Public health and water scarcity: the spatial distribution of quarter-disease patterns and access to clean water and safe drinking water in the water-scarce region of Makassar, Indonesia
Bibliographic record
Abstract
Abstract The scarcity and insufficient or poorly regulated water and sanitation will lead individuals to become vulnerable to different preventable health risks. When people lack even a basic drinking water solution, they rely on surface water and/or wastewater that is not risk-free. Furthermore, WHO (2019) stated that at least 2 billion individuals around the globe are using drinking water sources that are infected with feces. Tallo is one of the sub-districts in Makassar that is experiencing water scarcity, forcingly encouraging the community to use the available water sources, such as dug wells and rainwater. This study aimed to describe the spatial distribution of three-month disease patterns and access to clean water and safe drinking water in Tallo. Primary data was obtained from a face-to-face survey of 98 beneficiaries of Rainwater Harvesting (RWH) Tametotto and water clay filter in Tallo, while the spatial data was obtained from the spatially analyzed data by ArcGIS 8.0. The results revealed that the distribution of three-month diseases highly presented in diarrhea (8%) spatially shown around the river area, dermatitis (5%), and typhoid (3%). In addition, the most consumed drinking water is gallon mineral water, while the highest clean water accessed was from the artesian well in the entire research area. It concluded that the spatially distributed of the quarter diseases was diarrhea in the river area of Tallo (RT 3 and RT 4), and respondents mostly consumed gallons of mineral water for drinking and used artesian wells for Water, Hygiene, and Sanitation (WASH) in the whole area. It encouraged the government sectors to provide an appropriate water supply for Tallo as one of the water-scarce regions in Makassar and deliver education to people regarding WASH and the importance of safe water to avoid the occurrence of waterborne diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".