Exploring women’s coping strategies to combat suboptimal water access in rural Ghana
Bibliographic record
Abstract
Scholarly accounts on the importance and types of adaptive practices for suboptimal water access in Ghana's rural areas, where water inaccessibility persists, are lacking. Conducted in the Kologo rural community in Northern Ghana, known for its severe water insecurity, this study aimed to understand how women manage and adapt to poor water access. An equal number of women (n = 150) and men (n = 150), aged 18 or older, were randomly recruited to participate in a survey. Additionally, eighteen focus group discussions (FGDs) were purposefully conducted with both women and men across different age groups (young: ≤ 30, adult: 31-50, and older: ≥ 51) to provide context and deeper insight into the coping strategies identified through the survey. Surveys and focus group discussions were analyzed statistically and thematically. Findings reveal that these women employ exit (alternative water sources), loyalty (storage, treatment), and voice (community-driven actions) strategies to address water challenges simultaneously or interchangeably. However, specific coping mechanisms can be ineffective and even detrimental long-term. The study underscores that women’s coping strategies alone cannot comprehensively tackle the systemic issues causing access challenges. Collaborative efforts, policy interventions, and gender-sensitive approaches are required to improve equitable water access and alleviate the persistent challenges faced by these women and communities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".