Making medical student course evaluations meaningful: implementation of an intensive course review protocol
Bibliographic record
Abstract
BACKGROUND: Ongoing course evaluation is a key component of quality improvement in higher education. The complexities associated with delivering high quality medical education programs involving multiple lecturers can make course and instructor evaluation challenging. We describe the implementation and evaluation of an "intensive course review protocol" in an undergraduate medical program METHODS: We examined pre-clerkship courses from 2006 to 2011 - prior to and following protocol implementation. Our non-parametric analysis included Mann-Whitney U tests to compare the 2006/07 and 2010/11 academic years. RESULTS: We included 30 courses in our analysis. In the 2006/07 academic year, 13/30 courses (43.3 %) did not meet the minimum benchmark and were put under intensive review. By 2010/11, only 3/30 courses (10.0 %) were still below the minimum benchmark. Compared to 2006/07, courses ratings in the 2010/11 year were significantly higher (p = 0.004). However, during the study period mean response rates fell from 76.5 % in 2006/07 to 49.7 % in 2010/11. CONCLUSION: These results suggest an intensive course review protocol can have a significant impact on pre-clerkship course ratings in an undergraduate medical program. Reductions in survey response rates represent an ongoing challenge in the interpretation of student feedback.
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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 | 0.453 | 0.497 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".