Characterizing the gendered health burdens of poor water quality in the Global South
Bibliographic record
Abstract
Safe water is crucial for human health and sustainable development, yet unsafe water disproportionately affects vulnerable populations like women and children. Targeting the Global South, this study combines a systematic literature review and a health risk assessment to examine the gendered health consequences of unsafe water. The review identified 1916 articles in the original search; 428 articles were screened, and 73 articles from 2015 to 2022, onset to midway the UN Sustainable Development Goals era, were included. Nitrate (40%), fluoride (33%), and arsenic (16%) were the most reported water contaminants in the Global South. Fluoride and arsenic were selected for detailed health risk analysis based on the reporting frequency and global data availability. The results show that infants, children, and pregnant women face higher health risks from water contaminants. Women are more susceptible to endocrine-disrupting chemicals and enteric pathogens, while men have slightly higher non-carcinogenic health hazard quotients (HQ) from fluoride and arsenic exposure. Despite low fluoride HQs in most countries, several nations in South America and Asia showed high arsenic HQs, indicating elevated risks of arsenicosis, cancers, and neurological disorders. The study underscores the need to address the gendered impacts of water quality decline, recognizing that water-related issues are not gender-neutral.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".