Bibliographic record
Abstract
The prevalence of breast cancer in Venezuela is particularly alarming, which is attributed to healthcare inequalities, low health literacy, and lagging compliance with prevention methods (i.e., screening and mammography). While the right to health is acknowledged by the Venezuelan constitution, activism beyond governmental confines is required to increase women's breast cancer awareness and decrease mortality rates. Through the development of social support and strategic communicative methods enacted by healthcare providers, it may be possible to empower women with the tools necessary for breast cancer prevention. This paper discusses issues surrounding women's breast cancer, such as awareness of the disease and its risks, self-advocacy, and the roles of activists, healthcare providers, and society. Specifically, it describes a four-year action-oriented research project developed in Venezuela, which was a collaborative work among researchers, practitioners, NGOs, patients, journalists, and policymakers. The outcomes include higher levels of awareness and interest among community members and organizations to learn and seek more information about women's breast cancer, better understandings of the communicated messages, more media coverage and medical consultations, increasing positive patient treatments, expansion of networking of NGOs, as well as a widely supported declaration for a national response against breast cancer in Venezuela.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".