Drawing Boundaries: The Difficulty in Defining Clinical Reasoning
Bibliographic record
Abstract
Clinical reasoning is an essential component of a health professional's practice. Yet clinical reasoning research has produced a notably fragmented body of literature. In this article, the authors describe the pause-and-reflect exercise they undertook during the execution of a synthesis of the literature on clinical reasoning in the health professions. Confronted with the challenge of establishing a shared understanding of the nature and relevant components of clinical reasoning, members of the review team paused to independently generate their own personal definitions and conceptualizations of the construct. Here, the authors describe the variability of definitions and conceptualizations of clinical reasoning present within their own team. Drawing on an analogy from mathematics, they hypothesize that the presence of differing "boundary conditions" could help explain individuals' differing conceptualizations of clinical reasoning and the fragmentation at play in the wider sphere of research on clinical reasoning. Specifically, boundary conditions refer to the practice of describing the conditions under which a given theory is expected to hold, or expected to have explanatory power. Given multiple theoretical frameworks, research methodologies, and assessment approaches contained within the clinical reasoning literature, different boundary conditions are likely at play. Open acknowledgment of different boundary conditions and explicit description of the conceptualization of clinical reasoning being adopted within a given study would improve research communication, support comprehensive approaches to teaching and assessing clinical reasoning, and perhaps encourage new collaborative partnerships among researchers who adopt different boundary conditions.
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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 | 0.208 | 0.408 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.009 | 0.060 |
| Scholarly communication | 0.022 | 0.038 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".