Covid-19 pandemic & medical education: A medical student’s perspective
Bibliographic record
Abstract
The specific impact of the COVID-19 pandemic on medical education remains elusive and evolving. Clinical teaching opportunities have become limited with the shift in focus of supervising physicians away from trainees and towards the care of the sick and vulnerable. The presence of medical students in hospitals has come to represent an added strain on vital resources, and the added risk of viral dissemination into communities has left medical students eager to help observing from only the sidelines. The present article provides a medical student's perspective on this unique, evolving situation, and identifies several learning opportunities that medical students may reflect upon and carry forth into their careers ahead. By exploring the current and future impact of this pandemic on clerkship, pre-clerkship and post-graduate medical training, specific challenges and future direction for both medical students and educators are discussed.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".