Training Standardized Patients for a High‐Stakes Clinical Performance Examination in the California Consortium for the Assessment of Clinical Competence
Bibliographic record
Abstract
The use of standardized patients in teaching and assessment of clinical skills has become more ubiquitous in medical schools in the United States and Canada since Dr Howard Barrows introduced the first standardized patient at the University of Southern California in 1963. This increased usage is also due to the fact that the national licensing examination in the United States, includes a component to assess the clinical skills of the learners (United States Medical Licensure Examination Step 2 CS). The eight medical schools in California form a Consortium for the Assessment of Clinical Competence, which enables them to develop and implement a common clinical assessment tool, the Clinical Performance Examination (CPX), for final year medical students across the state. All medical schools in the Consortium share the same standardized patient cases and checklists. The standardization of training across the eight medical schools is presented. This paper describes the methods that have been used to train the SPs so that they can portray the gestalt of the patient, provide effective feedback, and reliably evaluate the students at the Keck School of Medicine of the University of Southern California. Quality assurance measures to ensure both performance and checklist accuracy are also described.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".