Feedback in an Entrustment-Based Objective Structured Clinical Examination: Analysis of Content and Scoring Methods
Bibliographic record
Abstract
Background The integration of entrustable professional activities (EPAs) within objective structured clinical examinations (OSCEs) has yielded a valuable avenue for delivering timely feedback to residents. However, concerns about feedback quality persist. Objective This study aimed to assess the quality and content alignment of verbal feedback provided by examiners during an entrustment-based OSCE. Methods We conducted a progress test OSCE for internal medicine residents in 2022, assessing 7 EPAs. The immediate 2-minute feedback provided by examiners was recorded and analyzed using the Quality of Assessment of Learning (QuAL) score. We also analyzed the degree of alignment with EPA learning objectives: competency milestones and task-specific abilities. In a randomized crossover experiment, we compared the impact of 2 scoring methods used to assess residents’ clinical performance (3-point entrustability scales vs task-specific checklists) on feedback quality and alignment. Results Twenty-one examiners provided feedback to 67 residents. The feedback demonstrated high quality (mean QuAL score 4.3 of 5) and significant alignment with the learning objectives of the EPAs. On average, examiners addressed in their feedback 2.5 milestones (61%) and 1.2 task-specific abilities (46%). The scoring methods used had no significant impact on QuAL scores (95% CI -0.3, 0.1, P =.28), alignment with competency milestones (95% CI -0.4, 0.1, P =.13), or alignment with task-specific abilities (95% CI -0.3, 0.1, P =.29). Conclusions In our entrustment-based OSCE, examiners consistently offered valuable feedback aligned with intended learning outcomes. Notably, we explored high-quality feedback and alignment as separate dimensions, finding no significant impact from our 2 scoring methods on either aspect.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".