Evidence-Based Approach to Switching to a Pass-Fail System for Clinical Year Veterinary Student Grading
Bibliographic record
Abstract
Veterinary schools have traditionally used letter grading systems to assess the performance of students on clinical rotations, but pass-fail grading may enhance the learning environment and student well-being. When a decision to switch grading systems is discussed, concerns are often raised about the effect of removing clinical year grades from final grade point average (GPA) calculations. In order to inform the decision making at our institution, retrospective analysis of the effects of clinical year grades on GPA was performed. The specific hypothesis tested was that clinical year GPA would not have a significant effect on cumulative GPA, as defined by a decrease or increase of 0.10 points on average. When data from two classes were examined, median (range) difference final GPA (0–4 scale) compared to GPA at the end of the pre-clinical curriculum (referred to as delta GPA) was 0.02 (–0.19 to 0.18) for the graduating class of 2016 after removal of two outliers and 0.03 (–0.10 to 0.18) for the class of 2017. Correlations between preclinical GPA and delta GPA (were –0.83 ( p < .001) for both classes. The hypothesis was supported, leading to the conclusion that the overall effect of clinical letter grades on final GPA was close to zero when whole classes were considered, and delta GPA ranged between –0.2 and 0.2 for all except two students. Data from this study were distributed prior to conducting a faculty vote to switch grading systems, and the motion was supported.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.273 | 0.526 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.012 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".