Social vulnerability and exposure to private well water
Bibliographic record
Abstract
One quarter of the population of Pennsylvania relies on private domestic well water: two-fold greater than the US average. Private well owners are responsible for the maintenance and treatment of their water supply. Targeted interventions are needed to support these well owners to ensure they have access to safe drinking water, free of contaminants. To develop appropriate interventions, an understanding of the characteristics and social vulnerability of communities with high well water use is needed. The purpose of this study was to determine the spatial patterning of social vulnerability in Pennsylvania and assess the association between social vulnerability and private domestic wells using profile regression. Census data and water supply information were used to estimate the proportion of the population using domestic wells. Ten area-level measures of social vulnerability at the census-tract level were investigated, using Bayesian profile regression to link clustering of social vulnerability profiles with prevalence of private domestic wells. Profile regression results indicated 15 distinct profiles of social vulnerability that differ significantly according to the area-level prevalence of domestic well use frequency. Out of these, two profiles of census tracts were identified as socially vulnerable and had a high proportion of well-water users, representing approximately 1.1 million Pennsylvanians or a third of all well water users in the State. High area-level social vulnerability profiles coincide with a high frequency of private well-water use in PA. This study presents a data-driven approach to supporting public health programs aimed at reducing exposure and health risks of chemical and infectious agents in household water supplies by targeting vulnerable populations.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".