Practice makes perfect: the development of a medical student-led crowdsourced question bank for self-study in undergraduate medical education
Bibliographic record
Abstract
Implication Statement The development of multiple-choice questions (MCQs) for undergraduate medical education study purposes is resource intensive. Commercially available question banks are typically expensive, only available in English, and may not be aligned with medical school learning objectives. Here, we introduce The Ottawa Question Bank: a student-led, bilingual study resource curated to a Canadian undergraduate medicine curriculum (www.theottawaquestionbank.ca). In total, 205 medical students wrote and edited 4438 original MCQs linked to objectives from the University of Ottawa undergraduate medical education curriculum. The project has received positive feedback from both developers and users. Our experience suggests that involving medical students in MCQ development is feasible and can result in the rapid creation of a low-cost, high-quality study resource curated to a program’s learning objectives. The platform outlined here can be used as a model for other medical schools and professional degree programs to develop their own question banks, including pharmacy, dentistry, nursing, and physiotherapy. Interested programs are encouraged to contact our team for collaborative opportunities.
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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 | 0.078 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.015 |
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".