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Record W3158089745 · doi:10.1021/acsestwater.1c00045

Seasonal Preferences and Alternatives for Domestic Water Sources: A Prospective Cohort Study in Malawi

2021· article· en· W3158089745 on OpenAlexafffund
Alexandra Cassivi, Elizabeth Tilley, Owen Waygood, Caetano C. Dorea

Bibliographic record

VenueACS ES&T Water · 2021
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsPolytechnique MontréalUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCohortGeographyEnvironmental healthEnvironmental scienceMedicineStatisticsMathematics

Abstract

fetched live from OpenAlex

Access to a sufficient quantity of safe water is widely recognized as fundamental to ensure health and prevent water- and excreta-related diseases. The objective of this study is to analyze seasonal variations in household preferences and alternatives in accessing domestic water, including for drinking, and to identify predictors for the use of multiple water sources. A prospective cohort study was conducted in Malawi, and data were collected using structured household questionnaires and water quality testing. Results showed that households fetching water were more likely to rely on multiple water sources during the rainy season, compared to the dry season. When access to a single water source is insufficient, and/or the main water source is broken or not functional, households use additional water sources that are more likely to be contaminated or distant as a coping strategy. Water source reliability (i.e., functionality and availability) and proximity to water sources (i.e., time to collect water, waiting time) were found to be the most important factors influencing households’ preferences. Ensuring reliable and continuous access, throughout the seasons, to at least a single water source that is located in proximity to the household is a key intervention to reduce the fetching burden.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.296
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes2
Has abstractyes

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