An environmental scan of policies in support of chronic disease self-management in Canada
Bibliographic record
Abstract
INTRODUCTION: The evidence supporting chronic disease self-management warrants further attention. Our aim was to identify existing policies, strategies and frameworks that support self-management initiatives. METHODS: This descriptive study was conducted as an environmental scan, consisting of an Internet search of government and other publicly available websites, and interviews with jurisdictional representatives identified through the Health Council of Canada and academic networking. RESULTS: We interviewed 16 representatives from all provinces and territories in Canada and found 30 publicly available and relevant provincial and national documents. Most provinces and territories have policies that incorporate aspects of chronic disease self-management. Alberta and British Columbia have the most detailed policies. Both feature primary care prominently and are not disease specific. Both also have provincial level implementation of chronic disease self-management programming. Canada's northern territories all lacked specific policies supporting chronic disease self-management despite a significant burden of disease. CONCLUSION: Engaging patients in self-management of their chronic diseases is important and effective. Although most provinces and territories have policies that incorporate aspects of chronic disease self-management, they were often embedded within other initiatives and/or policy documents framed around specific diseases or populations. This approach could limit the potential reach and effect of self-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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".