Improving System Integration: The Art and Science of Engaging Small Community Practices in Health System Innovation
Bibliographic record
Abstract
UNLABELLED: This paper focuses on successful engagement strategies in recruiting and retaining primary care physicians (PCPs) in a quality improvement project, as perceived by family physicians in small practices. Sustained physician engagement is critical for quality improvement (QI) aiming to enhance health system integration. Although there is ample literature on engaging physicians in hospital or team-based practice, few reports describe factors influencing engagement of community-based providers practicing with limited administrative support. The PCPs we describe participated in SCOPE: Seamless Care Optimizing the Patient Experience, a QI project designed to support their care of complex patients and reduce both emergency department (ED) visits and inpatient admissions. SCOPE outcome measures will inform subsequent papers. All the 30 participating PCPs completed surveys assessing perceptions regarding the importance of specific engagement strategies. Project team acknowledgement that primary care is challenging and new access to patient resources were the most important factors in generating initial interest in SCOPE. The opportunity to improve patient care via integration with other providers was most important in their commitment to participate, and a positive experience with project personnel was most important in their continued engagement. Our experience suggests that such providers respond well to personalized, repeated, and targeted engagement strategies.
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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 | 0.031 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".