Script concordance testing: From theory to practice: AMEE Guide No. 75
Bibliographic record
Abstract
The script concordance test (SCT) is used in health professions education to assess a specific facet of clinical reasoning competence: the ability to interpret medical information under conditions of uncertainty. Grounded in established theoretical models of knowledge organization and clinical reasoning, the SCT has three key design features: (1) respondents are faced with ill-defined clinical situations and must choose between several realistic options; (2) the response format reflects the way information is processed in challenging problem-solving situations; and (3) scoring takes into account the variability of responses of experts to clinical situations. SCT scores are meant to reflect how closely respondents' ability to interpret clinical data compares with that of experienced clinicians in a given knowledge domain. A substantial body of research supports the SCT's construct validity, reliability, and feasibility across a variety of health science disciplines, and across the spectrum of health professions education from pre-clinical training to continuing professional development. In practice, its performance as an assessment tool depends on careful item development and diligent panel selection. This guide, intended as a primer for the uninitiated in SCT, will cover the basic tenets, theoretical underpinnings, and construction principles governing script concordance testing.
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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 | 0.040 | 0.090 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.037 |
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".