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Record W3044840189 · doi:10.2166/wh.2020.162

COVID-19: urgent actions, critical reflections and future relevance of ‘WaSH’: lessons for the current and future pandemics

2020· article· en· W3044840189 on OpenAlex
Guy Howard, Jamie Bartram, Clarissa Brocklehurst, John M. Colford, Federico Costa, David Cunliffe, Robert Dreibelbis, Joseph Neil Spindel Eisenberg, Barbara Evans, Rosina Gironés, Steve E. Hrudey, Juliet Willetts, Caradee Y. Wright

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.

Bibliographic record

VenueJournal of Water and Health · 2020
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHygieneSanitationPandemicPreparednessGovernment (linguistics)BusinessPromotion (chess)Hand washingEnvironmental healthInvestment (military)Vulnerability (computing)Economic growthDiseaseCoronavirus disease 2019 (COVID-19)MedicinePolitical scienceEconomicsInfectious disease (medical specialty)Computer security

Abstract

fetched live from OpenAlex

The COVID-19 pandemic placed hygiene at the centre of disease prevention. Yet, access to the levels of water supply that support good hand hygiene and institutional cleaning, our understanding of hygiene behaviours, and access to soap are deficient in low-, middle- and high-income countries. This paper reviews the role of water, sanitation and hygiene (WaSH) in disease emergence, previous outbreaks, combatting COVID-19 and in preparing for future pandemics. We consider settings where these factors are particularly important and identify key preventive contributions to disease control and gaps in the evidence base. Urgent substantial action is required to remedy deficiencies in WaSH, particularly the provision of reliable, continuous piped water on-premises for all households and settings. Hygiene promotion programmes, underpinned by behavioural science, must be adapted to high-risk populations (such as the elderly and marginalised) and settings (such as healthcare facilities, transport hubs and workplaces). WaSH must be better integrated into preparation plans and with other sectors in prevention efforts. More finance and better use of financing instruments would extend and improve WaSH services. The lessons outlined justify no-regrets investment by government in response to and recovery from the current pandemic; to improve day-to-day lives and as preparedness for future pandemics.

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)0.001
metaresearch head score (Gemma)0.002
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.902
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.454
GPT teacher head0.542
Teacher spread0.088 · 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