The Script Concordance Test: A Tool to Assess the Reflective Clinician
Bibliographic record
Abstract
BACKGROUND: The Script Concordance (SC) test is a new assessment tool. It is designed to probe whether knowledge of examinees is efficiently organized for clinical actions. That kind of organization of knowledge is named a script. The SC test places examinees in written, but authentic, clinical situations in which they must interpret data to make decisions. PURPOSE: The SC test is designed to measure the degree of concordance that exists between examinees' scripts and scripts of a panel of experts. The objective of this article is to provide interested educators with the practical "how to" information needed to build and use an SC test. METHODS: The theoretical background of the SC test is described. The principles of construction of an SC test are presented, including the writing of clinical cases, the choice of item format, the validation of the test, and the elaboration of the scoring system. RESULTS: A series of studies have shown that the SC test has interesting psychometric properties, in terms of reliability, face validity, and construct validity. Results from these studies are succinctly presented and commented. CONCLUSION: The SC test is a simple and direct approach to testing organization and use of knowledge. It has the strong advantage for a testing method of being relatively easy to construct and use and to be machine-scorable. It can be either paper- or computer-based and can be used in undergraduate, postgraduate, or continuing medical education.
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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 | 0.016 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".