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OBSSR Connector Monthly Newsletter

The Office of Behavioral and Social Sciences Research (OBSSR) Connector Monthly Newsletter is a monthly e-newsletter featuring updates from OBSSR Director William T. Riley, Ph.D., information about behavioral and social sciences in the news, events and announcements, findings from recently published research, funding announcements, and other updates. The current newsletter is provided below.
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May 22, 2020

Director's Voice

Virtual NIH Matilda White Riley Behavioral and Social Sciences Honors on June 8.

This year marks OBSSR’s 25th anniversary and also the first year that we will conduct the Matilda White Riley (MWR) Honors virtually due to the social distancing necessities of the COVID-19 pandemic. Although we cannot conduct this ceremony in person this year, we have an exceptional slate of awardees, and I hope you’ll block out 9:00 AM to 12:15 PM on Monday, June 8 to listen to their presentations.

Our NIH Matilda White Riley Distinguished Lecturer for 2020 is Toni Antonucci, Ph.D., Elizabeth M. Douvan Collegiate Professor of Psychology and Program Director and Research Professor in the Life Course Development Program at the Institute for Social Research at the University of Michigan. Dr. Antonucci’s research has improved our understanding of how social relations and networks impact health across the lifespan and particularly how social relations influence one’s ability to manage life’s challenges — a particularly timely research area given the pandemic challenges in the context of constrained social relations. Dr. Antonucci received funding from various NIH Institutes including NIMH and NIA and is a recipient of the Research Career Development Award. Dr. Antonucci’s career contributions to the behavioral and social sciences are quite worthy of this award named in honor of Matilda White Riley and the prior awardees of this prestigious award.

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Research Spotlights

Findings from Recently Published Research

Evaluating the risk of school violence using natural language processing and machine learning

Evaluating the risk of school violence using natural language processing and machine learning

School violence can be extremely traumatic and devastating to all involved. In a recent publication, researchers supported by NCATS, NHGRI, NLM, the Agency for Healthcare Research and Quality, and Cincinnati Children’s Hospital Medical Center, developed a risk assessment program to determine the likelihood that a high school student was at risk of participating in an act of school violence. Current practices for these types of assessments rely primarily on the clinicians’ subjective impression in determining the individual’s risk levels and the methods are expensive and not scalable. The focus of this study was to improve current risk assessment practices by developing a natural language processing (NLP) and machine learning process to automate and improve the risk assessment process.
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Children can “catch” their parents’ hidden emotions through synchronization of physiological responses

Children can “catch” their parents’ hidden emotions through synchronization of physiological responses

Previous research has established that parents can influence children’s emotional responses through direct and subtle behavior. In a study supported by grants from the NIMH, National Science Foundation, and the Amini Foundation, researchers examined this relationship further to determine if parents’ experiences of negative emotion impact children’s emotions. Parents have a significant influence on their children’s developing self-regulatory skills and recent research indicates that parents also influence their children’s affective states by transmitting their own affective states to their children through synchronization of physiological responses. In the current study, the researchers investigated how parents’ acute stress responses are transmitted to their children and how parental emotional suppression would affect parents’ and children’s physiological responses and behavior.
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A risk-prediction model using patient electronic health records may help predict suicide risk in diverse populations

A risk-prediction model using patient electronic health records may help predict suicide risk in diverse populations

Suicide is a leading cause of mortality in the U.S. with a 30 percent increase in suicide-related deaths between 2000 and 2016. In order to effectively employ interventions for suicide, early and accurate identification of individuals at high risk for suicide are needed. In the current study, supported by grants from the NIMH, NCATS, Patient-Centered Outcomes Research Institute, Tommy Fuss Fund, Cancer Prevention Research Institute of Texas, Cullen Trust for Health Care, a Tepper Family MGH Research Scholarship, and the Demarest Lloyd Jr Foundation, researchers sought to improve and validate a risk-detection tool for suicide. Advances in automated techniques and the increase in the availability of longitudinal health data provide the opportunity for improved risk-detection tools.
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In the Know

Events and Announcements

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Funding Announcements

Recently Published FOAs

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Protocol Template for Behavioral and Social Sciences Research

Resource for communicating the science, methods, and operations of a clinical trial

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