Construction and utilization of a script concordance test as an assessment tool for dcem3 (5th year) medical students in rheumatology
Bibliographic record
Abstract
BACKGROUND: The script concordance test (SCT) is a method for assessing clinical reasoning of medical students by placing them in a context of uncertainty such as they will encounter in their future daily practice. Script concordance testing is going to be included as part of the computer-based national ranking examination (iNRE).This study was designed to create a script concordance test in rheumatology and use it for DCEM3 (fifth year) medical students administered via the online platform of the Clermont-Ferrand medical school. METHODS: Our SCT for rheumatology teaching was constructed by a panel of 19 experts in rheumatology (6 hospital-based and 13 community-based). One hundred seventy-nine DCEM3 (fifth year) medical students were invited to take the test. Scores were computed using the scoring key available on the University of Montreal website. Reliability of the test was estimated by the Cronbach alpha coefficient for internal consistency. RESULTS: The test comprised 60 questions. Among the 26 students who took the test (26/179: 14.5%), 15 completed it in its entirety. The reference panel of rheumatologists obtained a mean score of 76.6 and the 15 students had a mean score of 61.5 (p = 0.001). The Cronbach alpha value was 0.82. CONCLUSIONS: An online SCT can be used as an assessment tool for medical students in rheumatology. This study also highlights the active participation of community-based rheumatologists, who accounted for the majority of the 19 experts in the reference panel.A script concordance test in rheumatology for 5th year medical students.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".