The strategic role of Nursing in the Healthy Brazil Program to address socially determined diseases
Bibliographic record
Abstract
Brazilian public health is at a turning point.While neglected diseases continue to leave scars of inequality, the launch of the Healthy Brazil Program -Unite to Care (Programa Brasil Saudável -Unir para Cuidar) in 2024 emerges as an intersectoral government response, aligned with the World Health Organization (WHO) global goals, the United Nations (UN) Sustainable Development Goals (SDGs), and the Pan American Health Organization (PAHO) strategy to eliminate infections and diseases in the Americas.Coordinated by the Ministry of Health in partnership with 13 other ministries, this program aims to eliminate or reduce 11 diseases as public health concerns, including tuberculosis, leprosy, Chagas disease, and malaria -all closely linked to poverty, lack of sanitation, and territorial exclusion -along with five vertically transmitted infections (1) .The program is grounded in the recognition that neglected diseases are diseases of inequality and presents an opportunity to overcome health challenges by adopting a broad view of the health-disease process and its determinants.It is based on integrating technical and community knowledge and mobilizing institutional, community, public, and private resources to improve quality of life.Since the Ottawa Charter, Health Promotion has become a concept associated with values such as equity, democracy, and citizenship, involving coordinated actions among the state, community, individuals, the health system, and various sectors, reinforcing the idea of shared responsibility in seeking solutions (2) .In this context, it is essential to understand nursing as a strategic field with direct involvement in the development, implementation, expansion, and management of public policies in Brazil's Unified Health System (Sistema Único de Saúde -SUS), due to its multiplier and coordinating potential across all levels of care, integrating Nursing Care Systematization into the care context.The Ministry of Health, in partnership with the Federal Nursing Council, has been developing strategies to support the implementation of this interministerial program, considering nursing as a key player.Brazil, a continental country, requires actions that account for regional specificities.Among the 175 municipalities How to cite this articleMaciel EL, Almeida EC.The strategic role of Nursing in the Healthy Brazil Program to address socially determined diseases.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".