A Systematic Review of Water and Gender Interlinkages: Assessing the Intersection With Health
Bibliographic record
Abstract
Background: Significant developmental challenges in low-resource settings limit access to sustainable water, sanitation, and hygiene (WASH). However, in addition to reducing human agency and dignity, gendered WASH inequities can also increase disease burden among women and girls. In this systematic review, a range of challenges experienced by women relating to inadequate WASH resources are described and their intersection with health are explored. We further assess the effectiveness of interventions in alleviating inequalities related to the Sustainable Development Goals (SDGs) 3 (health), 5 (gender), and 6 (water). Methods: We searched the MEDLINE database to identify research articles related to water (i.e., WASH), gender, and sustainability. An analysis of both observational and interventional studies was undertaken. For each study, content analysis was performed to identify the relevant WASH, gender, and health related outcomes, and the main conclusions of the study. Results: Key themes from our search included that women and girls face barriers towards accessing basic sanitation and hygiene resources, including a lack of secure and private sanitation and of Menstrual Hygiene Management (MHM) resources. In total, 71% of identified studies reported a health outcome, suggesting an intersection of water and gender with health. Half of the research studies that included a health component reflected on the relationship between WASH, gender, and infantile diseases, including under-5 mortality, waterborne parasites, and stunting. In addition, we found that women and girls, as a result of their role as water purveyors, were at risk of exposure to contaminated water and of sustaining musculoskeletal trauma. A limited number of studies directly compared gender differences in accessing WASH resources, and an even smaller fraction (N=5, 8.5%) reported sex-disaggregated outcomes. Educational, infrastructural, and programmatic interventions showed promise in reducing WASH and health outcomes. Indeed, infrastructural WASH interventions can be successful if long-term maintenance is ensured. Conclusions: Significant WASH inequities in women and girls further manifest as health burdens, providing strong evidence that the water-gender-nexus intersects with health. Thus, addressing gender and water inequities holds the potential to alleviate disease burden and have a significant impact on achieving the SDGs, including SDG 3, 5, and 6.
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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 | 0.032 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".