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Record W3094957200

Women’s Water Access Is Associated With Measures of Empowerment and Social Support: A Cross-sectional Study in Sub-Saharan Africa

2020· article· en· W3094957200 on OpenAlexaffvenue
Hiliary Monteith, Davod Ahmadi, Kate Sinclair, Narges Ebadi, Hugo Melgar‐Quiñonez

Bibliographic record

VenueJournal of rural and community development · 2020
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcGill UniversityUniversity of Toronto
Fundersnot available
KeywordsEmpowermentMillennium Development GoalsWomen's empowermentEconomic growthWater securityCapacity buildingSocial supportSocioeconomicsWater resourcesPolitical scienceGeographyDeveloping countryPsychologySociologyEcologyEconomicsSocial psychology
DOInot available

Abstract

fetched live from OpenAlex

Water access is an important global issue that impacts health and wellbeing and has been recognized by the United Nations as a significant area for improvement. Despite some global improvements from the Millennium Development Goals (MDGs) targets, regions with the most compromised water access are still experiencing significant deficits. Among those regions, Sub-Saharan African (SSA) Countries are the most affected. Socio-ecological factors intersect to further contribute to this compromise in water resources, and community structures and social supports need to be considered. Women’s empowerment and social support have been shown to have an impact on community health and wellbeing, but the association with water access is not well researched. This cross-sectional study considers these relationships and aims to identify water access for women living within SSA and assess its relationship with measures of women’s empowerment and social support. Using data from the Gallup World Poll, our study highlights an association between these factors, suggesting a role for community and female capacity-building to empower women and foster relationships within SSA communities to further work toward improvements in water resources. Keywords: Water access, water security, empowerment, social support, SubSaharan Africa, capacity building

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 score0.173
Threshold uncertainty score0.362

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 integrity0.0000.001
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 head0.066
GPT teacher head0.298
Teacher spread0.232 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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