The Use of a Modified Script Concordance Test in Clinical Rounds to Foster and Assess Clinical Reasoning Skills
Bibliographic record
Abstract
The development of clinical reasoning skills is a high priority during clinical service, but an unpredictable case load and limited time for formal instruction makes it challenging for faculty to foster and assess students' individual clinical reasoning skills. We developed an assessment for learning activity that helps students build their clinical reasoning skills based on a modified version of the script concordance test (SCT). To modify the standard SCT, we simplified it by limiting students to a 3-point Likert scale instead of a 5-point scale and added a free-text box for students to provide justification for their answer. Students completed the modified SCT during clinical rounds to prompt a group discussion with the instructor. Student feedback was positive, and the instructor gained valuable insight into the students' thought process. A modified SCT can be adopted as part of a multimodal approach to teaching on the clinic floor. The purpose of this article is to describe our modifications to the standard SCT and findings from implementation in a clinical rounds setting as a method of formative assessment for learning and developing clinical reasoning skills.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".