While recreational cannabis laws have significantly reduced arrests for cannabis possession and sales, racial disparities in arrests still exist, according to a new study from Weill Cornell Medicine, Instituto Tecnológico Autónomo de México and The University of Texas at Austin. The authors suggest that recreational cannabis laws do not fully resolve underlying systemic inequities.

Talking helps if you’ve been through a difficult experience. But for children who have been victims of or witnesses to domestic violence, this isn’t always the case. They don’t always perceive traditional support figures such as parents and friends as safe or helpful, researchers write in a study published this week in the Journal of Child & Adolescent Trauma.

The widespread adoption of artificial intelligence (AI)—particularly in “hidden” everyday applications—is creating a new and distinct form of digital inequality.

Targeting only a small group of well-connected individuals with smoking reduction interventions produces the largest reductions in smoking, researchers report. The findings provide actionable insights for designing social network-based interventions that leverage peer influence effectively. Peer influence plays a powerful role in shaping adolescent behavior, particularly in health-related habits such as smoking, as behaviors and norms spread through social networks during a developmentally sensitive period. Although prior research has shown that such influences can cascade through friendships and even friends-of-friends, how quickly the influence fades with social distance, and how social network structure affects that process, has remained unclear. Using Stochastic Actor-Oriented Models (SAOMS) and data from 3,154 students across two schools in the National Longitudinal Study of Adolescent to Adult Health (Add Health) dataset, Cheng Wang and colleagues investigated how peer influence decays across social distance. To better understand how behavioral effects spread and diminish, they evaluated the effects of different smoking reduction intervention strategies, targeting either random students or those most central within social networks. Wang et al. found that peer influence in the context of smoking reduction interventions extends up to three degrees of separation from intervention targets. Targeting 10 to 30% of highly connected individuals within a social network produced the greatest reductions in smoking, while increasing coverage beyond 40 to 50% yielded diminishing returns due to network saturation. Moreover, the authors discovered that social network structure mattered. For example, influence spread more widely and decayed more slowly in denser networks, while sparser networks showed more limited diffusion.