Prevention and control of noncommunicable diseases through evidence-based public health: implementing the NCD 2020 action plan
Bibliographic record
Abstract
The control of noncommunicable diseases (NCDs) was addressed by the declaration of the 66th United Nations (UN) General Assembly followed by the World Health Organization's (WHO) NCD 2020 action plan. There is a clear need to better apply evidence in public health settings to tackle both behaviour-related factors and the underlying social and economic conditions. This article describes concepts of evidence-based public health (EBPH) and outlines a set of actions that are essential for successful global NCD prevention. The authors describe the importance of knowledge translation with the goal of increasing the effectiveness of public health services, relying on both quantitative and qualitative evidence. In particular, the role of capacity building is highlighted because it is fundamental to progress in controlling NCDs. Important challenges for capacity building include the need to bridge diverse disciplines, build the evidence base across countries and the lack of formal training in public health sciences. As brief case examples, several successful capacity-building efforts are highlighted to address challenges and further evidence-based decision making. The need for a more comprehensive public health approach, addressing social, environmental and cultural conditions, has led to government-wide and society-wide strategies that are now on the agenda due to efforts such as the WHO's NCD 2020 action plan and Health 2020: the European Policy for Health and Wellbeing. These efforts need research to generate evidence in new areas (e.g. equity and sustainability), training to build public health capacity and a continuous process of improvement and knowledge generation and translation.
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 | 0.153 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".