The role of simulation in EPA-based curricula
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it