Using standardized patients to measure professional performance of physicians
Bibliographic record
Abstract
OBJECTIVE: To determine the nature of inaccuracies likely to occur when standardized patients (SPs) are used to measure physician behaviour and to evaluate the potential impact of these inaccuracies on estimates of physician performance. DESIGN: Secondary analysis from a randomized controlled trial. SETTING: Family physicians' offices. STUDY PARTICIPANTS: Eighteen individuals, each portraying one of two patient scenarios, made a total of 179 visits to 92 family physicians who were participating in a separate randomized controlled trial to evaluate the impact of an educational workshop on implementation of preventive guidelines. MAIN OUTCOME MEASURES: Accuracy of SPs' portrayal of the assigned scenarios and accuracy of their coding of physician performance, determined on the basis of audiotapes of the visits and correlated with indicators of physicians' preventive practices. RESULTS: Accuracy of portrayal of the patient scenario was 84.8% for the male SPs and 93.5% for the female SPs. Inaccuracies in portrayal had no impact on physician performance scores. Accuracy of coding of physician performance was 90.5% for the female SPs (kappa = 0.66) and 90.1% for the male SPs (kappa = 0.68). Coding inaccuracies occurred most frequently for assessment of alcohol consumption and advice against smoking. CONCLUSION: SPs can provide valid information about physicians' professional performance. However, standardization of their activities must not be taken for granted. It may be more difficult to obtain standardized coding for counselling activities, an aspect of physician visits for which SPs are particularly appropriate.
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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 | 0.030 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".