The pre-clerkship medical student's experience during the COVID-19 pandemic: An international conversation with recommendations for progress
Bibliographic record
Abstract
In response to the spread of SARS-CoV-2 across North America in early March of 2020, Canadian and United States medical schools swiftly virtualized medical education for pre-clerkship students. With remote learning arrived novel challenges: barriers to students’ comprehension of course material, difficulties conveying the nuances of patient interaction, and social hardships hindering students’ continued progress. The 2020 Harvard-McGill Medical Student Exchange, a group of ten McGill University Faculty of Medicine and Harvard Medical School students, analyzed their institutions’ respective responses in the virtualization of medical education and their personal experiences with remote pre-clerkship education. The authors’ work provides insight into opportunities for mutual progress and cross-cultural exchange between Canadian and American medical schools, in the context of the COVID-19 pandemic. The authors detail potential changes to didactics, student research opportunities, support for students, and clerkship preparation that they expect would benefit pre-clerkship students in an ever-changing biomedical landscape. With gratitude toward their respective programs for their efforts in transitioning to virtual learning, the authors look toward a future of medical education increasingly interwoven with digital technology and responsive to social change.
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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 | 0.033 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.021 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.014 | 0.036 |
| Insufficient payload (model declined to judge) | 0.008 | 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".