Environmental Health and\nHousehold Demographics Impacting\nBiosand Filter Maintenance and Diarrhea in Guatemala: An Application\nof Structural Equation Modeling
Bibliographic record
Abstract
In rural health development practice, engineers and scientists\nmust recognize the complex interactions that influence individuals’\ncontact with disease-causing pathogens and understand how household\nhabits may impact the adoption and long-term sustainability of new\ntechnology. The goal of this study was to measure the effect of various\nenvironmental health factors and household demographics on the operation\nand maintenance of the Biosand filter (Centre for Affordable Water\nand Sanitation Technology, Calgary, Alberta, Canada) and diarrhea\nhealth burden in the region. In July and August 2010, randomized household\nsurveys (n = 286) were completed in rural Guatemala\ndetailing water access, sanitation availability, hygiene practice,\nsocio-economic status, education level, filter operation and maintenance,\nand diarrhea health burden of the home. A hypothesized structural\nequation model was developed based on a review of published research\nand tested using the surveyed data. Model-derived parameter estimates\nindicated that: (a) proper personal hygiene practices significantly\npromote proper filter operation and maintenance; and (b) higher household\neducation level, proper filter operation and maintenance, and improved\nwater supply significantly reduce diarrhea health burden. Additionally,\na high level of unexplained variance in diarrhea indicated the filter,\nthough protective of health, is not the only factor influencing diarrhea.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".