Who can do this procedure? Using entrustable professional activities to determine curriculum and entrustment in anesthesiology – An international survey
Bibliographic record
Abstract
INTRODUCTION: As competency-based curricula get increasing attention in postgraduate medical education, Entrustable Professional Activities (EPAs) are gaining in popularity. The aim of this survey was to determine the use of EPAs in anesthesiology training programs across Europe and North America. METHODS: A survey was developed and distributed to anesthesiology residency training program directors in Switzerland, Germany, Austria, Netherlands, USA and Canada. A convergent design mixed-methods approach was used to analyze both quantitative and qualitative data. RESULTS: The survey response rate was 38% (108 of 284). Seven percent of respondents used EPAs for making entrustment decisions. Fifty-three percent of institutions have not implemented any specific system to make such decisions. The majority of respondents agree that EPAs should become an integral part of the training of residents in anesthesiology as they are universal and easy to use. CONCLUSION: Although recommended by several national societies, EPAs are used in few anesthesiology training programs. Over half of responding programs have no specific system for making entrustment decisions. Although several countries are adopting or planning to adopt EPAs and national societies are recommending the use of EPAs as a framework in their competency-based programs, few are yet using these to make "competence" decisions.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".