Exploring medical students’ use of principles of self-explanation and structured reflection during clerkship
Bibliographic record
Abstract
Background: While educators observe gaps in clerkship students' clinical reasoning (CR) skills, students report few opportunities to develop them. This study aims at exploring how students who used self-explanation (SE) and structured reflection (SR) for CR learning during preclinical training, applied these learning strategies during clerkship. Methods: We conducted an explanatory sequential mixed-methods study involving medical students. With a questionnaire, we asked students how frequently they adopted behaviours related to SE and SR during clerkship. Next, we conducted a focus group with students to explore why they adopted these behaviours. Results: Fifty-two of 198 students answered the questionnaire and five participated in a focus group. Specific behaviours adopted varied from 50% to 98%. We identified three themes about why students used these strategies: as "just in time" learning strategies; to deepen their understanding and identify gaps in knowledge; to develop a practical approach to diagnosis. A fourth theme related to the balance between learning and assessment and its consequence on adopting SE behaviours. Conclusions: Students having experienced SE and SR regularly in preclinical training tend to transpose these strategies into the clerkship providing them with a practical way to reflect deliberately and capture learning opportunities of the unpredictable clinical context.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".