Applied developmental science (ADS) is scholarship that seeks to advance the integration of developmental research with actions-policies and programs-that promote positive development and/or enhance the life chances of vulnerable children and families. Through this integration ADS may become a major means to foster a science for and of the people. It may serve as an exemplar of the means through which scholarship, with community collaboration, may contribute directly to social justice. In so doing, ADS helps shift the model of amelioration, prevention, or optimization research from one demonstrating efficacy to one promoting outreach. When this contribution occurs in the context of university-community partnerships, ADS may serve also as a model of how higher education may engage policy makers, contribute to community capacity to sustain valued programs, and maintain and perpetuate civil society through knowledge-based, interinstitutional systems change.

Community engagement is increasingly becoming an integral part of research. “Community-engaged research” (CEnR) introduces new stakeholders as well as unique challenges to the protection of participants and the integrity of the research process. We—a group of representatives of CTSA-funded institutions and others who share expertise in research ethics and CEnR—have identified gaps in the literature regarding (1) ethical issues unique to CEnR; (2) the particular instructional needs of academic investigators, community research partners, and IRB members; and (3) best practices for teaching research ethics. This paper presents what we know, as well as what we still need to learn, in order to develop quality research ethics educational materials tailored to the full range of stakeholder groups in CEnR.

Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity.

TGNC adolescents expressed interest in multiple family building options, including adoption and biological parenthood, and identified a need for more information about these options. Thus, clinicians working with adolescents should be aware of the unique fertility and reproductive health needs of TGNC youth.

This project examined the attitudes of sexual and gender minority youth (SGMY) toward guardian permission for a pre-exposure prophylaxis (PrEP) adherence trial and their preparedness to provide informed, rational and voluntary self-consent. Sixty sexually active SGMY (ages 14–17) participated in online survey and asynchronous focus group questions after watching a video describing a PrEP adherence study. Youth responses highlighted guardian permission as a significant barrier to research participation, especially for those not “out” to families. Youth demonstrated understanding of research benefits, medical side effects, confidentiality risks, and random assignment and felt comfortable asking questions and declining participation. Reasoning about participation indicated consideration of health risks and benefits, personal sexual behavior, ability to take pills everyday, logistics, and post-trial access to PrEP. Results demonstrate youth’s ability to self-consent to age- and population- appropriate procedures, and underscore the value of empirical studies for informing IRB protections of SGMY research participants.