Challenges and opportunities for advancing data-driven WASH programming: Reflections from the UNC Chapel Hill Water and Health Conference side event "DATA: A key for unlocking quality in WASH programming"
Bibliographic record
Abstract
Achieving universal and equitable WASH services requires accurate, data-driven understanding of context, needs, and evidence. Investing in data systems enables stakeholders to assess needs, identify priorities, and allocate resources efficiently. As researchers and practitioners working in low- and middle-income countries (LMICs), we have never had greater access to data. However, there is still much that we can learn about how to translate this data into action in the pursuit of more effective, equitable, and accountable WASH programming. During the 2023 Water & Health Conference at the University of North Carolina, Chapel Hill, we convened a meeting of WASH researchers, practitioners, and data specialists to discuss the current state and trajectory of data-driven WASH programming in LMIC contexts. Our goals were to identify opportunities for the application of data to drive improvements in WASH programme quality, and to foster collaboration among organisations working in this space. Here we summarise four emergent themes, documenting examples of, and recommendations for, action. We aim this article at academic researchers, practitioners, and governments, particularly those involved in the design, implementation, and evaluation of WASH programmes in LMICs and in humanitarian contexts. We acknowledge that our perspectives are primarily rooted in the USA, Canada, and Europe, and recognize that a more globally inclusive dialogue around WASH data is needed to build a more comprehensive understanding of these issues.
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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 | 0.141 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.041 | 0.043 |
| Scholarly communication | 0.038 | 0.022 |
| Open science | 0.009 | 0.021 |
| Research integrity | 0.027 | 0.060 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".