Reported care giver strategies for improving drinking water for young children
Bibliographic record
Abstract
OBJECTIVES: Care givers may engage in a variety of strategies to try and improve drinking water for children. However, the pattern of these efforts is not well known, particularly for young children in high-risk situations. The objective of this study was to determine care giver-reported strategies for young children with (1) undernutrition and (2) living in an unplanned poor peri-urban community in the Dominican Republic. METHODS: Practices reported by care givers of young children from a community and clinic group were extracted from interviews conducted between 2004 and 2008 (n = 563). These results were compared to two previous similar samples interviewed in 1997 (n = 341). RESULTS: Bottled water is currently the most prevalent reported strategy for improving drinking water for young children. Its use increased from 6% to 69% in the community samples over the last decade and from 13% to 79% in the clinic samples. Boiling water continues to be a common strategy, particularly for the youngest children, though its overall use has decreased over time. Household-level chlorination is infrequently used and has dropped over time. CONCLUSIONS: Care givers are increasingly turning to bottled water in an attempt to provide safe drinking water for their children. While this may represent a positive trend for protecting children from water-transmitted diseases, it may represent an inefficient approach to safe drinking water provision that may place a financial burden on low-income families.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".