Can standardized patients replace physicians as OSCE examiners?
Bibliographic record
Abstract
BACKGROUND: To reduce inter-rater variability in evaluations and the demand on physician time, standardized patients (SP) are being used as examiners in OSCEs. There is concern that SP have insufficient training to provide valid evaluation of student competence and/or provide feedback on clinical skills. It is also unknown if SP ratings predict student competence in other areas. The objectives of this study were: to examine student attitudes towards SP examiners; to compare SP and physician evaluations of competence; and to compare predictive validity of these scores, using performance on the multiple choice questions examination (MCQE) as the outcome variable. METHODS: This was a cross-sectional study of third-year medical students undergoing an OSCE during the Internal Medicine clerkship rotation. Fifty-two students rotated through 8 stations (6 physician, 2 SP examiners). Statistical tests used were Pearson's correlation coefficient, two-sample t-test, effect size calculation, and multiple linear regression. RESULTS: Most students reported that SP stations were less stressful, that SP were as good as physicians in giving feedback, and that SP were sufficiently trained to judge clinical skills. SP scored students higher than physicians (mean 90.4% +/- 8.9 vs. 82.2% +/- 3.7, d = 1.5, p < 0.001) and there was a weak correlation between the SP and physician scores (coefficient 0.4, p = 0.003). Physician scores were predictive of summative MCQE scores (regression coefficient = 0.88 [0.15, 1.61], P = 0.019) but there was no relationship between SP scores and summative MCQE scores (regression coefficient = -0.23, P = 0.133). CONCLUSION: These results suggest that SP examiners are acceptable to medical students, SP rate students higher than physicians and, unlike physician scores, SP scores are not related to other measures of competence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".