A Proposal of Revised Curriculum to Circumvent the Impact of COVID Restrictions on Final Year Medical Students
Bibliographic record
Abstract
Medical education has been extraordinarily disrupted during the COVID-19 era worldwide. The pandemic limited routine ward or patient-based medical education. These limitations have resulted in new challenges for medical students, especially the final year students in completing their mandated curriculum. We are suggesting a revised curriculum for final year medical students, by following which we can address COVID restriction while making sure all competencies have been achieved by students. This revised curriculum centers around the usual placement of students in Surgical Assessment Unit (SAU), however all students will be posted in simulation wards/labs on their turn to enhance and consolidate their understanding and learning of common surgical cases in these wards, so that they can replicate these skills in SAU and wards on their turns. This article highlights how the proposed curriculum addresses the learning needs of final year medical students in their surgery rotation. The article will also summarize the critical appraisal process of our curriculum in the context of curriculum design theories. Finally, the article will highlight the quality assurance measures adhered to while developing the curriculum.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".