Health Impacts of Large-Scale Floods: Governmental Decision-Making and Resilience of the Citizens
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During the 15th World Congress on Disaster and Emergency Medicine in Amsterdam, May 2007 (15WCDEM), a targeted agenda program (TAP) about the public health aspects of large-scale floods was organized. The main goal of the TAP was the establishment of an overview of issues that would help governmental decision-makers to develop policies to increase the resilience of the citizens during floods. During the meetings, it became clear that citizens have a natural resistance to evacuations. This results in death due to drowning and injuries. Recently, communication and education programs have been developed that may increase awareness that timely evacuation is important and can be life-saving. After a flood, health problems persist over prolonged periods, including increased death rates during the first year after a flood and a higher incidence of chronic illnesses that last for decades after the flood recedes. Population-based resilience (bottom-up) and governmental responsibility (top-down) must be combined to prepare regions for the health impact of evacuations and floods. More research data are needed to become better informed about the health impact and consequences of translocation of health infrastructures after evacuations. A better understanding of the consequences of floods will support governmental decision-making to mitigate the health impact. A top-10 priority action list was formulated.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it