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Record W1674111392 · doi:10.5539/jsd.v8n8p1

Investigating the Predictors of Domestic Water Consumption in Urban Households with Children Under-Five Years: A Panel Study in the Atwima Nwabiagya District, Ghana

2015· article· en· W1674111392 on OpenAlexvenueno aff
Leslie Danquah, Esi Awuah, Seth Agyemang, Charlotte Monica Mensah

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsPer capitaDry seasonConsumption (sociology)Water consumptionSocioeconomicsGeographyEnvironmental scienceWet seasonToxicologyDemographyWater resource managementEconomicsBiologyPopulation

Abstract

fetched live from OpenAlex

This panel study investigated the effect of potential predictors on per capita domestic water consumption in the wet and dry seasons. A total of 242 urban households with children under age five were drawn from two urban communities, Abuakwa and Nkawie, in the Atwima Nwabiagya District, Ghana. Data were collected from mothers using interviewer-administered questionnaires and analyzed using correlation and stepwise multiple regression. Mean per capita daily water consumption was estimated at 38.97 and 20.70 liters in the wet (n = 140) and dry seasons (n = 235), respectively. The volume of the primary water storage vessel, number of water storage containers, and household size were the most significant predictors in the wet season, constituting 16% of the variation in water consumption. Duration of water storage, household size, number of water service hours, and volume of the primary water storage vessel emerged as the most significant predictors in the dry season, constituting 40% of the variation in water consumption. Further research that considers a wider range of socio-demographic factors, such as the gender of the household head, culture, religion and water use characteristics of each member of the household are recommended.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.319
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.208
Teacher spread0.187 · 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 teacher head, 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

Citations3
Published2015
Admission routes1
Has abstractyes

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