Exploring the global impact of the COVID-19 pandemic on medical education: an international cross-sectional study of medical learners
Bibliographic record
Abstract
BACKGROUND: The evidence surrounding the impact of COVID-19 on medical learners remains anecdotal and highly speculative despite the anticipated impact and potential consequences of the current pandemic on medical training. The purpose of this study was to explore the extent that COVID-19 initially impacted medical learners around the world and examine global trends and patterns across geographic regions and levels of training. METHODS: A cross-sectional survey of medical learners was conducted between March 25-June 14, 2020, shortly after the World Health Organization declared COVID-19 a pandemic. RESULTS: 6492 learners completed the survey from 140 countries. Most medical schools removed learners from the clinical environment and adopted online learning, but students reported concerns about the quality of their learning, training progression, and milestone fulfillment. Residents reported they could be better utilized and expressed concerns about their career timeline. Trainees generally felt under-utilized and wanted to be engaged clinically in meaningful ways; however, some felt that contributing to healthcare during a pandemic was beyond the scope of a learner. Significant differences were detected between levels of training and geographic regions for satisfaction with organizational responses as well as the impact of COVID-19 learner wellness and state-trait anxiety. CONCLUSIONS: The disruption to the status quo of medical education is perceived by learners across all levels and geographic regions to have negatively affected their training and well-being, particularly amongst postgraduate trainees. These results provide initial empirical insights into the areas that warrant future research as well as consideration for current and future policy planning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".