Monitoring chronic diseases in Canada: the Chronic Disease Indicator Framework
Bibliographic record
Abstract
INTRODUCTION: The Public Health Agency of Canada developed the Chronic Disease Indicator Framework (the Framework) with the goal of systematizing and enhancing chronic disease surveillance in Canada by providing the basis for consistent and reliable information on chronic diseases and their determinants. METHODS: Available national and international health indicators, frameworks and national health databases were reviewed to identify potential indicators. To make sure that a comprehensive and balanced set of indicators relevant to chronic disease prevention was included, a conceptual model with "core domains" for grouping eligible indicators was developed. Specific selection criteria were applied to identify key measures. Extensive consultations with a broad range of government partners, non-governmental organizations and public health practitioners were conducted to reach consensus and refine and validate the Framework. RESULTS: The Framework contains 41 indicators organized in a model comprised of 6 core domains: social and environmental determinants, early life / childhood risk and protective factors, behavioural risk and protective factors, risk conditions, disease prevention practices, and health outcomes/status. Also planned is an annual release of updated data on the proposed set of indicators, including national estimates, breakdowns by demographic and socioeconomic variables, and time trends. CONCLUSIONS: Understanding the evidence related to chronic diseases and theirdeterminants is key to interpreting trends and crucial to the development of public health interventions. The Framework and its related products have the potential of becoming an indispensable tool for evidence-informed decision making in Canada.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.022 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".