Medical education advances and innovations: A silver lining during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has disrupted healthcare processes substantially including medical education, necessitating several changes along the spectrum of medical training. While this crisis presents major challenges to medical education, it is also an immense opportunity for innovation. In this commentary, Canadian medical students cast a spotlight on four domains of Canadian medical education which have seen substantial changes during the COVID-19 pandemic: medical school admissions, pre-clerkship content delivery, virtual care and telemedicine curricula, and the residency matching process. Using the 10 recommendations noted in the Association of Faculties of Medicine of Canada (AFMC) 2010 Future of Medical Education in Canada report as a guiding framework, we discuss why these changes represent key steps forward that should be preserved in medical education beyond the pandemic, and advocate for a continuous quality improvement approach to evaluate and implement these innovations.
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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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.036 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.030 | 0.045 |
| Insufficient payload (model declined to judge) | 0.003 | 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".