The Creation of Standard-Setting Videos to Support Faculty Observations of Learner Performance and Entrustment Decisions
Bibliographic record
Abstract
Entrustable professional activities (EPAs) provide a framework to standardize medical education outcomes and advance competency-based assessment. Direct observation of performance plays a central role in entrustment decisions; however, data obtained from these observations are often insufficient to draw valid high-stakes conclusions. One approach to enhancing the reliability and validity of these assessments is to create videos that establish performance standards to train faculty observers. Little is known about how to create videos that can serve as standards for assessment of EPAs.The authors report their experience developing videos that represent five levels of performance for an EPA for patient handoffs. The authors describe a process that begins with mapping the EPA to the critical competencies needed to make an entrustment decision. Each competency is then defined by five milestones (behavioral descriptors of performance at five advancing levels). Integration of the milestones at each level across competencies enabled the creation of clinical vignettes that were converted into video scripts and ultimately videos. Each video represented a performance standard from novice to expert. The process included multiple assessments by experts to guide iterative improvements, provide evidence of content validity, and ensure that the authors successfully translated behavioral descriptions and vignettes into videos that represented the intended performance level for a learner. The steps outlined are generalizable to other EPAs, serving as a guide for others to develop videos to train faculty. This process provides the level of content validity evidence necessary to support using videos as standards for high-stakes entrustment decisions.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".