Documenting the impact of the COMPAS+ quality improvement collaborative on chronic disease management in primary care
Bibliographic record
Abstract
Context: Quality improvement collaboratives (QIC) are multifaceted interventions used to engage clinical teams in applying improvement methods to achieve best practices. COMPAS+ is a large scale QIC that is being implemented in partnership between the Ministry of Health and Social Services and the Institut national d’excellence en santé et en services sociaux (INESSS) in Quebec, Canada. Until now, COMPAS+ has focused on two chronic disease conditions, COPD and type 2 diabetes, to facilitate quality improvement (QI) of chronic disease management in primary care (PC). Objective: Evaluate the extent to which COMPAS+ supports the implementation of QI projects and the integration of a culture of continuous QI in primary care. Study design and analysis: A retrospective mixed-methods multiple case study design was used. Document analysis, interviews with key informants and survey methods were used to document COMPAS+ QIC impacts. Setting: The COMPAS+ QIC was implemented in 5 large integrated public healthcare organizations in three different regions of the Quebec province. Population: PC professionals, managers and patient partners. Intervention/instrument: The COMPAS+ intervention is composed of reflective practice workshops engaging approximately 30 participants from a local PC healthcare network and 2-year facilitation support provided by the COMPAS+ team to a local QI project committee. Results: The intervention supported the implementation of QI projects in 4 of the 5 cases that were managed at different organizational levels (strategic, tactical, and operational). The COMPAS+ intervention produced multiple outcomes that were organized into 6 categories: 1) improved methods used to conduct QI projects; 2) improved organization of COPD or diabetes services, 3) improved use of tools and strategies for the coordination of services, 4) improved screening strategies; 5) improved patient follow-up services; 6) improved PC professionals’ knowledge and competencies. Results from the survey confirmed that the intervention supported individual practice changes for most workshop participants but that perceptions of other outcomes varied between cases and were related to the organizational level at which the change was implemented. Conclusion: The COMPAS+ QIC supported the implementation of multiple QI projects that were managed at different levels of the organization and supported the improvement of 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 | 0.047 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".