Entrustment Unpacked: Aligning Purposes, Stakes, and Processes to Enhance Learner Assessment
Bibliographic record
Abstract
Educators use entrustment, a common framework in competency-based medical education, in multiple ways, including frontline assessment instruments, learner feedback tools, and group decision making within promotions or competence committees. Within these multiple contexts, entrustment decisions can vary in purpose (i.e., intended use), stakes (i.e., perceived risk or consequences), and process (i.e., how entrustment is rendered). Each of these characteristics can be conceptualized as having 2 distinct poles: (1) purpose has formative and summative, (2) stakes has low and high, and (3) process has ad hoc and structured. For each characteristic, entrustment decisions often do not fall squarely at one pole or the other, but rather lie somewhere along a spectrum. While distinct, these continua can, and sometimes should, influence one another, and can be manipulated to optimally integrate entrustment within a program of assessment. In this article, the authors describe each of these continua and depict how key alignments between them can help optimize value when using entrustment in programmatic assessment within competency-based medical education. As they think through these continua, the authors will begin and end with a case study to demonstrate the practical application as it might occur in the clinical learning environment.
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 | 0.030 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".