Evaluating implementation and impact of a provincial quality improvement collaborative for the management of chronic diseases in primary care: the COMPAS+ study protocol
Bibliographic record
Abstract
BACKGROUND: Chronic conditions such as diabetes and chronic obstructive pulmonary disease (COPD) are common and burdensome diseases primarily managed in primary care. Yet, evidence points to suboptimal quality of care for these conditions in primary care settings. Quality improvement collaboratives (QICs) are organized, multifaceted interventions that can be effective in improving chronic disease care processes and outcomes. In Quebec, Canada, the Institut national d'excellence en santé et en services sociaux (INESSS) has developed a large-scale QIC province-wide program called COMPAS+ that aims to improve the prevention and management of chronic diseases in primary care. This paper describes the protocol for our study, which aims to evaluate implementation and impact of COMPAS+ QICs on the prevention and management of targeted chronic diseases like diabetes and COPD. METHODS: This is a mixed-methods, integrated knowledge translation study. The quantitative component involves a controlled interrupted time series involving nine large integrated health centres in the province. Study sites will receive one of two interventions: the multifaceted COMPAS+ intervention (experimental condition) or a feedback only intervention (control condition). For the qualitative component, a multiple case study approach will be used to achieve an in-depth understanding of individual, team, organizational and contextual factors influencing implementation and effectiveness of the COMPAS+ QICs. DISCUSSION: COMPAS+ is a QI program that is unique in Canada due to its integration within the governance of the Quebec healthcare system and its capacity to reach many primary care providers and people living with chronic diseases across the province. We anticipate that this study will address several important gaps in knowledge related to large-scale QIC projects and generate strong and useful evidence (e.g., on leadership, organizational capacity, patient involvement, and implementation) having the potential to influence the design and optimisation of future QICs in Canada and internationally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".