‘Calibrating to scale: a framework for humanitarian health organizations to anticipate, prevent, prepare for and manage climate-related health risks’
Bibliographic record
Abstract
Climate Change is adversely affecting health by increasing human vulnerability and exposure to climate-related stresses. Climate change impacts human health both directly and indirectly, through extreme weather events, changing distribution of health risks, increased risks of undernutrition, population displacement, and greater risks of injuries, disease, and death (Ebi, K., Campbell-Lendrum, D., & Wyns, A. The 1. 5 health report. WHO. 2018). This risk amplification is likely to increase the need for humanitarian support. Recent projections indicate that under a business as usual scenario of sustained greenhouse gas emissions, climate change could double the demand for humanitarian assistance by 2050 (World Health Organization. Operational Framework for building climateresilient health systems. WHO. 2015). Humanitarian assistance is currently not meeting the existing needs, therefore, any additional burden is likely to be highly challenging.Global health advocates, researchers, and policymakers are calling for urgent action on climate change, yet there is little clarity on what that action practically entails for humanitarian organizations. While some humanitarian organizations may consider themselves well designed to respond, climate change as a transversal threat requires the incorporation of a resilience approach to humanitarian action and policy responses.By bringing together authors from two historically disparate fields - climate change and health, and humanitarian assistance - this paper aims to increase the capacity of humanitarian organizations to protect health in an unstable climate by presenting an adapted framework. We adapted the WHO operational framework for climate-resilient health systems for humanitarian organizations and present concrete case studies to demonstrate how the framework can be implemented. Rather than suggest a re-design of humanitarian operations we recommend the application of a climate-lens to humanitarian activities, or what is also referred to as mainstreaming climate and health concerns into policies and programs. The framework serves as a starting point to encourage further dialogue, and to strengthen collaboration within, between, and beyond humanitarian organizations.
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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 | 0.073 | 0.062 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.012 | 0.019 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".