Improving water, sanitation, and hygiene (WASH), with a focus on hand hygiene, globally for community mitigation of COVID-19
Bibliographic record
Abstract
Continuity of key water, sanitation, and hygiene (WASH) infrastructure and WASH practices-for example, hand hygiene-are among several critical community preventive and mitigation measures to reduce transmission of infectious diseases, including COVID-19 and other respiratory diseases. WASH guidance for COVID-19 prevention may combine existing WASH standards and new COVID-19 guidance. Many existing WASH tools can also be modified for targeted WASH assessments during the COVID-19 pandemic. We partnered with local organizations to develop and deploy tools to assess WASH conditions and practices and subsequently implement, monitor, and evaluate WASH interventions to mitigate COVID-19 in low- and middle-income countries in Latin America and the Caribbean and Africa, focusing on healthcare, community institution, and household settings and hand hygiene specifically. Employing mixed-methods assessments, we observed gaps in access to hand hygiene materials specifically despite most of those settings having access to improved, often onsite, water supplies. Across countries, adherence to hand hygiene among healthcare providers was about twice as high after patient contact compared to before patient contact. Poor or non-existent management of handwashing stations and alcohol-based hand rub (ABHR) was common, especially in community institutions. Markets and points of entry (internal or external border crossings) represent congregation spaces, critical for COVID-19 mitigation, where globally-recognized WASH standards are needed. Development, evaluation, deployment, and refinement of new and existing standards can help ensure WASH aspects of community mitigation efforts that remain accessible and functional to enable inclusive preventive behaviors.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".