Social factors, health policy, and environment: implications for cardiovascular disease across the globe
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of deaths worldwide, with 80% occurring in low- and middle-income countries. These countries are characterized by rapid urbanization, poorly funded health systems, poor access to prevention and treatment strategies, and increasing age and a higher prevalence of chronic disease. Rapid urbanization has contributed to the significant environmental and societal changes affecting daily life habits and cardiovascular health. There is growing awareness that environmental and social exposures and policies can influence CVD directly or through behavioural risk factors. However, much of this knowledge comes from studies in high-income countries and is applied to low- and middle-income countries without evidence to indicate this is appropriate. This state-of-the-art review will present and synthesize key findings from the Prospective Urban Rural Epidemiology study and related studies that have aimed to understand the environmental, social, and policy determinants of cardiovascular health in countries across varying levels of economic development through an urban/rural lens. Emerging from these findings are future policy and research recommendations to accelerate the reduction of the global burden of CVD.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".