A physician communication coaching program: Developing a supportive culture of feedback to sustain and reinvigorate faculty physicians.
Bibliographic record
Abstract
INTRODUCTION: Physician-patient communication involves complex skills that affect quality, outcome, and satisfaction for patients, families, and health care teams. Yet, institutional, regulatory, and scientific demands compete for physicians' attention. A framework is needed to support physicians continued development of communication skills: Coaching is 1 such evidence-based practice, and we assessed the feasibility of implementing such a program. METHOD: Participants were 12 physicians, representing high and low scorers on the Hospital Consumer Assessment of Health Care Providers and Systems (HCAHPS) survey. We added items to capture empathy and family experience to the Calgary-Cambridge Observation Guide for the Medical Interview. Coaches observed communication associated with patient satisfaction and quality measures: introductions (I), asking about concerns (C), and check for understanding (U), or ICU. Participants received a report describing their communication behaviors, emphasizing strengths, and identifying areas for improvement. RESULTS: Scores on the ICU significantly discriminated between low and high HCAHPS scorers, physicians from surgical and cognitive specialties, men and women. We collected anonymous feedback regarding the value of this training; participants recommended expanding the program. DISCUSSION: Based on physician endorsement, experienced coaches are expanding the coaching program to physicians throughout our institution. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".