TRENDS IN ACCESS TO WATER AND SANITATION IN MALAWI: PROGRESS AND INEQUALITIES (1992-2017)
Bibliographic record
Abstract
Abstract Billions of people globally gained access to improved drinking water sources and sanitation in the last decades, following effort towards the Millennium Development Goals. Global progress remains a general indicator as it is unclear if access is equitable across groups of the population. Agenda 2030 calling for “leaving no one behind”, there is a need to focus on the variations of access in different groups of the population, especially in the context of least developed countries including Malawi. We analyzed data from Demographic Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS) to describe emerging trends on progress and inequalities in water supply and sanitation services over a 25-year period (1992 - 2017) and to identify the most vulnerable population in Malawi. Data were disaggregated with geographic and socio-economic characteristics including regions, urban and rural areas, wealth and education level. Analysis of available data revealed progress in access to water and sanitation among all groups of the population. The largest progress is generally observed in the groups that were further behind at the baseline year, which likely reflects good targeting in interventions/improvements to reduce the gap in the population. Overall, results demonstrated that some segments of the population - foremost poorest Southern rural populations - still have limited access to water and are forced to practise open defecation. Finally, we suggest to include standardized indicators that address safely managed drinking water and sanitation services in future surveys and studies to increase accuracy of national estimates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".