Stakeholder perceptions and experiences of competency-based training with entrustable professional activities (SPECTRE): protocol of a systematic review and thematic synthesis of qualitative research
Bibliographic record
Abstract
BACKGROUND: Competency-Based Medical Education (CBME) aims to align educational outcomes with the demands of modern healthcare. Entrustable Professional Activities (EPAs) serve as key tools for feedback and professional development within CBME. With the growing body of literature on EPAs, there is a need to synthesize existing research on stakeholders' experiences and perceptions to enhance understanding of the implementation and impact of EPAs. In this synthesis, we will address the following research questions: How are Entrustable Professional Activities experienced and perceived by stakeholders in various healthcare settings, and what specific challenges and successes do they encounter during their implementation? METHODS: Using Thomas and Harden's thematic synthesis method, we will systematically review and integrate findings from qualitative and mixed-methods research on EPAs. The process includes a purposive literature search, assessment of evidence quality, data extraction, and synthesis to combine descriptive and analytical themes. DISCUSSION: This study aims to provide insights into the use of EPAs for competency-based education, reflecting diverse contexts and viewpoints, and identifying literature gaps. The outcomes will guide curriculum and policy development, improve educational practices, and set future research directions, ultimately aligning CBME with clinical realities. TRIAL REGISTRATION: Not required.
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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 | 0.174 | 0.154 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".