Enhancing global capacity in the surveillance, prevention, and control of chronic diseases: seven themes to consider and build upon: Table 1
Bibliographic record
Abstract
BACKGROUND: Chronic diseases are now a major health problem in developing countries as well as in the developed world. Although chronic diseases cannot be communicated from person to person, their risk factors (for example, smoking, inactivity, dietary habits) are readily transferred around the world. With increasing human progress and technological advance, the pandemic of chronic diseases will become an even bigger threat to global health. METHODS: Based on our experiences and publications as well as review of the literature, we contribute ideas and working examples that might help enhance global capacity in the surveillance of chronic diseases and their prevention and control. Innovative ideas and solutions were actively sought. RESULTS: Ideas and working examples to help enhance global capacity were grouped under seven themes, concisely summarised by the acronym "SCIENCE": Strategy, Collaboration, Information, Education, Novelty, Communication and Evaluation. CONCLUSION: Building a basis for action using the seven themes articulated, especially by incorporating innovative ideas, we presented here, can help enhance global capacity in chronic disease surveillance, prevention and control. Informed initiatives can help achieve the new World Health Organization global goal of reducing chronic disease death rates by 2% annually, generate new ideas for effective interventions and ultimately bring global chronic diseases under greater control.
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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 | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".