Meeting the Challenge of Chronic Conditions in a Sustainable Manner: Building on the AHC Learning
Bibliographic record
Abstract
The Atlantic Healthcare Collaboration for Innovation and Improvement in Chronic Disease (AHC) set out to achieve three aims: to create a patient- and family-centred approach to manage chronic diseases; to build a network of organizational, regional and provincial teams to share evidence-informed, systems-level solutions and work together to develop, implement and sustain improvement initiatives; and to promote the sustainability of the participating health systems. Important elements of all three aims were achieved and the synthesis provides a meaningful contribution to systems working to improve chronic care. This paper explores those achievements as well as some of the areas for improvement, including replicability, expanded outcome measurement, greater detail around patient and family engagement, increased focus on specific outcomes and processes, and further articulation of lessons learned and recommendations.
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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 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.014 | 0.032 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.034 | 0.076 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".