Constructing a question bank based on script concordance approach as a novel assessment methodology in surgical education
Bibliographic record
Abstract
BACKGROUND: Script Concordance Test (SCT) is a new assessment tool that reliably assesses clinical reasoning skills. Previous descriptions of developing SCT-question banks were merely subjective. This study addresses two gaps in the literature: 1) conducting the first phase of a multistep validation process of SCT in Plastic Surgery, and 2) providing an objective methodology to construct a question bank based on SCT. METHODS: After developing a test blueprint, 52 test items were written. Five validation questions were developed and a validation survey was established online. Seven reviewers were asked to answer this survey. They were recruited from two countries, Saudi Arabia and Canada, to improve the test's external validity. Their ratings were transformed into percentages. Analysis was performed to compare reviewers' ratings by looking at correlations, ranges, means, medians, and overall scores. RESULTS: Scores of reviewers' ratings were between 76% and 95% (mean 86% ± 5). We found poor correlations between reviewers (Pearson's: +0.38 to -0.22). Ratings of individual validation questions ranged between 0 and 4 (on a scale 1-5). Means and medians of these ranges were computed for each test item (mean: 0.8 to 2.4; median: 1 to 3). A subset of test items comprising 27 items was generated based on a set of inclusion and exclusion criteria. CONCLUSION: This study proposes an objective methodology for validation of SCT-question bank. Analysis of validation survey is done from all angles, i.e., reviewers, validation questions, and test items. Finally, a subset of test items is generated based on a set of criteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.143 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".