A Peer-to-Peer Approach to Implementation of a Chronic Disease Management Program
Bibliographic record
Abstract
Background: Peer-to-peer (P2P) learning occurs when individuals from similar social groups or professions help each other to learn new knowledge skills or problem solving. Peer-to-peer learning is used across many disciplines but has not been widely studied in primary care or chronic disease management. This study explored the use of an interprofessional P2P approach to support the implementation of a chronic disease management program in primary care for patients with chronic obstructive pulmonary disease (COPD), known as Best Care COPD (BCC). Methods and findings: A single descriptive case study design was used to explore P2P learning implementation approach. Focus groups and key informant interviews were held with providers involved in implementation (n = 26). Three key components of the P2P approach were identified: 1) an interprofessional team, 2) iterative peer-led training, and 3) continuous peer connection. Three recommendations are provided to support future P2P efforts: 1) enlist a champion in each profession, 2) build a P2P community, and 3) implement succession planning. Conclusion: This article provides an empirical example of the use of a P2P approach in primary care program implementation. The results will inform the future implementation of programs for chronic disease management as well as the continued sustainability of the BCC program.
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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 | 0.032 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.002 | 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".