Unobserved Observers: Nurses’ Perspectives About Sharing Feedback on the Performance of Resident Physicians
Bibliographic record
Abstract
PURPOSE: Postgraduate training programs are incorporating feedback from registered nurses (RNs) to facilitate holistic assessments of resident performance. RNs are a potentially rich source of feedback because they often observe trainees during clinical encounters when physician supervisors are not present. However, RN perspectives about sharing feedback have not been deeply explored. This study investigated RN perspectives about providing feedback and explored the facilitators and barriers influencing their engagement. METHOD: Constructivist grounded theory methodology was used in interviewing 11 emergency medicine and 8 internal medicine RNs at 2 campuses of a tertiary care academic medical center in Ontario, Canada, between July 2019 and March 2020. Interviews explored RN experiences working with and observing residents in clinical practice. Data collection and analysis were conducted iteratively. Themes were identified using constant comparative analysis. RESULTS: RNs felt they could observe authentic day-to-day behaviors of residents often unwitnessed by supervising physicians and offer unique feedback related to patient advocacy, communication, leadership, collaboration, and professionalism. Despite a strong desire to contribute to resident education, RNs were apprehensive about sharing feedback and reported barriers related to hierarchy, power differentials, and a fear of overstepping professional boundaries. Although infrequent, a key stimulus that enabled RNs to feel safe in sharing feedback was an invitation from the supervising physician to provide input. CONCLUSIONS: Perceived hierarchy in academic medicine is a critical barrier to engaging RNs in feedback for residents. Accessing RN feedback on authentic resident behaviors requires dismantling the negative effects of hierarchy and fostering a collaborative interprofessional working environment. A critical step toward this goal may require supervising physicians to model feedback-seeking behavior by inviting RNs to share feedback. Until a workplace culture is established that validates nurses' input and creates safe opportunities for them to contribute to resident education, the voices of nurses will remain unheard.
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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 | 0.022 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".