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Record W4392901840 · doi:10.53328/inr24gar011

Unmasking the Unseen: The Gendered Impacts of Water Quality, Sanitation and Hygiene

2024· report· en· W4392901840 on OpenAlexafffund
Grace Oluwasanya, Ayodetimi Omoniyi, Duminda Perera, Manzoor Qadir, Kaveh Madani

Bibliographic record

Venuenot available
Typereport
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUnited Nations University Institute for Water, Environment, and HealthGlobal Affairs Canada
FundersGlobal Affairs CanadaGovernment of Canada
KeywordsSanitationHygieneEnvironmental healthWater qualitySocioeconomic statusPublic healthData collectionEnvironmental planningSocioeconomicsBusinessGeographyMedicineNursingSociologyPopulation

Abstract

fetched live from OpenAlex

This report investigated the interplay between water quality, sanitation, hygiene and gender by examining distinct variables of water quality and their varying impacts on gender like reported water-related illnesses of males and females, and the consequences of water quality, sanitation, and hygiene on menstrual hygiene practices, particularly focusing on a low- and middle-income country- LMICs. This report presents the key findings, outlining a framework and guidance for examining gender-specific impacts stemming from poor water quality and WASH practices through a piloted case study in Abeokuta City, Nigeria, to serve as a preliminary guide for conducting comprehensive, site-specific assessments. The piloted Differential Impacts Assessment, DIA framework is a 5-step approach, guiding the evaluation of gendered impacts from method design to the field activities, which include water sampling and laboratory analysis, public survey, and health data collection, to the data and gender analysis. The focus on low- and middle-income countries underscores the importance of DIA in such regions for better health and socioeconomic outcomes, promoting inclusive development. The study results reveal unsettling, largely unseen gender disparities in exposure to health-related risks associated with non-utility water sources and highlight pronounced differences in water source preferences and utilization, the burden of water sourcing and collection, and health- and hygiene-related practices. Specifically, this preliminary assessment indicates an alarming inadequacy in accessing WASH services within the pilot study area, raising considerable doubts about achieving SDG 6 by 2030. While this finding is worrying, this report also discusses the lack of a standardized protocol for monitoring water-related impacts utilizing sex-disaggregated data, shedding light on the unseen global-scale gendered impacts. The report warns about the water safety of non-utility water sources. Without point-of-use treatment and water safety protocols, the water sources are unsuitable for potable uses, potentially posing compounded health risks associated with microbial contaminations and high calcium content, particularly affecting boys. Girls are likely the most affected by the repercussions of water collection, including time constraints, health implications, and safety concerns. Men and boys face a higher risk related to poor hygiene, while women may be more susceptible to health effects stemming from toilet cleaning responsibilities and shared sanitation facilities. Despite the preference for disposable sanitary pads among most women and girls, women maintain better menstrual hygiene practices than girls. This age-specific disparity highlights potential substantial health risks for girls in the near and distant future. Enhancing women's economic status could improve access to superior healthcare services and significantly elevate household well-being. The report calls for targeted actions, including urgent planning and implementation of robust water safety protocols for non-utility self-supply systems and mainstreaming gender concerns and needs as the “6th” accelerator for SDG 6. The piloted methodology is scalable and serves as an introductory guide that can be further refined to explore and track site-specific differential health and socioeconomic effects of inadequate water quality, especially in locales similar to the study area. The report targets policymakers and donor organizations advocating for sustainable water resource development, public health, human rights, and those promoting gender equality globally

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)0.007
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0030.003
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.071
GPT teacher head0.368
Teacher spread0.296 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2024
Admission routes2
Has abstractyes

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