Tensions in Assessment: The Realities of Entrustment in Internal Medicine
Bibliographic record
Abstract
PURPOSE: A key unit of assessment in competency-based medical education (CBME) is the entrustable professional activity. The variations in how entrustment is perceived and enacted across specialties are not well understood. This study aimed to develop a thorough understanding of the process, concept, and language of entrustment as it pertains to internal medicine (IM). METHOD: Attending supervisors of IM trainees on the clinical teaching unit were purposively sampled. Sixteen semistructured interviews were conducted and analyzed using constructivist grounded theory. The study was conducted at the University of Toronto from January to September 2018. RESULTS: Five major themes were elucidated. First, the concepts of entrustment, trust, and competence are not easily distinguished and sometimes conflated. Second, entrustment decisions are not made by attendings, but rather are often automatic and predetermined by program or trainee level. Third, entrustment is not a discrete, point-in-time assessment due to longitudinality of tasks and supervisor relationships with trainees. Fourth, entrustment scale language does not reflect attendings' decision making. Fifth, entrustment decisions affect the attending more than the resident. CONCLUSIONS: A tension arises between the need for a common language of CBME and the need for authentic representation of supervision within each specialty. With new assessment instruments required to operationalize the tenets of CBME, it becomes critically important to understand the nuanced and specialty-specific language of entrustment to ensure validity of assessments.
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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 | 0.041 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| 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".