Impact and adaptation to flooding: A focus on water supply, sanitation, and health in rural communities on the Barotse floodplain in Zambia
Bibliographic record
Abstract
Abstract Globally it is recognised that climate change impact is manifested through severe weather events that cause, for example, flooding. Floods have large social consequences for communities and individuals. This paper investigates the impact of flooding on water supply and sanitation conditions together with flood-induced health problems on the Barotse floodplain in Zambia. The study also explored rural response measures and adaptation strategies. This study relied on intensive field investigation in May 2021 where 99 households from different villages in the floodplain were randomly sampled to participate in a questionnaire survey. Key informants, including government officials from the Ministries of Health, Agriculture, and Local Government and Housing, were also interviewed. The findings showed that water supply and sanitation conditions are severely interrupted during flood periods, and this leads to various waterborne diseases. The findings further revealed that all pit latrines in the study area become submerged and therefore unsafe to use. Adaptation measures include boiling water before use and water disinfection using chlorine. Other, adaptive measures noted in the study area include open water defecation due to inundation of toilets, which further contaminates drinking water supplies.Based on the findings, the researchers propose that the government and other stakeholders should prioritise flood-proof water and sanitation facilities in the study area in order to improve the health of the rural community during floods.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".