Health system strengthening: Integration of breast cancer care for improved outcomes
Bibliographic record
Abstract
The adoption of the goal of universal health coverage and the growing burden of cancer in low- and middle-income countries makes it important to consider how to provide cancer care. Specific interventions can strengthen health systems while providing cancer care within a resource-stratified perspective (similar to the World Health Organization-tiered approach). Four specific topics are discussed: essential medicines/essential diagnostics lists; national cancer plans; provision of affordable essential public services (either at no cost to users or through national health insurance); and finally, how a nascent breast cancer program can build on existing programs. A case study of Zambia (a country with a core level of resources for cancer care, using the Breast Health Global Initiative typology) shows how a breast cancer program was built on a cervical cancer program, which in turn had evolved from the HIV/AIDS program. A case study of Brazil (which has enhanced resources for cancer care) describes how access to breast cancer care evolved as universal health coverage expanded. A case study of Uruguay shows how breast cancer outcomes improved as the country shifted from a largely private system to a single-payer national health insurance system in the transition to becoming a country with maximal resources for cancer care. The final case study describes an exciting initiative, the City Cancer Challenge, and how that may lead to improved cancer services.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".