Assessing the Measurement Properties of a Clinical Reasoning Exercise
Bibliographic record
Abstract
BACKGROUND: A challenge for Problem-Based Learning (PBL) schools is to introduce reliable, valid, and cost-effective testing methods into the curriculum in such a way as to maximize the potential benefits of PBL while avoiding problems associated with assessment techniques like multiple-choice question, or MCQ, tests. PURPOSE: We document the continued development of an exam that was designed to satisfy the demands of both PBL and the scientific principles of measurement. METHODS: A total of 102 medical students wrote a clinical reasoning exercise (CRE) as a requirement for two consecutive units of instruction. Each CRE consisted of a series of 18 short clinical problems designed to assess a student's knowledge of the mechanism of diseases that were covered in three subunits located within each unit. Responses were scored by a student's tutor and a 2nd crossover tutor. RESULTS: Generalizability coefficients for raters, subunits, and individual problems were low, but the reliability of the overall test scores and the reliability of the scores across 2 units of instruction were high. Subsequent analyses found that the crossover tutor's ratings were lower than the ratings provided by one's own tutor, and the CRE correlated with the biology component of a progress test. CONCLUSION: The magnitude of the generalizability coefficients demonstrates that the CRE is capable of detecting differences in reasoning across knowledge domains and is therefore a useful evaluation tool.
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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 | 0.047 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".