Clinical reasoning difficulties: A taxonomy for clinical teachers
Bibliographic record
Abstract
BACKGROUND: Clinical reasoning is the cornerstone of medical practice. To date, there is no established framework regarding clinical reasoning difficulties, how to identify them, and how to remediate them. AIM: To identify the most common clinical reasoning difficulties as they present in residents' patient encounters, case summaries, or medical notes. To develop a guide to support medical educators' process of educational diagnosis and management in this area. METHODS: We used a participatory action research method. We carried out eight iterative reflective cycles with a group of clinical teachers. The repeated phases of experimentation and observation were conducted by participants in their own clinical teaching setting. Our findings were tested and validated on both an individual and collective basis. RESULTS: We found five categories of clinical reasoning difficulties as they present in the clinical teaching settings. We identified indicators for each. Indicators may be different depending on the type of supervision. These findings were assembled and organized to construct a guide for clinical teachers. CONCLUSIONS: The guide should assist clinical teachers in detecting clinical reasoning difficulties during clinical teaching and in providing remediation that is tailored to the specific difficulty identified. Its development furthers our understanding of clinical reasoning difficulties and provides a useful tool.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".