A framework to support the progressive implementation of integrated team-based care for the management of COPD: a collective case study
Bibliographic record
Abstract
BACKGROUND: In Canada, there is widespread agreement about the need for integrated models of team-based care. However, there is less agreement on how to support the scale-up and spread of successful models, and there is limited empirical evidence to support this process in chronic disease management. We studied the supporting and mitigating factors required to successfully implement and scale-up an integrated model of team-based care in primary care. METHODS: We conducted a collective case study using multiple methods of data collection including interviews, document analysis, living documents, and a focus group. Our study explored a team-based model of care for chronic obstructive pulmonary disease (COPD) known as Best Care COPD (BCC) that has been implemented in primary care settings across Southwestern Ontario. BCC is a quality improvement initiative that was developed to enhance the quality of care for patients with COPD. Participants included healthcare providers involved in the delivery of the BCC program. RESULTS: We identified several mechanisms influencing the scale-up and spread of BCC and categorized them as Foundational (e.g., evidence-based program, readiness to implement, peer-led implementation team), Transformative (adaptive process, empowerment and collaboration, embedded evaluation), and Enabling Mechanisms (provider training, administrative support, role clarity, patient outcomes). Based on these results, we developed a framework to inform the progressive implementation of integrated, team-based care for chronic disease management. Our framework builds off our empirical work and is framed by local contextual factors. CONCLUSIONS: This study explores the implementation and spread of integrated team-based care in a primary care setting. Despite the study's focus on COPD, we believe the findings can be applied in other chronic disease contexts. We provide a framework to support the progressive implementation of integrated team-based care for chronic disease management.
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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 | 0.048 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.018 | 0.024 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".