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Record W4323359968 · doi:10.1080/09687599.2023.2181770

Access to water, sanitation and hygiene (WASH) for persons with disabilities in school settings: A call for research

2023· article· en· W4323359968 on OpenAlexafffund
Ebenezer Dassah, Elijah Bisung

Bibliographic record

VenueDisability & Society · 2023
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsQueen's University
FundersCanadian Institutes of Health ResearchKwame Nkrumah University of Science and Technology
KeywordsSanitationHygieneUniversal designBusinessEnvironmental healthResource (disambiguation)Service (business)PsychologyPublic relationsMedical educationMedicinePolitical scienceMarketingComputer science

Abstract

fetched live from OpenAlex

Improving access to water, sanitation and hygiene (WASH) services in school settings is critical in addressing access disparities experienced by persons with disabilities. As such, the Sustainable Development Goals (SDGs) established ambitious targets which aim to achieve universal access to water and sanitation by 2030. Despite this, access to inclusive WASH services in schools remain a big challenge in many resource-constrained settings. This review seeks to examine access to WASH for persons with disabilities in school settings. We undertook a review to identify a wide range of evidence from peer-reviewed sources. We identified only two studies, and they revealed environmental, social and institutional barriers that negatively affect persons with disabilities’ access to WASH services. We concluded the review with a call for urgent attention to build on this knowledge base as well as practical steps to improve WASH service provision in school settings in low- and middle-income countries.

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)0.029
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: Commentary · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0040.006
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.089
GPT teacher head0.405
Teacher spread0.316 · 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
GenreCommentary

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

Citations9
Published2023
Admission routes2
Has abstractyes

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