„WeiterbildungPLUS“: eLogbuch, Entrustable Professional Activities & Co.
Bibliographic record
Abstract
The transformation of time-bound and procedure-oriented specialist medical postgraduate training towards a competency-based approach (competency-based medical education, CBME) has been demanded for several years. Many frameworks, like the CANMEDs (Canadian Medical Education Directives for Specialists) describe competencies that should be acquired by each physician. In Germany, the medical council has recently obligated a competency-based postgraduate training. Although the idea of CBME emphasizes the learning process at the working place, CBME has also been criticized to be too theoretical and detached from the clinical working practice. To close this gap, the concept of Entrustable Professional Activities (EPA) has been introduced. An EPA describes concrete clinical tasks that are successively entrusted to the trainee. The decision to entrust a task is supported by the sum of workplace-based assessments.Sustainable implementation of competency-based training requires close collaboration among all involved individuals and institutions. Furthermore, continuous feedback and open dialogue are crucial for identifying challenges and areas for improvement. The success of CBME hinges on the collective effort of all stakeholders to create a framework to enhance specialty training and an overall advancement in the field. This cooperative approach is essential to successfully translate the theoretical foundations of competency-based teaching into clinical practice and ensure high-quality specialty training.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.270 | 0.263 |
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".