Reliability and validity of the script concordance test for postgraduate students of general practice
Bibliographic record
Abstract
BACKGROUND: The script concordance test (SCT) is a validated method of examining students' clinical reasoning. Medical students' professional skills are assessed during their postgraduate years as they study for a specialist qualification in general practice. However, no specific provision is made for assessing their clinical reasoning during their postgraduate study. OBJECTIVE: The aim was to demonstrate the reliability and validity of the SCT in general practice and to determine if this tool could be used to assess medical students' progress in acquiring clinical reasoning. METHODS: A 135-question SCT was administered to postgraduate medical students at the beginning of their first year of specialized training in general practice, and then every six months throughout their three-year training, as well as to a reference panel of 20 expert general practitioners. For score calculation, we used the combined scoring method as the calculator made available by the University of Montreal's School of Medicine in Canada. For the validity, student' scores were compared with experts, p <.05 was considered statistically significant. RESULTS: Ninety students completed all six assessments. The experts' mean score (76.7/100) was significantly higher than the students' score across all assessments (p <.001), with a Cronbach's alpha value of over 0.65 for all assessments. CONCLUSION: The SCT was found to be reliable and capable of discriminating between students and experts, demonstrating that this test is a valid tool for assessing clinical reasoning skills in general practice.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".