Governance of Multi-sectoral Interventions to Promote Healthy Living in Latin America and the Caribbean
Bibliographic record
Abstract
The Latin America and Caribbean (LAC) region has been experiencing a rapid demographic and epidemiological transition which has important health and economic consequences. Not only is the population aging rapidly, but it is also experiencing major changes in lifestyle. This has altered the disease and mortality profile, reflected in the increasing weight of Non-Communicable Diseases (NCDs), such as heart disease, stroke, cancer and diabetes. These conditions also represent an increasing economic and development threat to households, health systems, and economies. The study also ranked tobacco use among the first five risk factors in LAC and alcohol abuse as the main risk factor in all sub-regions, with the exception of the Caribbean and southern LAC, where alcohol was ranked among the first five. However, voluntary actions are often ineffective and policymakers have replaced them with regulations. For example, in Europe, Canada, and the US, early voluntary nutrition labeling actions failed to meet government standards and expectations which led governments to use mandatory guidelines. In New York City, authorities encouraged restaurants to voluntarily provide easily-seen nutrition information to customers, but, as this did not occur, the City passed a regulation. In general, policymakers and health advocates often gauge and mobilize public opinion to support these health promotion policies and ensure their design and implementation.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".