Can we share questions? Performance of questions from different question banks in a single medical school
Bibliographic record
Abstract
BACKGROUND: To use progress testing, a large bank of questions is required, particularly when planning to deliver tests over a long period of time. The questions need not only to be of good quality but also balanced in subject coverage across the curriculum to allow appropriate sampling. Hence as well as creating its own questions, an institution could share questions. Both methods allow ownership and structuring of the test appropriate to the educational requirements of the institution. METHOD: Peninsula Medical School (PMS) has developed a mechanism to validate questions written in house. That mechanism can be adapted to utilise questions from an International question bank International Digital Electronic Access Library (IDEAL) and another UK-based question bank Universities Medical Assessment Partnership (UMAP). These questions have been used in our progress tests and analysed for relative performance. RESULTS: Data are presented to show that questions from differing sources can have comparable performance in a progress testing format. CONCLUSION: There are difficulties in transferring questions from one institution to another. These include problems of curricula and cultural differences. Whilst many of these difficulties exist, our experience suggests that it only requires a relatively small amount of work to adapt questions from external question banks for effective use. The longitudinal aspect of progress testing (albeit summatively) may allow more flexibility in question usage than single high stakes exams.
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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 | 0.001 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 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".