Preparing Medical Students for the Final Examinations During the COVID-19 Crisis: A Bumpy Ride to the Finishing Line
Bibliographic record
Abstract
In this viewpoint, we share and reflect on the experiences of final-year students preparing for a high-stakes examination at the Faculty of Medicine, Universiti Kebangsaan Malaysia during the COVID-19 pandemic. We highlight the new challenges faced during web-based remote learning and major differences in the clinical learning environment at our teaching hospital, which was one of the designated COVID-19 centers in Malaysia. We also document how a face-to-face professional examination was conducted for final-year medical students at our institution despite in times of a global health crisis. The lessons learned throughout this process address the importance of resilience and adaptability in unprecedented times. Further, we recommend appropriate measures that could be applied by medical schools across the world to improve the delivery of quality medical education during a crisis in the years to come.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".