Unequal access to improved water and sanitation in a post-conflict context of Liberia: Evidence from the Demographic and Health Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it