Water use behaviors and water access in intermittent and continuous water supply areas during the COVID-19 pandemic
Bibliographic record
Abstract
More than one billion people worldwide receive intermittent water supply (IWS), in which water is delivered through a pipe network for fewer than 24 h/day, limiting the quantity and accessibility of water. During the COVID-19 pandemic, stay-at-home orders and efforts to limit contact with others can affect water access for those with unreliable home water supplies. We explored whether water service delivery and household water-use behaviors changed during the COVID-19 pandemic in Hubballi-Dharwad, India, and whether they differed if households had IWS or continuous (24×7) water supply through a longitudinal household survey in 2020-2021. We found few perceived differences in water service delivery or water access, although one-quarter of all households reported insufficient water for handwashing, suggesting an increased demand for water that was not satisfied. Many households with 24×7 supply reported water outages, necessitating the use of alternative water sources. These findings suggest that water demand at home increased and households with IWS and 24×7 both lacked access to sufficient water. Our findings indicate that water insecurity negatively affected households' ability to adhere to protective public health measures during the COVID-19 pandemic and highlight the importance of access to uninterrupted, on-premise water during public health emergencies.
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.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".