Entrustment Decision Making: Extending Miller’s Pyramid
Bibliographic record
Abstract
The iconic Miller's pyramid, proposed in 1989, characterizes 4 levels of assessment in medical education ("knows," "knows how," "shows how," "does"). The frame work has created a worldwide awareness of the need to have different assessment approaches for different expected outcomes of education and training. At the time, Miller stressed the innovative use of simulation techniques, geared at the third level ("shows how"); however, the "does" level, assessment in the workplace, remained a largely uncharted area. In the 30 years since Miller's conference address and seminal paper, much attention has been devoted to procedures and instrument development for workplace-based assessment. With the rise of competency-based medical education (CBME), the need for approaches to determine the competence of learners in the clinical workplace has intensified. The proposal to use entrustable professional activities as a framework of assessment and the related entrustment decision making for clinical responsibilities at designated levels of supervision of learners (e.g., direct, indirect, and no supervision) has become a recent critical innovation of CBME at the "does" level. Analysis of the entrustment concept reveals that trust in a learner to work without assistance or supervision encompasses more than the observation of "doing" in practice (the "does" level). It implies the readiness of educators to accept the inherent risks involved in health care tasks and the judgment that the learner has enough experience to act appropriately when facing unexpected challenges. Earning this qualification requires qualities beyond observed proficiency, which led the authors to propose adding the level "trusted" to the apex of Miller's pyramid.
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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 | 0.030 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".