Competency-Based Medical Education
Bibliographic record
Abstract
BACKGROUND: Competency-based medical education as a resident-training format will move postgraduate training away from time-based training, to a model based on observable outcomes. The purpose of this study was to determine whether junior residents and senior residents could demonstrate clinical skills to a similar level, after a sports medicine rotation. METHODS: All residents undertaking a three-month sports medicine rotation had to pass an Objective Structured Clinical Examination. The stations tested the fundamentals of history-taking, examination, image interpretation, differential diagnosis, informed consent, and clinical decision-making. Performance at each station was assessed with a binary station-specific checklist and an overall global rating scale, in which 1 indicated novice, 2 indicated advanced beginner, 3 indicated competent, 4 indicated proficient, and 5 indicated expert. A global rating scale was also given for each domain of knowledge. RESULTS: Over eighteen months, thirty-nine residents (twenty-one junior residents and eighteen senior residents) and six fellows (for a total of forty-five participants) completed the examination. With regard to junior residents and senior residents, analysis using a two-tailed t test demonstrated a significant difference (p < 0.01) in both total checklist score and overall global rating scale; the mean total checklist score (and standard deviation) was 56.15% ± 10.99% for junior residents and 71.87% ± 8.94% for senior residents, and the mean global rating scale was 2.44 ± 0.55 for junior residents and 3.79 ± 0.49 for senior residents. There was a significant difference between junior residents and senior residents for each knowledge domain, with a significance of p < 0.05 for history-taking and p < 0.01 for the remainder of the domains. CONCLUSIONS: Despite intensive teaching within a competency-based medical education model, junior residents were not able to demonstrate knowledge as well as senior residents, suggesting that overall clinical experience is critically important for achieving competency as measured by the Objective Structured Clinical Examination.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".