Prevention and control of noncommunicable diseases through evidence-based public health: implementing the NCD 2020 action plan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it