Universal cervical cancer control through a right to health lens: refocusing national policy and programmes on underserved women
Bibliographic record
Abstract
BACKGROUND: Cervical cancer claims 311,000 lives annually, and 90% of these deaths occur in low- and middle-income countries. Cervical cancer is a highly preventable and treatable disease, if detected through screening at an early stage. Governments have a responsibility to screen women for precancerous cervical lesions. Yet, national screening programmes overlook many poor women and those marginalised in society. Under-screened women (called hard-to-reach) experience a higher incidence of cervical cancer and elevated mortality rates compared to regularly-screened women. Such inequalities deprive hard-to-reach women of the full enjoyment of their right to sexual and reproductive health, as laid out in Article 12 of the International Covenant on Economic, Social and Cultural Rights and General Comment No. 22. DISCUSSION: This article argues first for tailored and innovative national cervical cancer screening programmes (NCSP) grounded in human rights law, to close the disparity between women who are afforded screening and those who are not. Second, acknowledging socioeconomic disparities requires governments to adopt and refine universal cancer control through NCSPs aligned with human rights duties, including to reach all eligible women. Commonly reported- and chronically under-addressed- screening disparities relate to the availability of sufficient health facilities and human resources (example from Kenya), the physical accessibility of health services for rural and remote populations (example from Brazil), and the accessibility of information sensitive to cultural, ethnic, and linguistic barriers (example from Ecuador). Third, governments can adopt new technologies to overcome individual and structural barriers to cervical cancer screening. National cervical cancer screening programmes should tailor screening methods to under-screened women, bearing in mind that eliminating systemic discrimination may require committing greater resources to traditionally neglected groups. CONCLUSION: Governments have human rights obligations to refocus screening policies and programmes on women who are disproportionately affected by discrimination that impairs their full enjoyment of the right to sexual and reproductive health. National cervical cancer screening programmes that keep the right to health principles (above) central will be able to expand screening among low-income, isolated and other marginalised populations, but also women in general, who, for a variety of reasons, do not visit healthcare providers for regular screenings.
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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 | 0.048 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.038 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.019 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 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".