Medical Students' Corner: Lessons From COVID-19 in Equity, Adaptability, and Community for the Future of Medical Education
Bibliographic record
Abstract
As UK medical students, we recently completed 3 months of remote learning due to the COVID-19 pandemic, before taking online end-of-the-year exams. We are now entering our final year of medical school. Based on our experiences and our understanding of others' experiences, we believe that three key lessons have been universal for medical students around the world. The lessons learned throughout this process address the need for a fair system for medical students, the importance of adaptability in all aspects of medical education, and the value of a strong medical school community. These lessons can be applied in the years to come to improve medical education as we know it.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.008 | 0.025 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".