Household water insecurity experience in the Upper West Region of Ghana: Insights for effective water resource management
Bibliographic record
Abstract
The global community is not on track to achieve Sustainable Development Goal 6 (SDG 6) by 2030. Many low- and middle-income countries like Ghana still struggle with water insecurity. In semi-arid regions like Ghana’s Upper West, climate change has worsened water insecurity, leading to health and livelihood consequences. In UWR, limited studies have explored water insecurity in rural areas. This study fills a knowledge gap by investigating the determinants of water insecurity in Ghana’s Upper West Region (UWR) from a political ecology of health (PEH) perspective. It comprehensively explores the interplay of social, economic, political, environmental, and health-related factors contributing to water insecurity in the UWR. The results from binary logistic regression show that households in the wealthier category (OR = 0.475, p<0.05) and those that spent less than thirty minutes on a roundtrip to fetch water (OR = 0.474, p<0.01) were less likely to experience water insecurity. On the other hand, households that did not use rainwater harvesting methods (OR = 2.117, p<0.01), had to travel over a kilometer to access water (OR = 3.249, p<0.01), had inadequate water storage systems (OR = 2.290, p<0.001), did not treat their water (OR = 2.601, p<0.001), were exposed to water-induced infections (OR = 3.473, p<0.001), did not receive any water, hygiene, and sanitation education (OR = 2.575, p<0.01), and faced water scarcity during the dry season (OR = 2.340, p<0.001) were at a higher risk of experiencing water insecurity. To mitigate the risks of water insecurity and adverse health impacts, policymakers and practitioners must work together to educate households on effective water conservation, storage, and treatment techniques. It is recommended that households harvest rainwater as a coping strategy, construct appropriate storage systems, and treat their water. Communal self-help water investments should be encouraged and supported. Given the significant aquifers and semi-arid landscape of the UWR, investing in groundwater development should be a top priority.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".