Examining the Relationship Between Pediatric Behavioral Health and Parent Productivity Through a Parent-Reported Survey in the Time of COVID-19: Exploratory Study.

BACKGROUND: Pediatric behavioral health needs skyrocketed during the COVID-19 pandemic. Parents and caregivers lacked access to well-established tools to identify risk and protective factors while also experiencing decreased access to treatment options to meet their families' behavioral health needs. OBJECTIVE: The aim of this study is to investigate the associations of known pediatric behavioral health risk factors and parents' reports of workplace productivity. METHODS: A clinical research team at Brightline-a virtual, pediatric behavioral health solution-drew on standardized instruments to create a survey designed to understand pediatric behavioral health conditions, child stress, and family resilience and connection during the COVID-19 pandemic. Multivariable linear regression was used to characterize the relationship between these variables and parents' reports of workplace productivity. RESULTS: Participants (N=361) completed the survey between October 2020 and November 2021. In the multivariable model, higher pediatric stress and time spent managing children's behavioral health needs were associated with greater productivity loss among working parents, whereas higher family connection was associated with lower productivity loss. COVID-19 diagnoses among parents and dependents, financial impact of COVID-19 on households, and family resilience were not associated with parents' workplace productivity. CONCLUSIONS: This survey captured child stress, family connection, and productivity as reported by parents and caregivers during the COVID-19 pandemic. Exploratory studies are the first step in understanding the relationship between these variables. The results from this study can empower parents by providing insights to help manage their child's behavioral health concerns and identify pediatric behavioral health services to aid working parents who are caregivers.

Statistical mechanical load balancer for the web.

The maximum entropy principle from statistical mechanics states that a closed system attains an equilibrium distribution that maximizes its entropy. We first show that for graphs with fixed number of edges one can define a stochastic edge dynamic that can serve as an effective thermalization scheme, and hence, the underlying graphs are expected to attain their maximum-entropy states, which turn out to be Erdös-Rényi (ER) random graphs. We next show that (i) a rate-equation-based analysis of node degree distribution does indeed confirm the maximum-entropy principle, and (ii) the edge dynamic can be effectively implemented using short random walks on the underlying graphs, leading to a local algorithm for the generation of ER random graphs. The resulting statistical mechanical system can be adapted to provide a distributed and local (i.e., without any centralized monitoring) mechanism for load balancing, which can have a significant impact in increasing the efficiency and utilization of both the Internet (e.g., efficient web mirroring), and large-scale computing infrastructure (e.g., cluster and grid computing).