Sound Practices: An Exploratory Study of Building and Monitoring Multiple-Choice Exams at Canadian Undergraduate Medical Education Programs
Bibliographic record
Abstract
PURPOSE: Written examinations such as multiple-choice question (MCQ) exams are a key assessment strategy in health professions education (HPE), frequently used to provide feedback, to determine competency, or for licensure decisions. However, traditional psychometric approaches for monitoring the quality of written exams, defined as items that are discriminant and contribute to increase the overall reliability and validity of the exam scores, usually warrant larger samples than are typically available in HPE contexts. The authors conducted a descriptive exploratory study to document how undergraduate medical education (UME) programs ensure the quality of their written exams, particularly MCQs. METHOD: Using a qualitative descriptive methodology, the authors conducted semistructured interviews with 16 key informants from 10 Canadian UME programs in 2018. Interviews were transcribed, anonymized, coded by the primary investigator, and co-coded by a second team member. Data collection and analysis were conducted iteratively. Research team members engaged in analysis across phases, and consensus was reached on the interpretation of findings via group discussion. RESULTS: Participants focused their answers around MCQ-related practices, reporting using several indicators of quality such as alignment between items and course objectives and psychometric properties (difficulty and discrimination). The authors clustered findings around 5 main themes: processes for creating MCQ exams, processes for building quality MCQ exams, processes for monitoring the quality of MCQ exams, motivation to build quality MCQ exams, and suggestions for improving processes. CONCLUSIONS: Participants reported engaging multiple strategies to ensure the quality of MCQ exams. Assessment quality considerations were integrated throughout the development and validation phases, reflecting recent work regarding validity as a social imperative.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".