The influence of medical students’ self‐explanations on diagnostic performance
Bibliographic record
Abstract
CONTEXT: Skill in clinical reasoning is a highly valued attribute of doctors, but instructional approaches to foster medical students' clinical reasoning skills remain scarce. Self-explanation is an instructional procedure, the positive effects of which on learning have been demonstrated in a variety of domains, but which remain largely unexplored in medical education. OBJECTIVES: The purpose of this study was to investigate the effects of self-explanation on students' learning of clinical reasoning during clerkships and to examine whether these effects are affected by topic familiarity. METHODS: An experimental study with a training phase and an assessment phase was conducted with 36 Year 3 medical students, randomly assigned to one of two groups. In the training phase, students solved 12 clinical cases (four cases on a less familiar topic; four on a more familiar topic; four on filler topics), either generating self-explanations (n = 18) or not (n = 18). The self-explanations were generated after minimal instructions and no feedback was provided to students. One week later, in the assessment phase, students were requested to diagnose 12 different, more difficult cases, similarly distributed among the same more familiar topic, less familiar topic and filler topics, and their diagnostic performance was assessed. RESULTS: In the training phase the performance of the two groups did not differ. However, in the assessment phase 1 week later, a significant interaction was found between self-explanation and case topic familiarity (F(1,34) = 6.18, p < 0.05). Students in the self-explanation condition, compared with those in the control condition, demonstrated better diagnostic performance on subsequent clinical cases, but this effect emerged only for cases concerning the less familiar topic. CONCLUSIONS: The present study shows the beneficial influence of generating self-explanations when dealing with less familiar clinical contexts. Generating self-explanations without feedback resulted in better diagnostic performance than in the control group at 1 week after the intervention.
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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 | 0.229 |
| 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) | 0.001 | 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".