Invisible vulnerability: WASH insecurity of older adults in Ghana during a global public emergency
Bibliographic record
Abstract
ABSTRACT The COVID-19 pandemic has exposed and deepened structural inequalities in water, sanitation, and hygiene (WASH) access in the Global South. While older adults, particularly older women, were among the most vulnerable populations, we know little about their WASH experiences during the pandemic. Drawing on the feminist political ecology of health theoretical framework, this paper examined the factors that shaped the WASH conditions of older women and the coping strategies they employed during the pandemic emergency. We analysed cross-sectional surveys of older women in Ghana during the pandemic. The findings suggest that well-being and food insecurity are major determinants of WASH insecurity. Older women with poor well-being and food insecurity had higher odds of reporting water insecurity. In response to water insecurity, older women adopted diverse coping strategies, including rainwater harvesting, borrowing water from social networks, and illegal connections to public networks. The likelihood of adopting a particular coping mechanism is linked to their socio-economic and living conditions. We call for a life course examination of how systemic inequalities shaped WASH insecurity among marginalized populations. In terms of policy, we echo calls for investing in WASH as a public health emergency response and post-pandemic recovery strategy.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".