Entrustable Professional Activities and Entrustment Decision Making: A Development and Research Agenda for the Next Decade
Bibliographic record
Abstract
To establish a research and development agenda for Entrustable Professional Activities (EPAs) for the coming decade, the authors, all active in this area of investigation, reviewed recent research papers, seeking recommendations for future research. They pooled their knowledge and experience to identify 3 levels of potential research and development: the micro level of learning and teaching; the meso level of institutions, programs, and specialty domains; and the macro level of regional, national, and international dynamics. Within these levels, the authors categorized their recommendations for research and development. The authors identified 14 discrete themes, each including multiple questions or issues for potential exploration, that range from foundational and conceptual to practical. Much research to date has focused on a variety of issues regarding development and early implementation of EPAs. Future research should focus on large-scale implementation of EPAs to support competency-based medical education (CBME) and on its consequences at the 3 levels. In addition, emerging from the implementation phase, the authors call for rigorous studies focusing on conceptual issues. These issues include the nature of entrustment decisions and their relationship with education and learner progress and the use of EPAs across boundaries of training phases, disciplines and professions, including continuing professional development. International studies evaluating the value of EPAs across countries are another important consideration. Future studies should also remain alert for unintended consequences of the use of EPAs. EPAs were conceptualized to support CBME in its endeavor to improve outcomes of education and patient care, prompting creation of this agenda.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.022 | 0.040 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".