Gaps in humanitarian WASH response: perspectives from people affected by crises, practitioners, global responders, and the literature
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.
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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 | 0.054 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".