‘We Are Drinking Diseases’: Perception of Water Insecurity and Emotional Distress in Urban Slums in Accra, Ghana
Bibliographic record
Abstract
Water security is critical to the health and well-being of people around the world, especially among populations experiencing water stresses and rapid urbanization in low- to middle-income countries (LMICs). Recent research suggests water insecurity is associated with negative mental health outcomes. Despite global improvement in access to safe water across the world, the World Health Organization (WHO) reports that access to safe water in urban areas has not changed significantly or has stagnated in certain countries. In most African cities, entrepreneurial water vendors have stepped up to fill supply gaps in the formal delivery system by selling vended water. As part of a larger research program that aims to assess and analyze public perceptions around vended water, this paper explores the links connecting water insecurity and emotional distress among urban slum dwellers who mostly use vended water in Accra, Ghana. We used a parallel mixed-methods approach. Our quantitative results show that water-insecure households (OR = 2.23, p = 0.01) were more likely to report emotional distresses compared to water-secure households. However, households with improved sanitation (OR = 0.28, p = 0.01) and those willing to participate for improved water and sanitation (OR = 0.28, p = 0.01) were less likely to report emotional distress. Our qualitative results offered support for the quantitative results, as participants not only hold various perceptions regarding the safety and quality of vended water but expressed emotional distresses such as fear of contamination, discomfort, worry over arbitrary change in prices, and anxiety. The implications of the results for policy and practice, specifically to ensuring access to safe water, are discussed.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".