Exploring the experiences of the overburden of water collection responsibility of rural women in Ghana
Bibliographic record
Abstract
Despite evidence emphasizing women's responsibility for collecting water in sub-Saharan Africa, more needs to be known about the gender-specific consequences of this obligation, especially in rural Ghana, where water inaccessibility is a persistent issue. Employing a community-based case study, this research aimed to explore the gendered consequences of women's water collection responsibility, using a coupled systems framework. Data were gathered from surveys and focus groups and analysed statistically and thematically, respectively. Key findings highlighted intersecting influences in women's water access and collection difficulties, including distance to water sources, poverty, and health issues. Results revealed that over 50% of women experienced multiple consequences, including physical and psychological injuries (>80%), animal attacks (≤12%), spousal violence (>40%), nutritional challenges (>30%), hygiene problems (>40%), and socioeconomic issues (>50%). Over half faced three to seven intersecting water-related consequences, which intensified their difficulty in accessing and collecting water. Differences were observed across sub-communities. Interestingly, not all men had knowledge of these consequences, highlighting the crucial need to broaden their understanding as part of the solution to ease women's burdens. Addressing sociocultural norms and the various factors influencing access through effective and gendered water management and planning is imperative to alleviate women's burdens and improve equitable access.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".