Integrating cancer into crisis: a global vision for action from WHO and partners
Bibliographic record
Abstract
More than a billion people live in fragile, conflict-affected, and vulnerable settings requiring humanitarian support, where cancer is a substantial health issue. Despite its substantial effect on populations, cancer care remains underprioritised in emergency preparedness and response frameworks and humanitarian operational planning. This Policy Review summarises the perspectives and actionable recommendations from the First Global High-Level Technical Meeting on Non-communicable Diseases in Humanitarian Settings, with a focus on cancer. The paper highlights the challenges of providing cancer care in fragile, conflict-affected, and vulnerable settings and proposes a comprehensive roadmap to address both immediate and long-term needs of patients with cancer living in these settings. Key solutions include: integrating the cancer care continuum into national preparedness and response plans to enhance health-care system resilience; integrating cancer into humanitarian responses efforts; addressing the specific needs of paediatric patients with cancer; improving cancer intelligence and surveillance systems; and developing strategies to navigate the logistical and financial challenges of providing cancer care during crises. Additionally, the paper outlines practical actions and next steps for international cooperation needed to drive a shift in global health priorities and elevate cancer in the global health security agenda. We hope the presented notions will help prevent millions of avoidable deaths among people with cancer.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".