Political priority and pathways to scale-up of childhood cancer care in five nations
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".