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Record W4318815864 · doi:10.3389/frwa.2023.1054182

Human rights, COVID-19, and barriers to safe water and sanitation among people experiencing homelessness in Mexico City

2023· article· en· W4318815864 on OpenAlexaff
Carla Liera, Sarah Dickin, Andrea Rishworth, Elijah Bisung, Alexia Moreno, Susan J. Elliott

Bibliographic record

VenueFrontiers in Water · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsQueen's UniversityUniversity of WaterlooUniversity of Toronto
FundersStockholm Environment InstituteStyrelsen för Internationellt Utvecklingssamarbete
KeywordsSanitationDignityHygieneHuman rightsPandemicPublic healthEnvironmental healthUniversal designSocioeconomicsEconomic growthCoronavirus disease 2019 (COVID-19)Political scienceBusinessMedicineSociologyNursingInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Access to safe water, sanitation, and hygiene (WASH) are human rights and play a fundamental role in protecting health, which has been particularly evident during the SARS-CoV2 (COVID-19) pandemic. People experiencing homelessness face frequent violations of their human rights to water and sanitation, negatively affecting their health and dignity and ability to protect themselves from COVID-19. This research aimed to identify barriers to safe water, sanitation and hygiene access for people experiencing homelessness in Mexico City during the COVID-19 pandemic. A survey of 101 respondents experiencing homelessness was conducted using mobile data collection tools in collaboration with El Caracol A.C., an NGO that contributes to the visibility and social inclusion of homeless people in Mexico. We report findings according to the following themes: general economic impacts of COVID-19; experiences with reduced access to WASH services due to COVID-19, challenges in accessing hand washing to follow COVID-19 public health advice; and coping mechanisms used to deal with reductions in access to WASH. We discuss the broader implications of the findings in terms of realization of the human rights to water and sanitation (HRtWS), and how people experiencing homelessness are left behind by the existing approaches to ensure universal access to water and sanitation under SDG 6.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.029
GPT teacher head0.361
Teacher spread0.331 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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