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Record W2521023153 · doi:10.1016/j.proenv.2016.09.013

Facilitators and Barriers to Effective Water and Sanitation Interventions for Characterizing Shigellosis Incidence in Jiangsu, China

2016· article· en· W2521023153 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProcedia Environmental Sciences · 2016
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Waterloo
FundersMitacs
KeywordsSanitationShigellosisPsychological interventionEnvironmental healthChinaImproved sanitationSocioeconomic statusOpen defecationBusinessDiarrhoeal diseaseIncidence (geometry)MedicineEnvironmental planningGeographyNursingShigellaPopulationDiarrhea

Abstract

fetched live from OpenAlex

Effective water and sanitation interventions can help to reduce the prevalence of waterborne diseases such as shigellosis, which is a diarrheal disease prevalent in rural areas of China. Water and sanitation interventions can be strengthened or undermined by facilitators and barriers, which are factors that assist or hinder access to safe water and adequate sanitation. Facilitators and barriers can be assessed using a conceptual framework of socioeconomic and water and sanitation determinants to understand the prevalence of shigellosis. Insight into facilitators and barriers can help various stakeholders to strategize with communities to implement a sensible solution to the rural water crisis in China.

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.

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 score0.095
Threshold uncertainty score0.231

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 communication0.0000.001
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 head0.010
GPT teacher head0.265
Teacher spread0.254 · 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