Assessment of accessibility to safe water and sanitation in Bẹẹrẹ community, Ibadan, Oyo state, Nigeria
Bibliographic record
Abstract
Globally, access to improved water and sanitation is crucial for a healthy, productive life, and environmental sustainability. However, disparities in access to potable water and adequate sanitation facilities remain a challenge in developing nations. Therefore, this study assessed the accessibility of safe water and sanitation in the Bẹẹrẹ community of Ìbàdàn, Nigeria. Water-sanitation-and-hygiene (WASH) and sustainable livelihoods provided the framework. This study adopted a mixed-method research design that employed qualitative and quantitative approaches. The study population comprised all households. A total of 381 buildings within the Bẹẹrẹ community boundary were obtained using the Google Earth and Geographic Information System (GIS). The head of a household in each of the 381 buildings was randomly selected. Data collected through questionnaires and direct observations were descriptively analyzed. The findings revealed that the average age of household heads was 49±12years, with an average income of N45,100 ± N10,400, and had a household size of 8–11 people. Their major source of water was wells (43.4%), while they (46.2%) rarely had access to clean drinkable water. The majority (87.8%) testified to water shortages yearly, and (83.2%) used unhealthy sanitation methods. The major challenges identified were financial constraints, lack of facilities, and illiteracy/behavioral issues. Therefore, there should be an institutional and communal-led sanitation program coupled with environmental education that will foster the expansion and rehabilitation of water infrastructure and promote behavioral changes in hygiene and sanitation practices to support the Bẹẹrẹ community's transition to sustainable livelihoods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".