What If Your Husband Doesn’t Feel the Pressure? An Exploration of Women’s Involvement in WaSH Decision Making in Nyanchwa, Kenya
Bibliographic record
Abstract
Access to water, sanitation and hygiene (WaSH) is a major challenge in sub-Saharan Africa (SSA). Women and girls suffer the main burden of a lack of access to WaSH because they are primarily responsible for collecting water for their homes. However, they are often excluded from WaSH decision-making and implementation processes. This research sought to explore women's experiences in participating in WaSH decision-making through a case study in Nyanchwa, Kenya. Twelve (12) key informant interviews were conducted with community leaders and members regarding challenges and possible measures for enhancing women and girls' participation in WaSH decision-making. From this research, it is evident that economic challenges and cultural factors such as male dominance, greatly inhibit women and girls' participation in WaSH decision-making and implementation processes. Other factors such as time constraints and low literacy rates also emerged. The paper concludes with a call for collaboration among women's groups to enhance collective action for improved access to WaSH. This will undoubtedly lead to enhanced community health and wellbeing (Sustainable Development Goal 3, SDG3) through the empowerment of women (Sustainable Development Goal 5, SDG5).
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".