did not pass the NAVLE before DVM graduation. Our remediation policy enabled successfully remediated students to avoid delayed graduation, but some students struggled in the clinical year and at passing the NAVLE prior to graduation. Additional support systems are necessary to help students pass the NAVLE before graduation.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".