Evidence of Construct Validity of Computer-Based Tests for Clinical Reasoning: Instrument Validation Study
Bibliographic record
Abstract
BACKGROUND: Clinical reasoning (CR) is a fundamental skill for all medical students. In our medical education system, however, there are shortcomings in the conventional methods of teaching CR. New technology is needed to enhance our CR teaching, especially as we are facing an influx of new health trainees. China Medical University (CMU), in response to this need, has developed a computer-based CR training system (CMU-CBCRT). OBJECTIVE: We aimed to find evidence of construct validity of the CMU-CBCRT. METHODS: We recruited 385 students from fifth year undergraduates to postgraduate year (PGY) 3 to complete the test on CMU-CBCRT. The known-groups technique was used to evaluate the construct validity of the CBCRT by comparing the test scores among 4 training levels (fifth year MD, PGY-1, PGY-2, and PGY-3). RESULTS: We found that test scores increased with years of training. Significant differences were found in the test scores on information collection, diagnosis, and treatment and total scores among different training years of participants. However, significant results were not found for treatment errors. CONCLUSIONS: We provided evidence of construct validity of the CMU-CBCRT, which could determine the CR skills of medical students at varying early stage in their careers.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".