Comparative Effectiveness of OSCEs, Virtual OSCEs, and Traditional Written Testing Methods in Assessing Medical School Students’ Competencies: A Scoping Review Interim Report
Bibliographic record
Abstract
By the end of their medical school training, all physicians no matter which specialty they are heading for, must be competent to prescribe medications. Selecting and prescribing medications will be the most common therapeutic intervention they make in most careers. Multiple choice questions (MCQs), written answer questions, and standardized patients within Objective Structured Clinical Examinations (OSCEs) form the current backbone of medical and other health professional education assessment in Canada and internationally. However, OSCEs are very resource-intense, and written answer questions are used sparingly as they are time-consuming to mark. We will look at the following: which CanMEDS domains have OSCEs been shown to be a superior evaluation method compared to multiple choice or short/long answer written questions? And what are the advantages and disadvantages of virtual OSCEs (examinee, examiner, simulated patient all online and at distance from each other) compared to in-person OSCEs (all 3 in same room)?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.132 | 0.238 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".