Health Impacts of Large-Scale Floods: Governmental Decision-Making and Resilience of the Citizens
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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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".