Teaching clinical reasoning to undergraduate medical students by illness script method: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: The illness script method employs a theoretical outline (e.g., epidemiology, pathophysiology, signs and symptoms, diagnostic tests, interventions) to clarify how clinicians organized medical knowledge for clinical reasoning in the diagnosis domain. We hypothesized that an educational intervention based on the illness script method would improve medical students' clinical reasoning skills in the diagnosis domain. METHODS: This study is a randomized controlled trial involving 100 fourth-year medical students in Shiraz Medical School, Iran. Fifty students were randomized to the intervention group, who were taught clinical reasoning skills based on the illness script method for three diseases during one clinical scenario. Another 50 students were randomized to the control group, who were taught the clinical presentation based on signs and symptoms of the same three diseases as the intervention group. The outcomes of interest were learner satisfaction with the intervention and posttest scores on both an internally developed knowledge test and a Script Concordance Test (SCT). RESULTS: Of the hundred participating fourth-year medical students, 47 (47%) were male, and 53 (53%) were female. On the knowledge test, there was no difference in pretest scores between the intervention and control group, which suggested a similar baseline knowledge in both groups; however, posttest scores in the intervention group were (15.74 ± 2.47 out of 20) statistically significantly higher than the control group (14.38 ± 2.59 out of 20, P = 0.009). On the SCT, the mean score for the intervention group (6.12 ± 1.95 out of 10) was significantly higher than the control group (4.54 ± 1.56 out of 10; P = 0.0001). Learner satisfaction data indicated that the intervention was well-received by students. CONCLUSION: Teaching with the illness script method was an effective way to improve students' clinical reasoning skills in the diagnosis domain suggested by posttest and SCT scores for specific clinical scenarios. Whether this approach translates to improved generalized clinical reasoning skills in real clinical settings merits further study.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".