An assessment of the effects of environmental health factors and household demographics on the operation and maintenance of the Biosand Filter and diarrhea health burden in rural Guatemala
Bibliographic record
Abstract
Public health policy and environmental interventions must recognize the complex interactions that influence individuals' contact with disease-causing pathogens and how household habits may impact adoption/acceptance of new technology.Understanding these interactions and habits is important for prioritizing expenditures and initializing programs.In August 2008, a water filter construction program was initiated by a non-profit organization near Playa Grande, Ixcán, Guatemala.The program has utilized the laboratory-and field-qualified household Biosand Filter, promoted by the Centre for Affordable Water and Sanitation Technology (Calgary, Alberta, Canada).Upon review, the organization noticed households had varying levels of proper filter operation and maintenance as well as varied diarrhea health response.The goal of this study is to measure the effect of various environmental health factors and household demographics on the operation and maintenance of the Biosand Filter and diarrhea health burden in the region.In July/August 2010, household surveys (n=286) were completed in the study region detailing water access, sanitation availability, hygiene practice, socio-economic status, education level, filter operation and maintenance and diarrhea health burden of the home.A structural equation model was created based on accepted public health practices, a review of published research, and the researcher's previous knowledge of rural health and development practice.Model-derived parameter estimates indicated that: a) proper hygiene practices significantly promote proper filter operation and maintenance; and b) household education level, proper filter operation and maintenance, and improved water supply significantly reduce diarrhea health burden.Additionally a high level of unexplained variance in diarrhea showed the filter, though protective of health, is not the only factor influencing diarrhea.The goal of this study is to measure the effect of various environmental health factors and household demographics on the operation and maintenance of the Biosand Filter and diarrhea health burden in the region surrounding Playa Grande Ixcán, Quiche, Guatemala.It is hoped the results of this study will illustrate how demographics, infrastructure, and practices within the home have a significant effect on proper operation and maintenance of the Biosand Filter.Additionally these effects on filter operation and maintenance may subsequently affect diarrhea health burden in conjunction with other observed independent effects.The results of this study will inform public health policy decisions regarding diarrhea health or technology intervention and direct policy makers to consider the characteristics of the people the policy will impact.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".