prepOSCE: A Virtual, Scalable Solution to Prepare Residents for Their OSCE Examination
Bibliographic record
Abstract
ABSTRACT: The Royal College Comprehensive Objective Examination in Neurology provides certification for Canadian neurologists and consists of a written examination and the Observed Structured Clinical Encounter (OSCE). The OSCE portion of the certification involves residents visiting several patient stations where they address case scenarios with an examiner. Unfortunately, residents lack exam preparation time due to demanding work hours. In response to resident needs, we created a novel, virtual preparation OSCE program – “prepOSCE” – and evaluated its efficacy. The prepOSCE program employed a proprietary virtual solution from CTC Communications Corp. Ten virtual sessions accommodated 70 residents totally. Seven Canadian physicians and two co-chairs created case scenarios for the stations. On session day, seven residents arrived in a virtual plenary room for briefing followed by assignment to a station by CTC. Residents then moved virtually through prepOSCE stations a different examiner and case scenario in each. Following their session, residents and evaluators were surveyed to capture experiences. The average program rating was 4.22 out of 5 (n= 36 residents of 70 residents who participated in the program) and 4.35 (n= 17 evaluators). Ninety-two percent of residents agreed or strongly agreed that they would recommend this program to their peers; they would like prepOSCE to continue next year; and the program was relevant and added value to their studies. The positive feedback received from prepOSCE participants indicates there is a need for a program like prepOSCE. This model has potential for expansion and it is hoped that specialties outside of neurology could benefit from a similar program.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".