Advancing the Chronic Care Road Map: A Contemporary Overview
Bibliographic record
Abstract
In an effort to assess and advance the community-based model of chronic care, we reviewed a contemporary spectrum of Canadian chronic disease management and prevention (CDMP) programs with a participatory audience of administrators, academics, professional and non-professional providers and patients. While many questions remain unanswered, several common characteristics of CDMP success were apparent. These included community-based partnerships with aligned goals; inter-professional and non-professional care, including patient self-management; measured and shared information on practices and outcomes; and visible leadership. Principal improvement opportunities identified were the enhanced engagement of all stakeholders; further efficacy evidence for team care; facile information systems, with clear rationales for data selection, access, communication and security; and increased education of, and resource support for, patients and caregivers. Two immediate actions were suggested. One was a broad and continuing communication plan highlighting CDMP issues and opportunities. The other was a standardized survey of team structures, interventions, measurements and communications in ongoing CDMP programs, with a causal analysis of their relation to outcomes. In the longer term, the key needs requiring action were more inter-professional education of health human resources and more practical information systems available to all stakeholders. Things can be better.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".