Use of "Standardized Examinees" to Screen for Standardized-Patient Scoring Bias in a Clinical Skills Examination
Bibliographic record
Abstract
BACKGROUND: Clinical skills examinations using standardized patients (SPs) are important in documenting the proficiency of trainees. "Standardized examinees" (SEs) are individuals trained to a specific level of performance; they can be used as internal controls in a high-stakes, clinical skills examination. PURPOSE: The purpose of this study was to determine whether SEs can be trained to portray a specified level of confidence and whether SPs' checklist scoring is affected by the personal manner of the examinee. METHODS: Eight SEs were trained as "students" and trained to achieve a failing score on six cases in an National Board of Medical Examiners (NBME) Prototype Clinical Skills Examination. Four SEs were coached to be confident in manner, and 4 were coached to be insecure. Checklist scores were compared. Seven lay reviewers scored the SEs as confident or insecure on a behavioral assessment form. RESULTS: SEs were not detected as simulations. There was no difference between the checklist scores of confident versus insecure SEs, but their manner was rated as significantly different on all scales in the behavioral assessment. CONCLUSIONS: SEs can be trained to a specified performance level and a desired level of confidence. In this small study, personal manner did not affect SPs' checklist scoring. The use of the SEs provides a mechanism to screen for bias in high-stakes SP examinations.
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 | 0.019 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".