Examining the Quality and Quantity of Verbal Feedback in the Operating Room: A Multi-Specialty Study in a Canadian Context
Bibliographic record
Abstract
OBJECTIVE: To investigate (1) the quantity and quality of verbal feedback provided to residents in surgery and anesthesiology in the operating room (OR), (2) the relationship between provided verbal feedback and assigned entrustment scores; and (3) residents' perceptions of usefulness of verbal feedback they received in the OR for a specific entrustable professional activity (EPA). DESIGN: This was a prospective observational study. SETTING: An academic tertiary care hospital in Kingston, Ontario, Canada. PARTICIPANTS: Twenty residents (8 anesthesiology, 8 general surgery, 4 orthopedic surgery) and 23 attending physicians (11 anesthesiology, 9 general surgery, 3 orthopedic surgery) from Kingston Health Sciences Centre participated voluntarily. Participants were recruited using convenience sampling and no compensation was provided. RESULTS: We recorded 1180 verbal feedback events across 122 hours of OR interactions for 50 attending physician-resident dyads. Verbal feedback events focused predominantly on analyzing performance (57%) and fostering learner agency (20%). Verbal feedback was rated by residents as "extremely useful" or "very useful" over 70% of the time. Verbal feedback events that addressed a greater number of FQI domains were perceived to be more useful by residents. There were no significant correlations between verbal feedback event quantity or quality and entrustment scores on EPAs. CONCLUSION: Attending physicians frequently provide residents with verbal feedback on their performance in the OR; however, the quality of this feedback is variable. Future research should focus on developing strategies to capture and utilize verbal feedback as assessment for learning within CBME.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".