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Record W4317934582 · doi:10.2166/washdev.2023.203

Coping with water insecurity in urban Ghana: patterns, determinants and consequences

2023· article· en· W4317934582 on OpenAlexaff
Meshack Achore, Elijah Bisung

Bibliographic record

VenueJournal of Water Sanitation and Hygiene for Development · 2023
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsQueen's University
Fundersnot available
KeywordsCoping (psychology)Psychological interventionUrbanizationEnvironmental healthSocioeconomicsPsychologyBusinessGeographyEconomic growthMedicineEconomicsClinical psychology

Abstract

fetched live from OpenAlex

Abstract In Sub-Saharan Africa (SSA), rapid urbanization poses unprecedented challenges in terms of water security and associated health risks. Like most SSA countries, many Ghanaian households lack access to safely managed drinking water sources and resort to a patchwork of alternative sources for their water needs. This paper examines determinants and implications of water insecurity coping strategies in resource-constrained neighborhoods in Ghana, using a survey (n = 1192) of adult active water collectors within households in Accra and Tamale. Findings suggest that water insecure households were more likely to adopt behavioral, physical and a mix of behavioral and physical coping strategies. Households were more likely to use behavioral (OR = 5.64, p = 0.00), physical (OR = 3.18, p = 0.00) and behavioral and physical (OR = 4.20, p = 0.00) coping strategies in the dry season. Compared with the wealthy, the less wealthy (OR = 0.27, p = 0.00) were less likely to employ a mix of physical and behavioral coping strategies. Likewise, males were less likely (OR = 0.64, p = 0.03) to employ a behavioral coping strategy. The findings can help practitioners identify vulnerable groups and provide targeted interventions that seek to build or strengthen coping strategies in the short term.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.027
GPT teacher head0.279
Teacher spread0.252 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

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