The terminology of clinical reasoning in health professions education: Implications and considerations
Bibliographic record
Abstract
Introduction: Clinical reasoning is considered to be at the core of health practice. Here, we report on the diversity and inferred meanings of the terms used to refer to clinical reasoning and consider implications for teaching and assessment.Methods: In the context of a Best Evidence Medical Education (BEME) review of 625 papers drawn from 18 health professions, we identified 110 terms for clinical reasoning. We focus on iterative categorization of these terms across three phases of coding and considerations for how terminology influences educational practices.Results: Following iterative coding with 5 team members, consensus was possible for 74, majority coding was possible for 16, and full team disagreement existed for 20 terms. Categories of terms included: purpose/goal of reasoning, outcome of reasoning, reasoning performance, reasoning processes, reasoning skills, and context of reasoning.Discussion: Findings suggest that terms used in reference to clinical reasoning are non-synonymous, not uniformly understood, and the level of agreement differed across terms. If the language we use to describe, to teach, or to assess clinical reasoning is not similarly understood across clinical teachers, program directors, and learners, this could lead to confusion regarding what the educational or assessment targets are for “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 | 0.227 | 0.338 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.009 | 0.010 |
| Research integrity | 0.007 | 0.016 |
| 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".