Improved Decision Making for Water Lead Testing in U.S. Child Care Facilities Using Machine-Learned Bayesian Networks.

Tap water lead testing programs in the U.S. need improved methods for identifying high-risk facilities to optimize limited resources.

Predicting the risk of GenX contamination in private well water using a machine-learned Bayesian network model.

Per- and polyfluoroalkyl substances (PFAS) are emerging contaminants that pose significant challenges in mechanistic fate and transport modeling due to their diverse and complex chemical characteristics. Machine learning provides a novel approach for predicting the spatial distribution of PFAS in the environment. We used spatial location information to link PFAS measurements from 1207 private drinking water wells around a fluorochemical manufacturing facility to a mechanistic model of PFAS air deposition and to publicly available data on soil, land use, topography, weather, and proximity to multiple PFAS sources.

Risk Communication and Factors Influencing Private Well Testing Behavior: A Systematic Scoping Review.

Unregulated private wells may be at risk for certain types of contamination associated with adverse health effects. Well water testing is a primary method to identify such risks, although testing rates are generally low. Risk communication is used as an intervention to promote private well testing behavior; however, little is known about whether these efforts are effective as well as the mechanisms that influence effectiveness.

Nursing theory: its importance to practice.

Background: Nursing theory should provide the principles that underpin practice and help to generate further nursing knowledge. However, a lack of agreement in the professional literature on nursing theory confuses nurses and has caused many to dismiss nursing theory as irrelevant to practice. This article aims to identify why nursing theory is important in practice.