COMBINING STANDARDIZED PATIENTS WITH SIMULATION TECHNOLOGY AT A NATIONAL SPECIALTY EXAMINATION
Bibliographic record
Abstract
Standardized patients (SPs), often lacking physical abnormalities, are frequently employed in high-stakes assessments of clinical competence. Incorporating simulation technology with SP assessments offers the advantage of standardizing patient abnormalities, provided that the assessment process demonstrates acceptable validity evidence. The objective of this study was to develop, implement, and validate OSCE-format stations that combined simulation technology with SPs for the 2004 Royal College of Physicians and Surgeons of Canada’s Comprehensive Objective Examination in Internal Medicine. Digital audio-video simulations of cardiology and neurology physical abnormalities were included in 11 SP OSCE-format stations. Two examiners evaluated each candidate’s performance. Reliability and validity data of the stations was assessed. Examiners were tested on a sub-set of the audio-video simulations. Inter-rater reliability for the audio-video simulations ranged from 0.83–0.85. Construct validity was addressed by assessing candidates’ and examiners’ diagnostic accuracy for a sub-set of simulations (mean score 0.79 +/- 0.26 and 0.84 +/- 0.24, respectively). Post-examination surveys confirmed face validity. Incorporating simulation technology with an SP assessment represents a feasible and valid approach to the assessment of clinical competence in a high-stakes setting. Conflict of Interest: Authors indicated they have nothing to disclose.
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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 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".