Encouraging residents to seek feedback
Bibliographic record
Abstract
AIM: To explore resident and faculty perceptions of the feedback process, especially residents' feedback-seeking activities. METHODS: We conducted focus groups of faculty and residents exploring experiences in giving and receiving feedback, feedback-seeking, and suggestions to support feedback-seeking. Using qualitative methods and an iterative process, all authors analyzed the transcribed audiotapes to identify and confirm themes. RESULTS: Emerging themes fit a framework situating resident feedback-seeking as dependent on four central factors: (1) learning/workplace culture, (2) relationships, (3) purpose/quality of feedback, (4) emotional responses to feedback. Residents and faculty agreed on many supports and barriers to feedback-seeking. Strengthening the workplace/learning culture through longitudinal experiences, use of feedback forms and explicit expectations for residents to seek feedback, coupled with providing a sense of safety and adequate time for observation and providing feedback were suggested. Tensions between faculty and resident perceptions regarding feedback-seeking related to fear of being found deficient, the emotional costs related to corrective feedback and perceptions that completing clinical work is more valued than learning. CONCLUSION: Resident feedback-seeking is influenced by multiple factors requiring attention to both faculty and learner roles. Further study of specific influences and strategies to mitigate the tensions will inform how best to support residents in seeking feedback.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".