Improving water, sanitation, and hygiene (WASH), with a focus on hand hygiene, globally for community mitigation of COVID-19
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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