A layered analysis of self-explanation and structured reflection to support clinical reasoning in medical students
Bibliographic record
Abstract
Self-explanation and structured reflection have been studied independently with results suggesting that both learning interventions can effectively support medical students' clinical reasoning development. Given this evidence, medical schools may want/begin to implement these interventions in their curricula. Implementing educational interventions requires educators to maintain the core philosophy and principles of the interventions intact while adjusting implementation techniques to the specificities of individual learning contexts. Educational scholars have yet to explicitly articulate the philosophy, principles and techniques of self-explanation and structured reflection. Without such descriptions, educators risk failing to realize self-explanation's and structured reflection's effect to support students' clinical reasoning skill development in their implementations. Relying on the layered analysis approach, we articulate the philosophy, principles and techniques of self-explanation and structured reflection. This description is framed within the context of an actual implementation to illustrate the philosophies underpinning self-explanation and structured reflection, the principles that realize those philosophies, and the techniques that can be used to enact those principles. Building on the similarities between self-explanation and structured reflection, while also harnessing their differences, we identify why and how these interventions can be combined in a single implementation, while preserving their philosophies and principles. The layered analysis of self-explanation and structured reflection offers essential insights into the underpinnings of these interventions. They are articulated in this manuscript in hopes that other scholars will continue to refine these descriptions thereby facilitating effective use of self-explanation and structured reflection for clinical reasoning development.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".