A Developmental Approach to Evaluating Competence in Clinical Reasoning
Bibliographic record
Abstract
In the past two decades there has been tremendous worldwide interest in assessing the clinical competence of learners in medical education. This interest marks a philosophical shift toward greater objectivity, accountability, and predictive power in the evaluation of trainees. One of the core competencies in medical education is clinical reasoning. Because veterinary and human medical training share several similarities and differences, a review of the current state of clinical reasoning competency assessment in medical education may be useful for veterinary educators. This article covers the core competency of clinical reasoning (not other important competencies, such as physical examination or communication) and reviews research from medical education on the development of clinical reasoning and its implications for the transition from novice to expert. Four common stage-related learner difficulties are described: reduced knowledge, dispersed knowledge, tunnel vision, and the outsider. Specific approaches to measuring competence in clinical reasoning for each developmental level are recommended. Finally, two specific examples of evaluation based on a developmental approach to clinical expertise, the RIME (reporter, interpreter, manager, expert) system and the Script Concordance Test (SCT) methods, are discussed.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".