A systematic review on entrustable professional activities in clerkships during undergraduate medical education - preliminary results
Bibliographic record
Abstract
Background: We conducted a systematic review in order to identify the available evidence in the field of designing clerkship curricula based on entrustable professional activities (EPAs) in undergraduate medical education (UME) (ten Cate et al. 2015). Further aims of the review were to clarify key strategies to implement EPA-based clerkship curricula and to examine the emerging evidence in the early phase of introducing EPAs in UME. The results are considered relevant for planning future research and clinical teaching activities in this medical education field.Summary of Work: We searched PubMed, Embase, Cochrane Library, ERIC,PsycINFO, Scopus, Web of Science and all Ovid journals for articles reporting qualitative and quantitative research as well as conceptual and curriculum development reports on entrustable professional activities in clerkships during undergraduate medical education until 15th January 2019. We based our review method on the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) (Moher et al. 2009).Summary of Results:2,026 records were screened after database searching. A total of 27 articles were included in the systematic review. Three countries (Canada, USA, Switzerland) have developed discrete sets of EPAs for UME. The included articles investigated assessment strategies of EPAs, EPA-based curriculum development, and educational outcomes of EPA-based curricula. 30.7% of studies were specialty specific research reports.Discussion and Conclusions:In addition to EPA-based curricula covering all years of medical school, several clinical specialties are starting to integrate EPAs in their clerkship curricula. Currently there are only preliminary data for few EPAs with regards to educational outcomes. However, there is a growing body of evidence indicating that EPAs can be effectively used for undergraduate medical education and serve asa basis for internationally comparable competency-based curricula.Take-home Messages: There is limited but increasing evidence, that EPAs have a positive impact on educational outcomes in undergraduate medical education. Further research is needed to understand how educational resources should be allocated in order to develop efficient and effective EPA-based curricula spanning the learning trajectory from classroom to bedside in different specialties in undergraduate medical education.
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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 | 0.034 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".