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Record W2800308041 · doi:10.1186/s13031-018-0159-8

Setting priorities for humanitarian water, sanitation and hygiene research: a meeting report

2018· article· en· W2800308041 on OpenAlexaff
Lauren D’Mello-Guyett, Travis Yates, Andy Bastable, Maysoon Dahab, Claudio Deola, Caetano C. Dorea, Robert Dreibelbis, Timothy Grieve, Thomas Handzel, Anne Harmer, Daniele Lantagne, Peter Maes, Melissa C. Opryszko, Sarah Palmer-Felgate, Brian Reed, Rafaël Van den Bergh, Dominique Porteaud, Oliver Cumming

Bibliographic record

VenueConflict and Health · 2018
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsUniversity of Victoria
FundersDepartment for International DevelopmentWellcome Trust
KeywordsSanitationHygienePsychological interventionHealth services researchPublic healthHumanitarian aidMedicinePublic relationsPolitical scienceEnvironmental healthNursing

Abstract

fetched live from OpenAlex

Recent systematic reviews have highlighted a paucity of rigorous evidence to guide water, sanitation and hygiene (WASH) interventions in humanitarian crises. In June 2017, the Research for Health in Humanitarian Crises (R2HC) programme of Elrha, convened a meeting of representatives from international response agencies, research institutions and donor organisations active in the field of humanitarian WASH to identify research priorities, discuss challenges conducting research and to establish next steps. Topics including cholera transmission, menstrual hygiene management, and acute undernutrition were identified as research priorities. Several international response agencies have existing research programmes; however, a more cohesive and coordinated effort in the WASH sector would likely advance this field of research.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.186
GPT teacher head0.440
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

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 designNot applicable
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

Citations20
Published2018
Admission routes1
Has abstractyes

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