Adapting medical education during crisis: Student–Faculty partnerships as an enabler of success
Bibliographic record
Abstract
Restrictions imposed by the COVID-19 pandemic have required medical educators to reimagine almost every aspect of undergraduate medical training, including curriculum delivery and assessments in a short timeline. In this personal view article, executive members of the University of Toronto medical student government and Faculty leads of pre-clerkship and clerkship education highlight five practical ways in which a student-Faculty partnership enabled the rapid and smooth adaptation of curricula during the COVID-19 pandemic. These included involving students as partners in decision making to contribute learner perspectives early, agile and collaborative meeting structures, frequent and consistent communication with the student body, providing learners with Faculty perspectives from the frontlines, and striking a balance in the level of feedback collected from students. These strategies may be of utility to medical administrators, educators, and student leaders in future crises affecting medical learners.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.002 | 0.040 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".