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 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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".