Stability of clinical reasoning assessment results with the Script Concordance test across two different linguistic, cultural and learning environments
Bibliographic record
Abstract
The Script Concordance (SC) test is designed to measure the organization of knowledge that allows interpretation of data in clinical reasoning. An originality of the test is that answer keys use an aggregate scoring method based on answers given by a panel of experts. Previous studies have shown that the SC test has good construct validity. This study, done in urology, explores (1) the stability of the construct validity of the test across two different linguistic and learning environments and (2) the effect of the use of experts who belong to different environments. An 80-item SC test was administered to participants from a French and a Canadian university. Two levels of experience were tested: 25 residents in urology (11 from the French university and 14 from the Canadian university) and 23 students (15 from the French faculty, eight from the Canadian faculty). Reliability analysis was studied with Cronbach's alpha coefficient. Scores between groups were compared by analysis of variance. Reliability coefficient of the 80 items test was 0.794 for the French participants and 0.795 for the Canadian participants. Scores increased with clinical experience in urology in the two sites. Candidates obtained higher scores when correction was done using the answer key provided by the experts from the same country. These data support the stability of the construct validity of the tool across different learning environments.
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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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".