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Record W4385377514 · doi:10.1186/s12939-023-01964-w

Implementing and monitoring the right to health in breast cancer: selection of indicators using a Delphi process

2023· article· en· W4385377514 on OpenAlexfundno aff
Lisa Montel, Michel P. Coleman, Thérèse Murphy, Dina Balabanova, Raffaele Ciula, Dabney P. Evans, Claire Lougarre, Didier Verhoeven, Claudia Allemani

Bibliographic record

VenueInternational Journal for Equity in Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsnot available
FundersUniversiteit AntwerpenQueen's UniversityCancer Research UKQueen's University BelfastDalhousie UniversityUlster UniversityEmory UniversityUniversity of EssexNewcastle UniversitySheffield Hallam UniversitySapienza Università di RomaLondon School of Hygiene and Tropical MedicineUnited Nations Population Fund
KeywordsRight to healthMedicineBreast cancerHealth carePopulationPublic healthHealth policyHealth equityNursingCancerEnvironmental healthPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.114
GPT teacher head0.527
Teacher spread0.412 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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