Advancing the assessment of clinical reasoning across the health professions: Definitional and methodologic recommendations
Bibliographic record
Abstract
The importance of clinical reasoning in patient care is well-recognized across all health professions. Validity evidence supporting high quality clinical reasoning assessment is essential to ensure health professional schools are graduating learners competent in this domain. However, through the course of a large scoping review, we encountered inconsistent terminology for clinical reasoning and inconsistent reporting of methodology, reflecting a somewhat fractured body of literature on clinical reasoning assessment. These inconsistencies impeded our ability to synthesize across studies and appropriately compare assessment tools. More specifically, we encountered: 1) a wide array of clinical reasoning-like terms that were rarely defined or informed by a conceptual framework, 2) limited details of assessment methodology, and 3) inconsistent reporting of the steps taken to establish validity evidence for clinical reasoning assessments. Consolidating our experience in conducting this review, we provide recommendations on key definitional and methodologic elements to better support the development, description, study, and reporting of clinical reasoning assessments.
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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 | 0.383 | 0.611 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.015 | 0.029 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".