How Clinical Supervisors Conceptualize Procedural Entrustment: An Interview-Based Study of Entrustment Decision Making in Endoscopic Training
Bibliographic record
Abstract
Purpose Entrustment is central to assessment in competency-based medical education (CBME). To date, little research has addressed how clinical supervisors conceptualize entrustment, including factors they consider in making entrustment decisions. The aim of this study was to characterize supervisors’ decision making related to procedural entrustment, using gastrointestinal endoscopy as a test case. Method Using methods from constructivist grounded theory, the authors interviewed 29 endoscopy supervisors in the United States and Canada across multiple specialties (adult and pediatric gastroenterology, surgery, and family medicine). Semistructured interviews, conducted between April and November 2019, focused on how supervisors conceptualize procedural entrustment, how they make entrustment decisions, and what factors they consider. Transcripts were analyzed using constant comparison to generate an explanatory framework and themes. Results Three themes were identified from the analysis of interview transcripts: (1) entrustment occurs in varying degrees and fluctuates over time; (2) entrustment decisions can transfer within and across procedural and nonprocedural contexts; (3a) persistent static factors (e.g., supervisor competence, institutional culture, legal considerations) influence entrustment decisions, as do (3b) fluctuating, situated dynamic factors (e.g., trainee skills, patient acuity, time constraints), which tend to change from one training encounter to the next. Conclusions In the process of making procedural entrustment decisions, clinical supervisors appear to synthesize multiple dynamic factors against a background of static factors, culminating in a decision of whether to entrust. Entrustment decisions appear to fluctuate over time, and assessors may transfer decisions about specific trainees across settings. Understanding which factors supervisors perceive as influencing their decision making has the potential to inform faculty development, as well as competency committees seeking to aggregate faculty judgments about trainee unsupervised practice. Those leading CBME programs may wish to invest in optimizing the observed static factors, such that these foundational factors are tuned to facilitate trainee learning and achievement of entrustment.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".