Teaching and learning clinical reasoning: a teacher's toolbox to meet different learning needs
Bibliographic record
Abstract
Clinical reasoning is an essential part of medical practice and therefore should be an important part of clinical teaching. However, it has been and is still a challenge for clinical teachers to support learners in the development of their clinical reasoning skills. As learners progress in clerkship, so do their learning needs. As a result, teachers need multiple tools to foster the development of clinical reasoning and should know when and why to use them. This article presents tools gathered as part of a clinical teacher's toolbox aimed at coaching learners towards the next step in their clinical reasoning development as well as helping teachers diagnose clinical reasoning difficulties and meet the diverse learning needs of their learners. The article focuses on three tools that were developed by faculty at the University of Sherbrooke Faculty of Medicine and Health Sciences: the iSNAPPS-OMP Technique, the Anticipatory Supervision Technique and the Clinical Sudoku or table of discriminating clues. This article uses the term 'tools' as a generic expression to signify 'items in a toolbox'. It includes all kinds of resources (techniques, strategies, models) that were gathered to help clinical teachers with the teaching of clinical reasoning.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".