Partnered Educational Governance: Rethinking Student Agency in Undergraduate Medical Education
Bibliographic record
Abstract
Historically, students have been "consumers" of undergraduate medical education (UME) rather than stakeholders in its design and implementation. Student input has been retrospective, and although UME leaders have been open to feedback, matters most important to students have often been overlooked, leaving students feeling largely unheard. Student representation has also lacked structure and unity of feedback.A vision for effective student representation drove the creation of a partnered educational governance (PEG) model at McGill University in Montreal, Quebec, Canada, where sharing of expertise between student representatives and UME leadership has improved the UME program and the educational experience of students.The PEG model is grounded in the literature on student government, the student-as-partner framework, and theories of accountability. As part of the model, the student Medical Education Committee, an organized structure for discussion and reporting to student constituents, was established. This structure allows student representatives, entrusted by their peers and faculty, to proactively provide input to UME committees in the development of policies and curricula. The partnership between students and faculty facilitates a shared understanding of educational challenges and potential solutions.Within the first year, meaningful changes associated with the PEG model included increased student engagement in key program decisions, such as the redesign of a research course and an update to the absences and leaves policy. The PEG model enables unified student representation that is accountable and representative-and has a significant impact on outcomes-while maintaining the UME program's ownership of and responsibility for the curriculum and policies.
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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 | 0.094 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.033 |
| Scholarly communication | 0.025 | 0.022 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".