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Record W2969631047 · doi:10.1371/journal.pone.0221292

Political priority and pathways to scale-up of childhood cancer care in five nations

2019· article· en· W2969631047 on OpenAlexaff
Avram Denburg, Adriana G. Ramirez, Suresh K. Pavuluri, Erin McCann, Shivani Shah, Ana Patricia Alcasabas, Federico Antillón, Ramandeep Singh Arora, Soad Fuentes-Alabí, Lorna Renner, Catherine G. Lam, Paola Friedrich, Brandon Maser, Lisa M Force, Carlos Rodríguez‐Galindo, Rifat Atun

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick ChildrenPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsPoliticsEconomic growthContext (archaeology)Political scienceHealth careCorporate governanceDeveloping countryGlobal healthHealth policyDevelopment economicsMedicineGeographyBusinessEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Despite increasing global attention to non-communicable diseases (NCDs) and their incorporation into universal health coverage (UHC), the factors that determine whether and how NCDs are prioritized in national health agendas and integrated into health systems remain poorly understood. Childhood cancer is a leading non-communicable cause of death in children aged 0-14 years worldwide. We investigated the political, social, and economic factors that influence health system priority-setting on childhood cancer care in a range of low- and middle-income countries (LMIC). METHODS AND FINDINGS: Based on in-depth qualitative case studies, we analyzed the determinants of priority-setting for childhood cancer care in El Salvador, Guatemala, Ghana, India, and the Philippines using a conceptual framework that considers four principal influences on political prioritization: political contexts, actor power, ideas, and issue characteristics. Data for the analysis derived from in-depth interviews (n = 68) with key informants involved in or impacted by childhood cancer policies and programs in participating countries, supplemented by published academic literature and available policy documents. Political priority for childhood cancer varies widely across the countries studied and is most influenced by political context and actor power dynamics. Ghana has placed relatively little national priority on childhood cancer, largely due to competing priorities and a lack of cohesion among stakeholders. In both El Salvador and Guatemala, actor power has played a central role in generating national priority for childhood cancer, where well-organized and -resourced civil society organizations have disrupted legacies of fragmented governance and financing to create priority for childhood cancer care. In India, the role of a uniquely empowered private actor was instrumental in creating political priority and establishing sustained channels of financing for childhood cancer care. In the Philippines, the childhood cancer community has capitalized on a window of opportunity to expand access and reduce disparities in childhood cancer care through the political prioritization of UHC and NCDs in current health system reforms. CONCLUSIONS: The importance of key health system actors in determining the relative political priority for childhood cancer in the countries studied points to actor power as a critical enabler of prioritization in other LMIC. Responsiveness to political contexts-in particular, rhetorical and policy priority placed on NCDs and UHC-will be crucial to efforts to place childhood cancer firmly on national health agendas. National governments must be convinced of the potential for foundational health system strengthening through attention to childhood cancer care, and the presence and capability of networked actors primed to amplify public sector investments and catalyze change on the ground.

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)0.000
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 score0.034
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.021
GPT teacher head0.279
Teacher spread0.258 · 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

Citations37
Published2019
Admission routes1
Has abstractyes

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