The Influence of Relationship-Centered Coaching on Physician Perceptions of Peer Review in the Context of Mandated Regulatory Practices
Bibliographic record
Abstract
PURPOSE: Medical regulatory authorities are legally mandated to protect patients by monitoring the practice of medicine. While principally a matter of public safety, many pursue this mission by establishing quality improvement initiatives that prioritize professional development for all rather than identification of substandard performers. Engaging practitioners in directed learning opportunities, however, is rife with challenge given inherent social complexities. This study was run to explore whether relationship-centered coaching could improve physicians' perceptions of the value of engaging with College-mandated peer review. METHOD: A quasi-experimental analysis was performed on physician ratings of the effectiveness of peer assessor interactions and assessment processes during 3 time periods: (1) an historical control (March 2016-December 2016; n = 296); (2) a period after assessors were trained to deliver feedback using relationship-centered coaching (December 2016-March 2017; n = 96); and (3) after physicians were given more capacity to choose patient records for peer review and engage in discussion about multisource feedback results (March 2017-December 2018; n = 448). RESULTS: Psychometric analyses supported the aggregation of survey items into assessor interaction and assessment process subscores. Training assessors to engage in relationship-centered coaching was related with higher assessor interaction scores (4.64 vs 4.47; P < .05; d = 0.37). Assessment process scores did not increase until after additional program enhancements were made in period 3 (4.33 vs 4.17, P < .05, d = 0.29). CONCLUSIONS: Despite peer interactions being inherently stressful for physicians when they occur in the context of regulatory authority visits, efforts to establish a quality improvement culture that prioritizes learning can improve physicians' perceptions of peer review.
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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 | 0.014 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".