Undergraduate Radiology Education During the COVID-19 Pandemic: A Review of Teaching and Learning Strategies
Bibliographic record
Abstract
The Coronavirus disease 2019 (COVID-19) pandemic has altered how medical education is delivered, worldwide. Didactic sessions have transitioned to electronic/online platforms and clinical teaching opportunities are limited. These changes will affect how radiology is taught to medical students at both the pre-clerkship (ie, year 1 and 2) and clinical (ie, year 3 and 4) levels. In the pre-clerkship learning environment, medical students are typically exposed to radiology through didactic lectures, integrated anatomy laboratories, case-based learning, and ultrasound clinical skills sessions. In the clinical learning environment, medical students primarily shadow radiologists and radiology residents and attend radiology resident teaching sessions. These formats of radiology education, which have been the tenets of the specialty, pose significant challenges during the pandemic. This article reviews how undergraduate radiology education is affected by COVID-19 and explores solutions for teaching and learning based on e-learning and blended learning theory.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".