Entrustability Scales
Bibliographic record
Abstract
Meaningful residency education occurs at the bedside, along with opportunities for situated in-training assessment. A necessary component of workplace-based assessment (WBA) is the clinical supervisor, whose subjective judgments of residents' performance can yield rich and nuanced ratings but may also occasionally reflect bias. How to improve the validity of WBA instruments while simultaneously capturing meaningful subjective judgment is currently not clear. This Perspective outlines how "entrustability scales" may help bridge the gap between the assessment judgments of clinical supervisors and WBA instruments. Entrustment-based assessment evaluates trainees against what they will actually do when independent; thus, "entrustability scales"-defined as behaviorally anchored ordinal scales based on progression to competence-reflect a judgment that has clinical meaning for assessors. Rather than asking raters to assess trainees against abstract scales, entrustability scales provide raters with an assessment measure structured around the way evaluators already make day-to-day clinical entrustment decisions, which results in increased reliability. Entrustability scales help raters make assessments based on narrative descriptors that reflect real-world judgments, drawing attention to a trainee's readiness for independent practice rather than his/her deficiencies. These scales fit into milestone measurement both by allowing an individual resident to strive for independence in entrustable professional activities across the entire training period and by allowing residency directors to identify residents experiencing difficulty. Some WBA tools that have begun to use variations of entrustability scales show potential for allowing raters to produce valid judgments. This type of anchor scale should be brought into wider circulation.
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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 | 0.010 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".