Implementing and monitoring the right to health in breast cancer: selection of indicators using a Delphi process
Bibliographic record
Abstract
BACKGROUND: Women with breast cancer have different chances of surviving their disease, depending on where they live. Variations in survival may stem from unequal access to prompt diagnosis, treatment and care. Implementation of the right to health may help remedy such inequalities. The right to health is enshrined in international human rights law, notably Article 12 of the International Covenant on Economic, Social and Cultural Rights. A human rights-based approach to health requires a robust, just and efficient health system, with access to adequate health services and medicines on a non-discriminatory basis. However, it may prove challenging for health policymakers and cancer management specialists to implement and monitor this right in national health systems. METHOD: This article presents the results of a Delphi study designed to select indicators of implementation of the right to health to inform breast cancer care and management. In a systematic process, 13 experts examined an initial list of 151 indicators. RESULTS: After two rounds, 54 indicators were selected by consensus, three were rejected, three were added, and 97 remained open for debate. For breast cancer, right-to-health features selected as worth implementing and monitoring included the formal recognition of the right to health in breast cancer strategies; a population-based screening programme, prompt diagnosis, strong referral systems and limited waiting times; the provision of palliative, survivorship and end-of-life care; the availability, accessibility, acceptability and quality (AAAQ) of breast cancer services and medicines; the provision of a system of accountability; and the collection of anonymised individual data to target patterns of discrimination. CONCLUSION: We propose a set of indicators as a guide for health policy experts seeking to design national cancer plans that are based on a human rights-based approach to health, and for cancer specialists aiming to implement principles of the right to health in their practice. The 54 indicators selected may be used in High-Income Countries, or member states of the OECD who also have signed the International Covenant on Economic, Social and Cultural Rights to monitor progress towards implementation of the right to health for women with breast cancer.
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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 | 0.136 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".