Unequal access to improved water and sanitation in a post-conflict context of Liberia: Evidence from the Demographic and Health Survey
Bibliographic record
Abstract
Public health and wellbeing in Liberia have been compromised by a lack of access to safe drinking water, sanitation, and hygiene (WASH), compounded by 14 years of civil unrest. After almost two decades of relative peace and stability, disparities in WASH access persist and diseases linked to WASH such as Ebola, cholera, and COVID-19 have posed major public health challenges. Yet, there is nascent research in the context of post-war Liberia examining the determinants of access to WASH. To contribute to WASH policy in Liberia, this study examined the predictors of improved water and sanitation using the 2019–20 Liberia Demographic and Health Survey. Using the complementary log-log link function, we found that some socioeconomic and geographical factors were associated with access to improved water and sanitation. For example, poorer and rural households were less likely to have access to improved water and sanitation compared to their wealthier and urban counterparts, respectively. Based on these findings, we discussed policy implications and potential directions for future research.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".