Network and Proximity Effects on LGBTQ Youth's Psychological Outcomes during a Camp Intervention.

Research has found a tendency for youth's psychological states to influence their friendship development, and vice versa. Whether this occurs among LGBTQ youth in the context of identity-affirming intervention programming has not been established. The current study provides a longitudinal assessment of self-esteem, depressive symptoms, and anxiety in a network of 238 youth ages 12 to 18 participating in a summer camp for young LGBTQ people. Results showed youth experienced significant increases in self-esteem and decreases in depressive symptoms and anxiety at camp. Peer proximity based on cabin assignment influenced youth's depressive symptoms over time. The network processes of peer selection and influence did not significantly affect psychological outcomes. Our findings highlight the impact of affirming programming on the self-esteem, depressive symptoms, and anxiety of LGBTQ youth and the influence of intervention-based proximity on youth's depressive symptoms over time.

User-Agent Bond in Generalizable Environments: Long-Term Risk-Reduction via Nudged Virtual Choices.

Avatars or agents are digitized self-representations of a player in mediated environments. While using agents to navigate through mediated environments, players form bonds with their self-agents or characters, a process referred to as identification. Identification can involve automatic, but temporary, self-concept "shifts in implicit self-perceptions" (Klimmt et al., 2010, p. 323) of the media user by adopting or emphasizing the action choices on behalf of the social expectation of the avatar in the mediated environment. In the current study, we test the possibility that users' identification with video game avatars-a bond built between avatars and players- would account for subsequent behavior changes. We did so by using 3-month longitudinal data involving a narratively-based serious game: Socially Optimized Learning in Virtual Environments (SOLVE), a 3D-interactive game designed to reduce risky sexual behaviors among young men who have sex with men (n = 444). Results show that video game identification predicts both the reduction of risky sexual behaviors over time, and reduction in the number of non-primary partners with whom risky sex occurs. And when players identify with the game character, they tend to make healthier choices, which significantly mediates the link between video game identification and reduction of risky behaviors.

"She Finally Smiles for Real": Reducing Depressive Symptoms and Bolstering Resilience Through a Camp Intervention for LGBTQ Youth.

While summer camps are a recognized evidence-based strategy for building social and emotional skills among youth (U.S. Centers for Disease Control and Prevention [CDC], 2009), no known studies have evaluated the effects of camp programming for LGBTQ youth in the United States. This pilot study evaluates a novel program (Brave Trails) for LGBTQ youth ages 12 to 20, using a pre-post camper survey (N = 56) and a post-camp parent survey (N = 54). Results show campers experienced increases in identity affirmation and hope and a reduction in depressive symptoms. Regression analyses found changes in identity affirmation predicted reductions in depressive symptoms and increases in resilience. Additionally, campers' experience of key camp programming features predicted changes in depressive symptoms. Findings from the parent survey were consistent with camper survey results. Theoretical and practical implications are discussed.

Causal Inference in Generalizable Environments: Systematic Representative Design.

Causal inference and generalizability both matter. Historically, systematic designs emphasize causal inference, while representative designs focus on generalizability. Here, we suggest a transformative synthesis - Systematic Representative Design (SRD) - concurrently enhancing both causal inference and "built-in" generalizability by leveraging today's intelligent agent, virtual environments, and other technologies. In SRD, a "default control group" (DCG) can be created in a virtual environment by representatively sampling from real-world situations. Experimental groups can be built with systematic manipulations onto the DCG base. Applying systematic design features (e.g., random assignment to DCG versus experimental groups) in SRD affords valid causal inferences. After explicating the proposed SRD synthesis, we delineate how the approach concurrently advances generalizability and robustness, cause-effect inference and precision science, a computationally-enabled cumulative psychological science supporting both "bigger theory" and concrete implementations grappling with tough questions (e.g., what is context?) and affording rapidly-scalable interventions for real-world problems.