A Collaborative Approach to a Chronic Care Problem
Bibliographic record
Abstract
Quality improvement collaboratives (QICs) are popular vehicles for supporting healthcare improvement; however, the effectiveness of these models and the factors associated with their success are not fully understood. This paper presents a QIC in the Canadian context, where provincial healthcare systems have historically faced difficulty in transcending their structural and political limitations as well as moving from reactive models of care (prioritizing illness treatment in a hospital-reliant system) to more proactive ones (prioritizing population health in a primary care-based system). In March 2012, in a move that has been described as "unprecedented," 17 health regions across four provinces in Atlantic Canada, together with the Canadian Foundation for Healthcare Improvement (CFHI), developed a collaborative to improve chronic disease prevention and management. This paper introduces the Atlantic Healthcare Collaboration for Innovation and Improvement in Chronic Disease (AHC), reflecting on the experience of developing and implementing the model, which involved teams of front-line clinicians and managers working with CFHI faculty, coaches and staff to assess, design, implement, evaluate and share healthcare improvements for people living with chronic diseases. The paper shares key results and lessons learned from the AHC QIC experience, thus far, for improving chronic disease prevention and management in healthcare in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.023 | 0.036 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".