Tools for a comprehensive assessment of public health risks associated with limited sanitation services provision
Bibliographic record
Abstract
Three water, sanitation and hygiene (WASH) support tools were applied to Kampala city, Uganda, to evaluate areas with the highest health hazard due to poor wastewater and faecal sludge management and to develop interventions to improve sanitation and reduce exposure. The Pathogen Flow and Mapping Tool (PFMT) assessed how different sanitation management interventions influence pathogen emissions to surface water using rotavirus as the indicator pathogen, while the HyCRISTAL health hazard tool evaluated how flooding and drainage infrastructure influence the presence of human excreta in the environment. The SaniPath tool identified common high-risk pathways of exposure to faecal contamination in food, open drains and floodwater. An overlap in high health hazard hotspot areas was identified by the PFMT and the HyCRISTAL tools. Across the city, the most important hazard sources were the indiscriminate disposal of faecal waste into open stormwater drains from onsite sanitation technologies, open defecation and the insufficient treatment of wastewater. The SaniPath tool identified drain water, floodwater, street food and uncooked produce as the dominant faecal exposure pathways for selected parishes in the city, demonstrating the presence of excreta in the environment. Together, the tools provide collective evidence guiding household, community, and city-wide sanitation, hygiene and infrastructure management interventions from a richer assessment than when a single tool is applied. For areas with high spatial risks, those practising open defecation, and for low-lying areas, these interventions include the provision of watertight pit latrines or septic tanks that are safely managed and regularly emptied. Faecal sludge should be emptied before flood events, direct connections of latrines to open storm drains should be prevented, and the safe handling of food and water promoted. The tools enhance decision making for local authorities, and the assessments can be replicated in other cities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".