The impact of entrustment assessments on feedback and learning: Trainee perspectives
Bibliographic record
Abstract
CONTEXT: Assessment for and of learning in workplace settings is at the heart of competency-based medical education. In postgraduate medical education (PGME), entrustable professional activities (EPAs) and entrustment scales are increasingly used to assess competence. However, the educational impacts of these assessment approaches remain unknown. Therefore, this study aimed to explore trainee perceptions regarding the impacts of EPAs and entrustment scales on feedback and learning processes in the clinical setting. METHODS: Four focus groups were conducted with postgraduate trainees in anaesthesia, emergency medicine, general internal medicine and nephrology at McMaster University in Hamilton, Ontario, Canada. Data collection and analysis were informed by principles of constructivist grounded theory. RESULTS: Entrustable professional activities representing well-defined tasks are perceived as potentially effective drivers for feedback and learning. Use of EPAs and entrustment scales, however, may augment existing tensions between developmental (for learning) and decision-making (of learning) assessment functions. Three key dilemmas seem to influence the impact of EPA-based assessment approaches on residents' learning: (a) standardisation of outcomes versus flexibility in assessment to align with individual learning experiences; (b) assessment tasks focusing on performance standards versus opportunities for learning, and (c) feedback focusing on numeric entrustment scores versus narrative and dialogue. Use of entrustment as an assessment outcome may impact trainees' motivation and feelings of self-efficacy, further enhancing tensions between learning and performance. CONCLUSIONS: Entrustable professional activities and entrustment scales may support assessment for learning in PGME. However, their successful implementation requires the careful management of dilemmas that arise in EPA-based assessment in order to support competence development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".