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Record W4403555546 · doi:10.2196/63415

Assessment of Health and Well-Being Effects Associated With the Challenging Drinking Water Situation in the Gaza Strip: Protocol for a Cross-Sectional Household Survey Study

2024· article· en· W4403555546 on OpenAlexvenueno aff
Curdin Brugger, Dominik Dietler, Bassam Abu Hamad, Tammo van Gastel, Federico Sittaro, Rodolfo Rossi, Branwen Nia Owen, Nicole Probst‐Hensch, Mirko S. Winkler

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsPalestineGaza stripCross-sectional studyEnvironmental healthPreprintProtocol (science)MedicineGeographyAlternative medicineAncient historyComputer scienceHistoryWorld Wide Web

Abstract

fetched live from OpenAlex

BACKGROUND: The water supply in the Gaza Strip, Palestine, has been unstable and under strain for decades, resulting in major issues with drinking water quality, reliability, and acceptability. In 2018, between 25% and 30% of Gazans did not have regular access to running water. The progressive deterioration of water infrastructure and concerns over the quality of piped water have resulted in a complex mix of drinking water sources used in the Gaza Strip. The challenges of safe water provision in the Gaza Strip could potentially have severe adverse effects on the population's health and well-being. OBJECTIVE: The main objectives of this survey are to determine the quality of drinking water at the household level and to investigate the association of various health outcomes with water quality at the household level in the Gaza Strip. METHODS: We conducted a cross-sectional household survey in the North Gaza, Gaza, and Rafah governorates between January and March 2023. We selected a subsample of households from a representative cross-sectional survey conducted in the Gaza Strip in 2020 with persons aged 40 years and older. From each household in the 2023 survey, we invited 3 individuals (2 older than 40 years and 1 between 18 and 30 years) to participate. The face-to-face interview included questions on drinking water, mental health and well-being, self-reported diagnoses for selected diseases, use of antibiotics, and knowledge about antimicrobial resistance. Additionally, we measured each participant's blood pressure. We sampled drinking water from each household and analyzed the samples for microbial contamination, nitrate, sodium, and mineral content. RESULTS: In total, we visited 905 households and interviewed 2291 participants. In both age groups, more female participants were interviewed. A total of 56.60% (914/1615) were aged ≥40 years, and 58.9% (398/676) were aged between 18 and 30 years. We obtained water samples from nearly all households (902/905, 99.8%). The results are expected to be published in several papers in 2025. CONCLUSIONS: The extensive survey components, coupled with drinking water testing and building on an existing survey, allowed us to identify a broad set of potential impacts on health and well-being and to track changes over time. This study intends to identify humanitarian and development interventions that could impact the population served most. However, we completed data collection before the escalation of violence in October 2023. Given the impact of the still ongoing conflict, the initial intent of this work is no longer valid. However, the results emerging from the survey may still serve as a valuable baseline to assess the impacts of the current escalations on physical and mental health and on drinking water quality. In addition, our findings could provide important information for rebuilding the Gaza Strip in a more health-promoting way. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/63415.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.025
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.011
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.002
Science and technology studies0.0040.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0250.006

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 head0.394
GPT teacher head0.574
Teacher spread0.180 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreProtocol

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

Citations3
Published2024
Admission routes1
Has abstractyes

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