National Strategies for Preventing and Managing Non-communicable Diseases in Selected Countries
Bibliographic record
Abstract
Non-communicable diseases (NCDs) are the leading cause of death worldwide and are responsible for a high burden of disease. Many countries have developed national strategies for the management and prevention of NCDs to improve the care of chronically ill people or prevent NCDs. This article aims to provide an overview of national NCD strategies from selected countries and their implementation. The focus was on cardiovascular and chronic respiratory diseases, diabetes type II, and depression. A comprehensive, structured hand search was conducted in various databases and websites for national strategies on the 4 NCDs. According to pre-defined criteria, 18 strategies from 8 countries (Germany, Switzerland, Netherlands, Finland, Ireland, United Kingdom, Canada, Australia) were selected. The included NCD strategies differ considerably in terms of level of detail, structure and implementation. All strategies include information on planned activities, but only a few provide detailed information on these interventions, including their evaluation. A structured approach from the macro to the micro level seems crucial for a comprehensive, coordinated overall policy. Strategies should be evaluated regularly using appropriate methods to measure target achievement. For the prevention and management of NCDs, it is important to start in early childhood and to adequately consider the social determinants of health with a "Health in All Policies" approach.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".