Breast cancer policy in Latin America: account of achievements and challenges in five countries
Bibliographic record
Abstract
BACKGROUND: The recent increase of breast cancer mortality has put on alert to most countries in the region. However it has taken some time before breast cancer could be considered as a relevant problem. Only in recent years breast cancer has been considered a priority in some Latin American countries and resources have been mobilized to confront the problem at the institutional level. The article analyzes the efforts made in five Latin American countries (Argentina, Brazil, Colombia, Mexico and Venezuela) in the last 15 years to design and implement policies to address the growing incidence of breast cancer. METHODS: Data was collected between July and December 2010 from both primary and secondary sources. Semi-structured interviews were conducted with key informants from governmental and non-governmental organizations. Secondary data was obtained from publications in journals, government reports and official statistics in each country. Analysis combines information from both types of sources. RESULTS: Countries have followed different paths and are in different stages of policy implementation. In all cases early detection is a key strategy. Through the design of programs and guidelines, the allocation of financial resources to treat patients, as well as a formally structured information system, Brazil and Mexico have been able to set up comprehensive national policies. Argentina, Colombia and Venezuela have made important advancements but not yet capable of coordinating comprehensive national policies. CONCLUSION: Breast cancer is being considered a priority in all five countries but there are different stages in the rolling out of comprehensive national policies due to differences in their capacity to allocate resources, implement operational strategies and encourage the participation of relevant stakeholders.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".