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Record W4403793046 · doi:10.2166/wh.2024.033

Exploring the experiences of the overburden of water collection responsibility of rural women in Ghana

2024· article· en· W4403793046 on OpenAlexaff
Gervin Ane Apatinga, Corinne J. Schuster‐Wallace, Sarah Dickson‐Anderson

Bibliographic record

VenueJournal of Water and Health · 2024
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcMaster UniversityUniversity of Saskatchewan
FundersUniversity of Ghana
KeywordsPovertySanitationFocus groupSocioeconomicsHygieneSocioeconomic statusObligationPsychologySociologyEnvironmental healthEconomic growthEnvironmental planningBusinessGeographyPolitical scienceMedicineMarketingEconomicsPopulation

Abstract

fetched live from OpenAlex

Despite evidence emphasizing women's responsibility for collecting water in sub-Saharan Africa, more needs to be known about the gender-specific consequences of this obligation, especially in rural Ghana, where water inaccessibility is a persistent issue. Employing a community-based case study, this research aimed to explore the gendered consequences of women's water collection responsibility, using a coupled systems framework. Data were gathered from surveys and focus groups and analysed statistically and thematically, respectively. Key findings highlighted intersecting influences in women's water access and collection difficulties, including distance to water sources, poverty, and health issues. Results revealed that over 50% of women experienced multiple consequences, including physical and psychological injuries (>80%), animal attacks (≤12%), spousal violence (>40%), nutritional challenges (>30%), hygiene problems (>40%), and socioeconomic issues (>50%). Over half faced three to seven intersecting water-related consequences, which intensified their difficulty in accessing and collecting water. Differences were observed across sub-communities. Interestingly, not all men had knowledge of these consequences, highlighting the crucial need to broaden their understanding as part of the solution to ease women's burdens. Addressing sociocultural norms and the various factors influencing access through effective and gendered water management and planning is imperative to alleviate women's burdens and improve equitable access.

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)0.002
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.851
Threshold uncertainty score0.099

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.058
GPT teacher head0.326
Teacher spread0.268 · 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 designQualitative
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

Citations7
Published2024
Admission routes1
Has abstractyes

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