The Role of Standardized Patient and Trainer Training in Quality Assurance for a High‐Stakes Clinical Skills Examination
Bibliographic record
Abstract
For over 30 years, medical educators have used standardized patients (SPs), laypersons trained to portray a patient case in a realistic manner, to teach and to assess clinical skills. All medical schools in the US have SP programs in place, and the US and Canada require national examinations using SPs to assess the competency of those wishing to obtain licensure to practice medicine in these countries. To ensure a valid and reliable examination, unwanted variance that can be introduced by SP performance must be addressed. The goal of SP training is to imbue the SP with the characteristics, mannerisms and history of a real patient so that the portrayal is consistent and accurate. The challenge is to ensure consistent portrayal of each case with sufficient realism to elicit the expected clinical performance and to ensure standardized SP performance across multiple examinees. This paper considers the quality assurance methods applied to training the SP trainers and the protocols used to train the SPs, to ensure that the SP performances are sufficiently accurate and standardized, and that the evaluators completing the checklists and scales used for scoring do so correctly and consistently.
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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 | 0.402 | 0.384 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".