Relationships as the Backbone of Feedback: Exploring Preceptor and Resident Perceptions of Their Behaviors During Feedback Conversations
Bibliographic record
Abstract
PURPOSE: Newer definitions of feedback emphasize learner engagement throughout the conversation, yet teacher and learner perceptions of each other's behaviors during feedback exchanges have been less well studied. This study explored perceptions of residents and faculty regarding effective behaviors and strategies during feedback conversations and factors that affected provision and acceptance of constructive feedback. METHOD: Six outpatient internal medicine preceptors and 12 residents at Brigham and Women's Hospital participated (2 dyads per preceptor) between September 2017 and May 2018. Their scheduled feedback conversations were observed by the lead investigator, and one-on-one interviews were conducted with each member of the dyad to explore their perceptions of the conversation. Interviews were transcribed and analyzed for key themes. Because participants repeatedly emphasized teacher-learner relationships as key to meaningful feedback, a framework method of analysis was performed using the 3-step relationship-centered communication model REDE (relationship establishment, development, and engagement). RESULTS: After participant narratives were mapped onto the REDE model, key themes were identified and categorized under the major steps of the model. First, establishment: revisit and renew established relationships, preparation allows deeper reflection on goals, set a collaborative agenda. Second, development: provide a safe space to invite self-reflection, make it about a skill or action. Third, engagement: enhance self-efficacy at the close, establish action plans for growth. CONCLUSIONS: Feedback conversations between longitudinal teacher-learner dyads could be mapped onto a relationship-centered communication framework. Our study suggests that behaviors that enable trusting and supportive teacher-learner relationships can form the foundation of meaningful feedback.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".