Poorly performing physicians: Does the script concordance test detect bad clinical reasoning? *
Bibliographic record
Abstract
INTRODUCTION: Evaluation of poorly performing physicians is a worldwide concern for licensing bodies. The Collège des Médecins du Québec currently assesses the clinical competence of physicians previously identified with potential clinical competence difficulties through a day-long procedure called the Structured Oral Interview (SOI). Two peer physicians produce a qualitative report. In view of remediation activities and the potential for legal consequences, more information on the clinical reasoning process (CRP) and quantitative data on the quality of that process is needed. This study examines the Script Concordance Test (SCT), a tool that provides a standardized and objective measure of a specific dimension of CRP, clinical data interpretation (CDI), to determine whether it could be useful in that endeavor. METHODS: Over a 2-year period, 20 family physicians took, in addition to the SOI, a 1-hour paper-and-pencil SCT. Three evaluators, blind as to the purpose of the experiment, retrospectively reviewed SOI reports and were asked to estimate clinical reasoning quality. Subjects were classified into 2 groups (below and above median of the score distribution) for the 2 assessment methods. Agreement between classifications is estimated with the use of the Kappa coefficient. RESULTS: Intraclass correlation for SOI was 0.89. Cronbach alpha coefficient for the SCT was 0.90. Agreement between methods was found for 13 participants (Kappa: 0.30, P = 0.18), but 7 out of 20 participants were classified differently in both methods. All participants but 1 had SCT scores below 2 SD of panel mean, thus indicating serious deficiencies in CDI. DISCUSSION: The finding that the majority of the referred group did so poorly on CDI tasks has great interest for assessment as well as for remediation. In remediation of prescribing skills, adding SCT to SOI is useful for assessment of cognitive reasoning in poorly performing physicians. The structured oral interview should be improved with more precise reporting by those who assess the clinical reasoning process of examinees, and caution is recommended in interpreting SCT scores; they reflect only a part of the reasoning process.
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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 | 0.010 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".