Exploring the Quality of Narrative Feedback Provided to Residents During Ambulatory Patient Care in Medicine and Surgery
Bibliographic record
Abstract
OBJECTIVESThe transition to competency-based medical education (CBME) has increased the volume of residents’ assessment data; however, the quality of the narrative feedback is yet to be used as feedback-on-feedback for faculty. Our objectives were (1) to explore and compare the quality and content of narrative feedback provided to residents in medicine and surgery during ambulatory patient care and (2) to use the Deliberately Developmental Organization framework to identify strengths, weaknesses, and opportunities to improve quality of feedback within CBME.METHODSWe conducted a mixed convergent methods study with residents from the Departments of Surgery (DoS; <i>n</i> = 7) and Medicine (DoM; <i>n</i> = 9) at Queen's University. We used thematic analysis and the Quality of Assessment for Learning (QuAL) tool to analyze the content and quality of narrative feedback documented in entrustable professional activities (EPAs) assessments for ambulatory care. We also examined the association between the basis of assessment, time to provide feedback, and the quality of narrative feedback.RESULTSForty-one EPA assessments were included in the analysis. Three major themes arose from thematic analysis: Communication, Diagnostics/Management, and Next Steps. Quality of the narrative feedback varied; 46% had sufficient evidence about residents’ performance; 39% provided a suggestion for improvement; and 11% provided a connection between the suggestion and the evidence. There were significant differences between DoM and DoS in quality of feedback scores for evidence (2.1 [1.3] vs. 1.3 [1.1]; <i>p</i> < 0.01) and connection (0.4 [0.5] vs. 0.1 [0.3]; <i>p</i> = 0.04) domains of the QuAL tool. Feedback quality was not associated with the basis of assessment or time taken to provide feedback.CONCLUSIONThe quality of the narrative feedback provided to residents during ambulatory patient care was variable with the greatest gap in providing connections between suggestions and evidence about residents’ performance. There is a need for ongoing faculty development to improve the quality of narrative feedback provided to residents.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".