Evaluating the quality of multiple‐choice question pilot database: A global educator‐created tool for concept‐based pharmacology learning
Bibliographic record
Abstract
Abstract The Core Concepts of Pharmacology (CCP) initiative is developing educational resources to transform pharmacology education into a concept‐based approach. This study evaluated the quality of global educator‐created MCQs in generating items for the pharmacology concept inventory (PCI) instrument and developed as a resource for learning pharmacology fundamental concepts. A panel of 22 global pharmacology experts recruited from the CCP initiative research team participated in the MCQ pilot database design and evaluation. The quality analysis framework of the MCQs in the pilot database included four assessment tools: item writing guidelines (IWGs), Bloom's taxonomy, the CCP, and the MCQ design format. A two‐phase evaluation process was involved, including inter‐rater agreement on item quality, followed by resolving conflicts that occurred in quality assessment. The chi‐square ( χ 2 ) test of independence and Cramer's V correlation tests were utilized to measure the relationship among quality assessment attributes. About 200 MCQs were gathered and 98% underwent expert evaluation. Nearly 80% addressed one or more CCP, with 52% designed using a context‐dependent format. However, only 40% addressed higher levels of Bloom's cognitive domain and 10% adhered to all IWGs. A strong positive correlation was observed between the context‐based item format and its effectiveness in assessing the higher cognitive domain, the main CCP and improved IWGs adherence. Context‐based item construction can assess the higher cognitive skills and fundamental pharmacology concepts, showing potential for rigorous PCI development. The pilot database will store items to create the PCI, aiding the development of a concept‐based pharmacology curriculum.
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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 | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".