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Record W2792621356 · doi:10.1177/1077801218754410

Gender Violence as a Water, Sanitation, and Hygiene Risk: Uncovering Violence Against Women and Girls as It Pertains to Poor WaSH Access

2018· article· en· W2792621356 on OpenAlexaff
Morgan Pommells, Corinne J. Schuster‐Wallace, Susan Watt, Zachariah Mulawa

Bibliographic record

VenueViolence Against Women · 2018
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUnited Nations University Institute for Water, Environment, and HealthMcMaster UniversityAga Khan Foundation
Fundersnot available
KeywordsSanitationHygieneFocus groupTanzaniaEnvironmental healthDomestic violencePoison controlSocioeconomicsEquity (law)Qualitative researchGender equitySuicide preventionMedicinePolitical scienceBusinessSociologyGender studies

Abstract

fetched live from OpenAlex

The purpose of this study was to better understand the gender violence risks that exist in communities where poor water, sanitation, and hygiene (WaSH) access is a known problem. Focus groups and key informant interviews were used to capture the lived experiences of community and health care practitioners from Rwanda, Tanzania, Uganda, and Kenya. This article provides lived narratives of the various cultural and environmental conditions leading to assaults directly attributable to inadequate WaSH. The results shed light on the complex intersections between water access and violence and have significant implications for achieving gender equity and universal access to WaSH.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0070.008
Scholarly communication0.0040.004
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.282
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 source (direct Gemma or distilled Codex), 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

Citations120
Published2018
Admission routes1
Has abstractyes

Explore more

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