Ten Years of the International Cancer Control Partnership: Promoting National Cancer Control Plans to Shape the Health System Response for Cancer Control
Bibliographic record
Abstract
Growing premature mortality because of cancer is an increasing public health concern in all countries. This article reviews 10 years of the International Cancer Control Partnership (ICCP) considering the themes of National Cancer Control Plan (NCCP) support, technical assistance, governance, and the renewed momentum of global calls to action. ICCP has provided key resources for the cancer community by hosting a portal with national cancer control and noncommunicable disease (NCD) plans, strategies, guidelines, and key implementation guides for a growing community of best practices. ICCP partners have responded to the changing needs of country planners, adjusting technical guidance as needs evolve from planning to implementation at the national level with an associated shift to peer-to-peer learning and knowledge exchange. The ICCP offer to assist countries in cancer planning continues to be relevant as countries focus on implementation of global initiatives for breast, cervical, and childhood cancers. These initiatives are important to drive priority actions and a systems approach in the emerging road map on NCDs-a message that will be supported by a second global review of NCCPs in 2023. This is critical for driving national action in all countries on cancer and other NCDs in line with global health commitments made for 2030 and adopted by the United Nations General Assemblies. ICCP sees robust systems and financial planning for implementation, monitoring, and evaluation of NCCPs and protection from cancer-related catastrophic expenditure, as critical to longer-term sustainability and success. ICCP calls for national policymakers to prioritize integration of cancer prevention and control into emerging universal health care approaches, including pandemic preparedness/health system resilience and calls for an equity focus in new NCCPs.
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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 | 0.065 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.014 | 0.041 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".