Mapping clinical reasoning literature across the health professions: a scoping review
Bibliographic record
Abstract
BACKGROUND: Clinical reasoning is at the core of health professionals' practice. A mapping of what constitutes clinical reasoning could support the teaching, development, and assessment of clinical reasoning across the health professions. METHODS: We conducted a scoping study to map the literature on clinical reasoning across health professions literature in the context of a larger Best Evidence Medical Education (BEME) review on clinical reasoning assessment. Seven databases were searched using subheadings and terms relating to clinical reasoning, assessment, and Health Professions. Data analysis focused on a comprehensive analysis of bibliometric characteristics and the use of varied terminology to refer to clinical reasoning. RESULTS: Literature identified: 625 papers spanning 47 years (1968-2014), in 155 journals, from 544 first authors, across eighteen Health Professions. Thirty-seven percent of papers used the term clinical reasoning; and 110 other terms referring to the concept of clinical reasoning were identified. Consensus on the categorization of terms was reached for 65 terms across six different categories: reasoning skills, reasoning performance, reasoning process, outcome of reasoning, context of reasoning, and purpose/goal of reasoning. Categories of terminology used differed across Health Professions and publication types. DISCUSSION: Many diverse terms were present and were used differently across literature contexts. These terms likely reflect different operationalisations, or conceptualizations, of clinical reasoning as well as the complex, multi-dimensional nature of this concept. We advise authors to make the intended meaning of 'clinical reasoning' and associated terms in their work explicit in order to facilitate teaching, assessment, and research communication.
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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 | 0.071 | 0.310 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.110 | 0.079 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".