You cannot prevent a disease; you only treat diseases when they occur: knowledge, attitudes and practices to water-health in a rural Kenyan community.
Bibliographic record
Abstract
OBJECTIVE: Almost 1 billion individuals lack access to improved water supplies, with 2.6 billion lacking adequate sanitation. This leads to the propagation of multiple waterborne diseases. The objective of this study was to explore local knowledge, attitudes and practices to understand the mechanisms and pre-conditions for sustainable uptake and use of these facilities. METHODS: Data collection took place in a rural Kenyan community in September 2009. A qualitative approach was taken, with 4 focus groups and 25 in-depth interviews conducted. Participant characteristics varied by age, gender, education, marital status, employment and community standing. RESULTS: Few participants reported current access to improved water and sanitation facilities. Though they expressed desire for latrines and water sources, barriers including lack of funds and social capital, decrease the ability for installation. Participants understood that there was a link between the quality of water and their health, however, perceived benefits of current contaminated sources outweigh the potential health impacts and proliferate their continued use. CONCLUSION(S): While water-health links are understood to varying degrees within the community, contextual (physical environment), compositional (individual) and collective (community) factors interact to influence health. Community challenges, such as lack of unity, lack of education and lack control were identified as the main barriers to initiating change, despite a desire for increased access to safe water and sanitation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".