Within-Session Score Gains for Repeat Examinees on a Standardized Patient Examination
Bibliographic record
Abstract
PURPOSE: Previous studies on standardized patient (SP) exams reported score gains both across attempts when examinees failed and retook the exam and over multiple SP encounters within a single exam session. The authors analyzed the within-session score gains of examinees who repeated the United States Medical Licensing Examination Step 2 Clinical Skills to answer two questions: How much do scores increase within a session? Can the pattern of increasing first-attempt scores account for across-session score gains? METHOD: Data included encounter-level scores for 2,165 U.S. and Canadian medical students and graduates who took Step 2 Clinical Skills twice between April 1, 2005 and December 31, 2010. The authors modeled examinees' score patterns using smoothing and regression techniques and applied statistical tests to determine whether the patterns were the same or different across attempts. In addition, they tested whether any across-session score gains could be explained by the first-attempt within-session score trajectory. RESULTS: For the first and second attempts, the authors attributed examinees' within-session score gains to a pattern of score increases over the first three to six SP encounters followed by a leveling off. Model predictions revealed that the authors could not attribute the across-session score gains to the first-attempt within-session score gains. CONCLUSIONS: The within-session score gains over the first three to six SP encounters of both attempts indicate that there is a temporary "warm-up" effect on performance that "resets" between attempts. Across-session gains are not due to this warm-up effect and likely reflect true improvement in performance.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".