Human rights, COVID-19, and barriers to safe water and sanitation among people experiencing homelessness in Mexico City
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".