Barriers to Climate Disaster risk Management for Public Health: Lessons from a Pilot Survey of National Public Health Representatives
Bibliographic record
Abstract
OBJECTIVES: This study sought to examine current national disaster risk management capacities, and identify governance barriers to strengthening national preparedness for responding to public health emergencies, associated with the anticipated climate-driven intensification of natural disaster cycles. METHODS: A mixed-methods online survey, assessing broader governance constraints to climate change adaptation (CCA) for public health, was distributed to representatives of national public health associations, and societies of 82 member countries under the World Federation of Public Health Associations. Specific questions relevant to disaster risk management capacities and barriers were analyzed as part of a narrowed focus on the CCA subdomain of emergency preparedness. RESULTS: Existence of some technology, infrastructure, and/ or human resources, necessary to develop early warning and other surveillance systems for climate-related health risks was reported by 9 out of 11 responding countries. However, 7 reported persistent limitations and/ or regional discrepancies. Most significant identified barriers to strengthening emergency preparedness at the national level included governance coordination challenges, and, in the case of many developing countries, technical, medical, and human resource shortages. CONCLUSIONS: The development of new frameworks for intersectoral governance and large-scale resource mobilization will prove crucial to ongoing efforts to strengthen national climate-health resiliency and prepare for disaster-associated health threats.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".