Chronic Disease Management: It's Time for Transformational Change!
Bibliographic record
Abstract
The authors of the lead essay present a compelling case for the development and implementation of a national strategy on chronic disease prevention and management (CDPM). The literature demonstrates that the Chronic Care Model can improve quality and reduce costs. Substantial evidence supports the role of health information technologies such as electronic health records (EHRs) in achieving these goals. However, an interoperable pan-Canadian health infostructure does not exist; funding is required to establish this across the continuum of care. An investment of $350 per capita would provide a robust health technology platform to support a national CDPM strategy. Such an investment would deliver annual benefits of $6-$7.6 billion; this could be leveraged to support national healthcare priorities such as CDPM. EHRs will improve decisions about care, reduce system errors and increase efficiency. They will also improve our ability to measure, assess and manage care. We cannot run a high-performing health system without sound data. This was a key step to enabling progress on wait times management. Leadership is required if a national CDPM strategy is to become reality. The authors made a convincing case for the development of a national strategy; we need to turn their words into actionable events to gain necessary momentum.
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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.052 | 0.089 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".