Mapping Rural Household Vulnerability to Flood-Induced Health Risks in Disaster-Stricken Khyber Pakhtunkhwa, Pakistan
Bibliographic record
Abstract
This study maps the rural household vulnerability to flood-induced health risks in flood-affected Khyber Pakhtunkhwa (KPK), Pakistan, focusing on the devastating 2022 flood. Using data from 600 households in the severely impacted districts of Khyber Pakhtunkhwa province (including Charsadda and Nowshera), this research examines the influence of demographic, socioeconomic, and infrastructural factors on household vulnerability. This study assesses household vulnerability to flooding and health issues using logistic regression. The current study findings revealed that female-headed households, those with younger heads, and families with lower educational levels are particularly vulnerable. Income disparities significantly shape coping capacity, with wealthier households more likely to adopt effective risk-mitigation strategies. Proximity to functioning healthcare facilities emerged as a crucial factor in reducing vulnerability, as these households faced fewer health hazards. Conversely, households in areas where health and water infrastructure were damaged experienced higher risks of disease outbreaks, including cholera and malaria, due to water contamination and inadequate sanitation. This study highlights the urgent need for resilient infrastructure, strengthened public health systems, improved health education, and enhanced water and sanitation services to mitigate flood-induced health risks. Policymakers are urged to sustainable development practices by adopting gender-sensitive disaster management strategies, prioritizing educational initiatives, and fostering community support networks to enhance resilience to future flood events in KPK.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".