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Record W2044561049 · doi:10.2166/wh.2014.009

Issues in access to safe drinking water and basic hygiene for persons with physical disabilities in rural Cambodia

2014· article· en· W2044561049 on OpenAlexaff
Marin MacLeod, Mala Pann, Ray Cantwell, Spencer Moore

Bibliographic record

VenueJournal of Water and Health · 2014
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsQueen's University
Fundersnot available
KeywordsSanitationHygieneEnvironmental healthThematic analysisPsychological interventionPopulationPhysical accessBusinessRural areaMedicineQualitative researchNursingComputer security

Abstract

fetched live from OpenAlex

An estimated 1.6 million people die from diarrheal diseases each year due to lack of access to safe water and sanitation, and persons with physical disabilities face additional barriers. In Cambodia, approximately 5% of the population is disabled, presenting substantial obstacles in accessing these basic services. The purpose of this study was twofold: first, to identify the challenges facing persons with physical disabilities in accessing safe household water and basic hygiene in rural Cambodia; and, second, to use these results to generate policy and practice recommendations for the water and sanitation hygiene sector implementing water treatment system interventions in rural settings. Fifteen field interviews were conducted with persons with physical disabilities. Thematic analysis was used to identify six main themes. The results indicated that environmental barriers to access were greater in the workplace than household settings and those persons with disabilities had greater awareness about safe drinking water compared to basic hygiene. Additionally, lack of physical strength, distance to water, and lack of financial means were noted as common access barriers. The findings support ongoing research and offer insight into the particular challenges facing persons with physical disabilities in rural areas in accessing safe drinking water and basic hygiene.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.267

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 head0.028
GPT teacher head0.338
Teacher spread0.309 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

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