Clinical Reasoning Assessment Methods: A Scoping Review and Practical Guidance
Bibliographic record
Abstract
PURPOSE: An evidence-based approach to assessment is critical for ensuring the development of clinical reasoning (CR) competence. The wide array of CR assessment methods creates challenges for selecting assessments fit for the purpose; thus, a synthesis of the current evidence is needed to guide practice. A scoping review was performed to explore the existing menu of CR assessments. METHOD: Multiple databases were searched from their inception to 2016 following PRISMA guidelines. Articles of all study design types were included if they studied a CR assessment method. The articles were sorted by assessment methods and reviewed by pairs of authors. Extracted data were used to construct descriptive appendixes, summarizing each method, including common stimuli, response formats, scoring, typical uses, validity considerations, feasibility issues, advantages, and disadvantages. RESULTS: A total of 377 articles were included in the final synthesis. The articles broadly fell into three categories: non-workplace-based assessments (e.g., multiple-choice questions, extended matching questions, key feature examinations, script concordance tests); assessments in simulated clinical environments (objective structured clinical examinations and technology-enhanced simulation); and workplace-based assessments (e.g., direct observations, global assessments, oral case presentations, written notes). Validity considerations, feasibility issues, advantages, and disadvantages differed by method. CONCLUSIONS: There are numerous assessment methods that align with different components of the complex construct of CR. Ensuring competency requires the development of programs of assessment that address all components of CR. Such programs are ideally constructed of complementary assessment methods to account for each method's validity and feasibility issues, advantages, and disadvantages.
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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 | 0.154 | 0.295 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.053 | 0.041 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".