Peer-Coaching for Family Physicians to Close the Intention-to-Action Gap
Bibliographic record
Abstract
INTRODUCTION: Peer coaching has the potential to enhance the effectiveness of clinical performance feedback reports to family physicians, but few peer-coaching quality improvement programs have been implemented and evaluated in primary care. Authors designed, implemented and evaluated a peer-coaching program for family physicians in a large, academic primary-care organization to explore its potential to enhance family physicians' use of clinical performance data for quality improvement. METHODS: Coaches were nominated by their peers and were trained to follow an evidence-informed facilitated feedback model for coaching. Data were collected through surveys, a focus-group with coaches, and individual interviews with coached family physicians ("coachees"). Data were analyzed inductively using reflexive thematic analysis. RESULTS: Authors trained 10 coaches who coached 25 family physicians over 3 months. Coachees who completed the survey (21/25) indicated a desire for additional coaching sessions in future; most (19/21) reported confidence in making practice change. Interview (n = 11) and focus-group participants (n = 8) findings validated acceptability of the coaching approach that emphasized empathy ahead of change-talk. Coaches helped coachees interpret care-quality measures, deal with negative emotional responses evoked, encouraged a sense of accountability for improvement, and sometimes offered new ways to manage common challenges. Coaching sessions led to a wide range of practice-improvement goals. However, effects on practice change were felt to be limited by the data available and the focus on individual physician factors when broader clinic issues acted as important barriers to improvement. CONCLUSIONS: Peer coaching is a feasible approach to supporting family physicians' use of data for learning and practice improvement. More research is needed to understand the impact on practice outcomes and physician wellness.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".