Barriers to Drinking Water Security in Rural Ghana: The Vulnerability of People with Disabilities
Bibliographic record
Abstract
Because it is a life-giving substance and one of the crucial components of good health and human survival, access to potable water has been recognised globally as a human rights issue. The current development paradigm also endorses inclusivity in development interventions, calling on leaders of countries to leave no one behind. In most developing countries, however, there seems to be a dilemma as to whether governments can achieve the 'all-inclusive agenda'. Among the most marginalised people are those with disabilities; in terms of access to potable water, this group is likely to face some of the greatest inequalities. Using a qualitative approach that employs in-depth interviews with members of three rural communities in Ghana, this study assesses the water security experiences of persons with disabilities (PWDs). The study identifies barriers such as social exclusion, stigma, distance and water costs, all of which make it difficult for PWDs to collect a sufficient quantity of potable water. Considering the need to achieve universal access to clean water globally, understanding access barriers is essential for rural water management policy decisions. We conclude that in order to enhance access to potable water by PWDs, it is imperative that their needs are assessed, that members of this group are included in rural water management decision-making, and that they are involved in the day-to-day management of drinking water facilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".