Identifying Core Components of EPA Implementation: A Path to Knowing if a Complex Intervention Is Being Implemented as Intended
Bibliographic record
Abstract
PURPOSE: Competency-based assessment, using entrustable professional activities (EPAs), is rapidly being implemented worldwide without sufficient agreement on the essential elements of EPA-based assessment. The rapidity of implementation has left little time to understand what works in what circumstances and why or why not. The result is the attempted execution of a complex service intervention without a shared mental model for features needed to remain true to implementing an EPA assessment framework as intended. The purpose of this study was to identify the essential core components necessary to maintain integrity in the implementation of this intended intervention. METHOD: A formal consensus-building technique, the Delphi process, was used to identify core components for implementing an EPA-based assessment framework. Twelve EPA experts from the United States, Canada, and the Netherlands participated in this process in February and March 2020. In each Delphi round, participants rated possible core components on a scale from 1 to 6, with 1 reflecting the worst fit and 6 the best fit for EPA-based assessment implementation. Predetermined automatic inclusion and exclusion criteria for candidate core components were set at ≥ 80% of participants assigning a value of 5 or 6 and ≥ 80% assigning a value of 1 or 2, respectively. RESULTS: After 3 rounds, participants prioritized 10 of 19 candidate core components for inclusion: performance prediction, shared local mental model, workplace assessment, high-stakes entrustment decisions, outcomes based, value of the collective, informed clinical competency committee members, construct alignment, qualitative data, and entrustment decision consequences. The study closed after 3 rounds on the basis of the rankings and comments. CONCLUSIONS: Using the core components identified in this study advances efforts to implement an EPA assessment framework intervention as intended, which mitigates the likelihood of making an incorrect judgment that the intervention demonstrates negative results.
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 | 0.167 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.012 |
| 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".