Access to improved water and sanitation in sub-Saharan Africa in a quarter century
Bibliographic record
Abstract
The realization of the scale, magnitude, and complexity of the water and sanitation problem at the global level has compelled international agencies and national governments to increase their resolve to face the challenge. There is extensive evidence on the independent effects of urbanicity (rural-urban environment) and wealth status on access to water and sanitation services in sub-Saharan Africa. However, our understanding of the joint effect of urbanicity and wealth on access to water and sanitation services across spatio-temporal scales is nascent. In this study, a pooled regression analysis of the compositional and contextual factors that systematically vary with access to water and sanitation services over a 25-year time period in fifteen countries across sub-Saharan Africa (SSA) was carried out. On the whole, substantial improvements have been made in providing access to improved water sources in SSA from 1990 to 2015 unlike access to sanitation facilities over the same period. Households were 28.2 percent and 125.2 percent more likely to have access to improved water sources in 2000-2005 and 2010-2015 respectively, than in 1990-1995. Urban rich households were 329 percent more likely to have access to improved water sources compared with the urban poor. Although access to improved sanitation facilities increased from 69 percent in 1990-1995 and 74 percent in 2000-2005 it declined significantly to 53 percent in 2010-2015. Urban rich households were 227 percent more likely to have access to improved sanitation facilities compared with urban poor households. These results were mediated and attenuated by biosocial, socio-cultural and contextual factors and underscore the fact that the challenge of access to water and sanitation in sub-Saharan Africa is not merely scientific and technical but interwoven with environment, culture, economics and human behaviour necessitating the need for interdisciplinary research and policy interventions.
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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 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".