The role of simulation in EPA-based curricula
Bibliographic record
Abstract
Entrustable professional activities (EPAs) form the cornerstone of competency-based health professions education, focusing on the critical tasks trainees must master for their future unsupervised clinical practice. Recognizing the challenges in assessing EPAs, especially those caused by the rarity of some clinical events and the dynamic nature of health care settings, there is an increasing interest in utilizing simulation as a complementary approach. Using simulation modalities, educators can design controlled and relevant settings for learning and assessment, allowing students to apply theoretical knowledge, practical skills, and professional attitudes in a risk-free environment. This chapter delves into whether and how simulation can be integrated into EPA-based curricula to enhance training and preparation for performing EPAs, as well as to provide a controlled setting for assessing trainees’ entrustment levels. We explore the theoretical underpinnings for applying simulation in an EPA-based curriculum, highlighting its potential dual roles in bridging educational experiences with assessment activities, and relating both to real-world clinical practice. While we propose a model for the promising integration of simulation into EPA-based curriculum, we also note that the evidence supporting its efficacy remains preliminary. Further research must substantiate the role and value of simulation in an EPA-based training and assessment modality. Our model describes the possible application of EPAs that progresses from an individual’s basic skill acquisition to their becoming capable of acting in complex, broader team-based clinical challenges. Incorporating simulation meaningfully into EPA-based curricula represents a transformative approach in preparing health care professionals for the challenges of clinical practice.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".