Self‐explanation in learning clinical reasoning: the added value of examples and prompts
Bibliographic record
Abstract
CONTEXT: Recent studies suggest that self-explanation (SE) while diagnosing cases fosters the development of clinical reasoning in medical students; however, the conditions that optimise the impact of SE remain unknown. The example-based learning framework justifies an exploration of students' use of their own SEs combined with the study of examples. This study aimed to assess the impact on medical students' diagnostic performance of: (i) combining students' SEs with their listening to examples of residents' SEs, and (ii) the addition of prompts (specific questions) while working with examples. METHODS: This study consisted of a training phase and an assessment phase conducted 1 week later. In the training phase, 54 Year 3 medical students were randomly assigned to one of three groups. In all groups, students first solved four clinical cases using SE. Subsequently, Group 1 listened to examples of residents' SEs with prompts; Group 2 listened to examples of residents' SEs without prompts, and the control group solved word puzzles. Then, all students again solved the same four cases. One week later, all students solved four similar and four different cases. Students' diagnostic performance and diagnostic accuracy scores were assessed for each case at each time-point. RESULTS: Although all groups' diagnostic accuracy scores on similar cases improved significantly between the training and the assessment phase, Group 1 showed a significantly higher diagnostic performance score after 1 week than the control group (p = 0.037). On different cases, Group 1 obtained significantly higher diagnostic accuracy (p = 0.011) and diagnostic performance (p < 0.001) scores than the control group and a significantly higher diagnostic performance score than Group 2 (p = 0.018). CONCLUSIONS: Self-explanation seems to be an effective technique to help medical students learn clinical reasoning. Its impact is increased significantly by combining it with examples of residents' SEs and prompts. Although students' exposure to examples of clinical reasoning is important, their 'active processing' of these examples appears to be critical to their learning from them.
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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 | 0.003 | 0.255 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".