Overview of national strategies for the prevention and management of non-communicable diseases
Bibliographic record
Abstract
Abstract Background Several countries have developed national strategies or policies for preventing and managing non-communicable diseases (NCDs) which are the leading cause of death worldwide. We aim to provide an overview of these strategies from selected countries and their implementation, focusing on chronic respiratory and cardiovascular diseases, diabetes and depression. Methods Using a comprehensive structured hand search, strategies from 8 countries (Germany, Switzerland, Netherlands, Finland, Ireland, United Kingdom, Canada, Australia) were identified and information on the main characteristics and implementation process of the strategies was extracted. Results A total of 18 strategies were included. Most of the strategies formulate rather broad overarching aims or visions (e.g., “stay healthy” or “living healthier lives”) as well as more specific targets that differ across strategies, e.g. focusing on improving quality of life and health literacy, reducing health inequalities or strengthening integrated care. The level of detail of information on implementation, monitoring and evaluation processes as well as financing is very heterogeneous. All strategies provide information on activities to achieve their aims, e.g. in the areas of health promotion/primary prevention, self-management, screening, integrated care, measures for specific risk groups or activities outside the health sector. Only a few strategies mention specific, already implemented (and evaluated) interventions, such as prevention or disease management programmes. Conclusions The included NCD strategies differ considerably in terms of level of detail, structure and implementation. We focused on interventions within the health sector and on adults as a target group. However, for the prevention and management of NCDs, it is important to start in early childhood and to adequately address the social determinants of health with a ‘Health in All Policies’ approach. Key messages
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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 | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".