Cash transfers, beneficiaries’ livelihoods, and co-responsibilities in the context of water and sanitation insecurity in Ghana
Bibliographic record
Abstract
ABSTRACT The Livelihood Empowerment Against Poverty (LEAP) program is a social protection initiative implemented in 2008 to provide cash transfers to extremely poor households to alleviate poverty and promote human development in Ghana. However, as an unconditional cash transfer program, beneficiaries are expected to perform co-responsibilities related to education, health, nutrition, and savings. In regions with water and sanitation challenges, poor access to water, sanitation, and hygiene (WASH) may be impacting LEAP beneficiaries’ livelihoods and the co-responsibilities mandated by the program. Therefore, using a descriptive case study, we explored the implications of WASH on beneficiaries’ livelihoods and their health and education co-responsibilities. Women (n = 25) and girls (n = 19) were interviewed in two communities (Wechiau and Kandeu) in the Wa West district, Ghana. Key themes that emerged included agricultural and trading livelihood impacts and the impact of WASH on educational and health co-responsibilities. Findings revealed that inadequate WASH facilities pose a threat to beneficiaries’ health, absenteeism from school, and frequently result in school dropouts which undermine the intended goals of LEAP. The study recommends urgent investment in infrastructure to provide safe water, improved sanitation, and hygiene for beneficiaries to promote sustainable development and better education and health outcomes among beneficiaries.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".