Setting priorities for humanitarian water, sanitation and hygiene research: a meeting report
Bibliographic record
Abstract
Recent systematic reviews have highlighted a paucity of rigorous evidence to guide water, sanitation and hygiene (WASH) interventions in humanitarian crises. In June 2017, the Research for Health in Humanitarian Crises (R2HC) programme of Elrha, convened a meeting of representatives from international response agencies, research institutions and donor organisations active in the field of humanitarian WASH to identify research priorities, discuss challenges conducting research and to establish next steps. Topics including cholera transmission, menstrual hygiene management, and acute undernutrition were identified as research priorities. Several international response agencies have existing research programmes; however, a more cohesive and coordinated effort in the WASH sector would likely advance this field of research. This report shares the conclusions of that meeting and proposes a research agenda with the aim of strengthening humanitarian WASH policy and practice.
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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 | 0.298 | 0.258 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".