Impact of COVID-19 on Canadian Medical Education: Pre-clerkship and Clerkship Students Affected Differently
Bibliographic record
Abstract
The coronavirus pandemic (COVID-19) has altered the undergraduate learning experience for many students across Canada. Medical education is no exception; clinical programs, in-person lectures, and mandatory hands-on activities have been suspended to adhere to social distancing guidelines. As remote teaching becomes the forefront of education, medical curricula have been forced to adapt accordingly in order to fulfill the core competencies of medical training and to provide quality education to medical students. With that in mind, the COVID-19 crisis offers a unique opportunity to evaluate the current "continuity plans" in medical education as they stand. This paper provides the perspective of medical students on how medical education is changing for both pre-clerkship and clerkship students, using their experience at McGill University as an example for the Canadian medical education system. Additionally, we discuss the accommodations put forth by the undergraduate medical education (UGME) office, and reflect on the limitations and sustainable solutions in supporting quality medical education.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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".