Constructing Approaches to Entrustable Professional Activity Development that Deliver Valid Descriptions of Professional Practice
Bibliographic record
Abstract
Issue: Entrustable Professional Activities (EPAs) describe the core tasks health professionals must be competent performing prior to promotion and/or moving into unsupervised practice. When used for learner assessment, they serve as gateways to increased responsibility and autonomy. It follows that identifying and describing EPAs is a high-stakes form of work analysis aiming to describe the core work of a profession. However, hasty creation and adoption of EPAs without rigorous attention to content threatens the quality of judgments subsequently made from using EPA-based assessment tools. There is a clear need for approaches to identify validity evidence for EPAs themselves prior to their deployment in workplace-based assessment. Evidence: For EPAs to realize their potential in health professions education, they must first be constructed to reflect accurately the work of that profession or specialty. If the EPAs fail to do so, they cannot predict a graduate’s readiness for or future performance in professional practice. Evaluating the methods used for identification, description, and adoption of EPAs through a construct validity lens helps give leaders and stakeholders of EPA development confidence that the EPAs constructed are, in fact, an accurate representation of the profession’s work. Implications: Application of a construct validity lens to EPA development impacts all five commonly followed steps in EPA development: selection of experts; identification of candidate EPAs; iterative revisions; evaluation of proposed EPAs; and formal adoption of EPAs into curricula. It allows curricular developers to avoid pitfalls, bias, and common mistakes. Further, construct validity evidence for EPA development provides assurance that the EPAs adopted are appropriate for use in workplace-based assessment and entrustment decision-making.
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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 | 0.139 | 0.183 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".