Gaps in humanitarian WASH response: perspectives from people affected by crises, practitioners, global responders, and the literature
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
Water, sanitation, and hygiene (WASH) interventions prevent and control disease in humanitarian response. To inform future funding and policy priorities, WASH 'gaps' were identified via 220 focus-group discussions with people affected by crises and WASH practitioners, 246 global survey respondents, and 614 documents. After extraction, 2,888 (48 per cent) gaps from direct feedback and 3,151 (52 per cent) from literature were categorised. People affected by crises primarily listed 'services gaps', including a need for water, sanitation, solid waste disposal, and hygiene items. Global survey respondents principally cited 'mechanism gaps' in providing services, including collaboration, WASH staffing expertise, and community engagement. Literature highlighted gaps in health (but not other) WASH intervention impacts. Overall, people affected by crises wanted the 'what' (services), responders wanted the 'how' (to supply), and researchers wanted the 'why' (health consequences). This study suggests a need for a renewed focus on basic WASH services, collaboration across stakeholders, and research on WASH outcomes beyond health.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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