Viewpoint: Competency-Based Postgraduate Training: Can We Bridge the Gap between Theory and Clinical Practice?
Bibliographic record
Abstract
The introduction of competency-based postgraduate medical training, as recently stimulated by national governing bodies in Canada, the United States, the United Kingdom, The Netherlands, and other countries, is a major advancement, but at the same time it evokes critical issues of curricular implementation. A source of concern is the translation of general competencies into the practice of clinical teaching. The authors observe confusion around the term competency, which may have adverse effects when a teaching and assessment program is to be designed. This article aims to clarify the competency terminology. To connect the ideas behind a competency framework with the work environment of patient care, the authors propose to analyze the critical activities of professional practice and relate these to predetermined competencies. The use of entrustable professional activities (EPAs) and statements of awarded responsibility (STARs) may bridge a potential gap between the theory of competency-based education and clinical practice. EPAs reflect those activities that together constitute the profession. Carrying out most of these EPAs requires the possession of several competencies. The authors propose not to go to great lengths to assess competencies as such, in the way they are abstractly defined in competency frameworks but, instead, to focus on the observation of concrete critical clinical activities and to infer the presence of multiple competencies from several observed activities. Residents may then be awarded responsibility for EPAs. This can serve to move toward competency-based training, in which a flexible length of training is possible and the outcome of training becomes more important than its length.
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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 | 0.020 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.019 | 0.029 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".