Web-Based Medical Examinations During the COVID-19 Era: Reconsidering Learning as the Main Goal of Examination
Bibliographic record
Abstract
Like other aspects of the health care system, medical education has been greatly affected by the COVID-19 pandemic. To follow the requirements of lockdown and virtual education, the performance of students has been evaluated via web-based examinations. Although this shift to web-based examinations was inevitable, other mental, educational, and technical aspects should be considered to ensure the efficiency and accuracy of this type of evaluation in this era. The easiest way to address the new challenges is to administer traditional questions via a web-based platform. However, more factors should be accounted for when designing web-based examinations during the COVID-19 era. This article presents an approach in which the opportunity created by the pandemic is used as a basis to reconsider learning as the main goal of web-based examinations. The approach suggests using open-book examinations, using questions that require high cognitive domains, using real clinical scenarios, developing more comprehensive examination blueprints, using advanced platforms for web-based questions, and providing feedback in web-based examinations to ensure that the examinees have acquired the minimum competency levels defined in the course objectives.
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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 | 0.028 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".