Equity and Prevention of Cardiovascular Diseases in Latin America and the Caribbean
Bibliographic record
Abstract
Non-communicable diseases, particularly cardiovascular diseases, are the leading cause of decreased life expectancy and death in Latin America and the Caribbean. Although a lifestyle, which includes no tobacco use, good nutrition, and regular physical activity is touted as key to health, the environmental, racial, social and economic conditions, which underpin lifestyle are often ignored or considered only secondarily. Placing the main responsibility on a patient to change their lifestyle or to simply comply with pharmacological treatment ignores the specific conditions in which the individual lives. Furthermore, there are major disparities in access to both healthy living conditions as well as access to medical care. There is sufficient evidence to support advocating for policies that support healthy living, particularly healthy food choices. Progress is being made to improve the food environment with enactment of front of package nutritional labels. However, policies were enacted only after intense regional research and advocacy supporting their implementation. Government officials must rise above the pressures of commercial interests and support health-promoting policies or be exposed as self-interest groups themselves. Strong advocacy is required to persuade officials that all policies should take health into consideration both to improve lives and economies.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 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".