Improving childhood cancer medicines access in developing countries: Towards an implementation framework to inform the Global Platform for Access to Childhood Cancer Medicines for Nigeria
Bibliographic record
Abstract
Children and adolescents in developing countries continue to be disproportionately affected by cancer and have significantly lower survival rates (30%) than their counterparts in high-income countries (80%). This disparity is driven by poor access to childhood cancer medicines. The World Health Organization and St. Jude Children's Research Hospital launched the Global Platform for Access to Childhood Cancer Medicines to provide continuous supply of quality childhood cancer medicines to developing countries. As much movement has not been seen with the platform, this research aimed to develop a stakeholder-informed guidance to support effective implementation of the platform and maximize opportunities to deliver on its intended goals. This study was guided by the Consolidated Framework for Implementation Research (CFIR). Participants were recruited based on the stakeholder categories framework and included policymakers from the Ministry of Health, organizations implementing access to medicines programs in Nigeria, medicines logistics providers, and health professionals and personnel at service delivery points such as oncologists and pharmacists. Data collection involved key informant interviews using a pilot-tested semi-structured interview guide. Data analysis was done by thematic content analysis. Ethical approval was obtained from the National Health Research Ethics Committee of Nigeria and the Ethics Review Board of University of Toronto. The findings reveal critical insights spanning five domains of the CFIR framework, each contributing uniquely to understanding the multifaceted issues of childhood cancer medicine access with a view to understanding pathways to implementation of the platform. Successfully implementing the platform could entail a partner-driven approach, integration with existing programs to avoid fragmentation, supporting capacity strengthening at the primary care level, and engaging patients and communities. This information was used to suggest a nuanced implementation framework for the platform in Nigeria and similar settings which could be beneficial for improving access for children who desperately need childhood cancer medicines to survive.
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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 | 0.077 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".