Fostering Student–Faculty Partnerships for Continuous Curricular Improvement in Undergraduate Medical Education
Bibliographic record
Abstract
PROBLEM: A number of medical schools have used curricular reform as an opportunity to formalize student involvement in medical education, but there are few published assessments of these programs. Formal evaluation of a program's acceptability and use is essential for determining its potential for sustainability and generalizability. APPROACH: Harvard Medical School's Education Representatives (Ed Reps) program was created in 2015 to launch alongside a new curriculum. The program aimed to foster partnerships between faculty and students for continuous and real-time curricular improvement. Ed Reps, course directors, and core faculty met regularly to convey bidirectional feedback to optimize the learning environment in real time. OUTCOMES: A survey to assess the program's impact was sent to students and faculty. The majority of students (202/222; 91.0%) reported Ed Reps had a positive impact on the curriculum. Among faculty, 35/37 (94.6%) reported making changes to their courses as a result of Ed Reps feedback, and 34/37 (91.9%) agreed the program had a positive impact on the learning environment. Qualitative feedback from students and faculty demonstrated a change in school culture, reflecting the primary goals of partnership and continuous quality improvement (CQI). NEXT STEPS: This student-faculty partnership demonstrated high rates of awareness, use, and satisfaction among faculty and students, suggesting its potential for local sustainability and implementation at other schools seeking to formalize student engagement in CQI. Next steps include ensuring the feedback provided is representative of the student body and identifying new areas for student CQI input as the curriculum becomes more established.
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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 | 0.053 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".