The Role of Gender in Adolescents' Social Networks and Alcohol, Tobacco, and Drug Use: A Systematic Review

Jacobs, Wura; Goodson, Patricia; Barry, Adam E.; McLeroy, Kenneth R.

doi:10.1111/josh.12381

Cited by 32 publications

(25 citation statements)

References 46 publications

(99 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Social network analysis adds, therefore, a unique perspective to framing, understanding, and intervening upon health problems. Studies of social networks have been successful, for example, in generating better understanding of compulsive exercise (Patterson & Goodson, 2017), drinking (Dumas, Graham, Maxwell-Smith, & Wells, 2015), stress (Kornienko, Clemans, Out, & Granger, 2013), substance use (Jacobs, Goodson, Barry, & McLeroy, 2016;Jacobs, Jeon, Goodson, & Valente, 2016), co-occurrence of risky behaviors (Jeon & Goodson, 2015), sexual behaviors (Swartout, 2013), social capital (Hanson et al, 2008;Whitham, 2012), and information seeking behaviors (Veinot, 2009), among others.…”

Section: How Many Pairs Of Nodes Connected Through a Given Nodementioning

confidence: 99%

The Spread and Utility of Social Network Analysis across a Group of Health Behavior Researchers

Patterson¹,

MEd²,

Goodson³

2019

Health Behavior Research

Self Cite

View full text Add to dashboard Cite

Social network analysis (SNA), both as theory and methodology, is a powerful framework for delimiting and studying health behaviors. Using SNA allows scholars to answer new research questions, innovatively investigate the social and systemic contexts of health and behavior, and collaborate on multi-or interdisciplinary projects. As a result, SNA is growing in popularity within health behavior research and practice. Despite SNA's contribution and appeal, few health behavior researchers and practitioners have access to formal SNA education; much of the current training efforts occur outside degree-granting curricula. Therefore, the aims of this paper were to: 1) assess the diffusion of SNA, over time, among scholars presenting at AAHB annual meetings; and 2) determine whether AAHB can function as a professional venue for fostering development of SNA-related skills, especially by capitalizing on mentoring relationships. To assess the "spread" of SNA among AAHB scholars, we conducted a network analysis to capture the connections among those presenting research posters between 2016 and 2019. Results indicated sizeable increases in adoption of, and exposure to SNA within this network. Based on these findings, we recommend responding to the growing trends of SNA use by providing conferencebased training and education in SNA. We also propose utilizing mentorship ties as leverage points in diffusing SNA within a system of professional scholars and, as a result, advancing health behavior research and practice.

show abstract

Section: How Many Pairs Of Nodes Connected Through a Given Nodementioning

confidence: 99%

The Spread and Utility of Social Network Analysis across a Group of Health Behavior Researchers

Patterson¹,

MEd²,

Goodson³

2019

Health Behavior Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Second, most studies only examine a single type of network connection, most frequently the social network such as friendship networks [17]. However, other network types such as drug use networks (i.e., individuals one uses drugs with) have received less attention, but may provide important information that is not typically captured in social networks alone.…”

Section: Introductionmentioning

confidence: 99%

Not getting high with a little help from your friends: Social versus drug network correlates of marijuana use among YMSM

Janulis

Birkett

Phillips

et al. 2019

Addictive Behaviors

View full text Add to dashboard Cite

Substantial evidence has documented the importance of social connections in shaping health and drug use behaviors among adolescents and young adults. The current study extends previous research into the associations between network characteristics and drug use behavior among young men who have sex with men (YMSM) by 1) examining multiple network characteristics, 2) simultaneously assessing multiple network types (i.e., social and drug use), and 3) examining change in network characteristics and drug use behavior over time. Data for the current study comes from RADAR, a longitudinal cohort study of YMSM. Latent growth curve models examined the change in frequency of marijuana use across four observations and individual and network correlates of this change including: demographics, drug network size, drug network density, social network size, and social network density. Baseline frequency of marijuana use was positively associated with drug network size and density, while it was inversely related to social network size and density. In addition, increasing frequency of marijuana use was associated with increases in drug network size and density, while it was associated with decreases in social network size. These findings highlight the complexity of multiple network types (e.g., drug and social) and network structures (e.g., size and density) in understanding drug use behavior among YMSM. Furthermore, as changes in drug and social networks may be indicative of changes in marijuana use, peer relationships may be especially important in understanding an individual's trajectories of marijuana use.

show abstract

“…behaviors including: smoking [15]; substance use [16]; mental health, HIV practices, family planning, dieting, exercise [17]; obesity among adults [18,19] and children [20][21][22][23]. Much of the existing obesity literature utilized social network approaches to explore the role of social influence on behavior change at an individual level [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Networks for prevention in 19 communities at the start of a large-scale community-based obesity prevention initiative

Marks

Sanigorski

Owen

et al. 2018

Translational Behavioral Medicine

View full text Add to dashboard Cite

Community-based obesity prevention efforts are dependent on the strength and function of collaborative networks across multiple community members and organizations. There is little empirical work on understanding how community network structure influences obesity prevention capacity. We describe network structures within 19 local government communities prior to a large-scale community-based obesity prevention intervention, Healthy Together Victoria, Australia (2012-2015). Participants were from a large, multi-site, cluster randomized trial (cRCT) of a whole-of-systems chronic disease prevention initiative. Community leaders from 12 intervention and seven comparison (non-intervention) regions identified and described their professional networks in relation to dietary, physical activity, and weight status among young children (<5 years of age). Social network measures of density, modularity, clustering, and centrality were calculated for each community. Comparison of means and tests of association were conducted for each network relationship. One-hundred and seven respondents (78 intervention; 29 comparison) reported on 996 professional network relationships (respondent average per region: 10 intervention; 8 comparison). Networks were typically sparse and highly modular. Networks were heterogeneous in size and relationship composition. Frequency of interaction, close and influential relationships were inversely associated with network density. At baseline in this cRCT there were no significant differences between community network structures of key actors with influence over environments affecting children's diet and physical activity. Tracking heterogeneity in both networks and measured outcomes over time may help explain the interaction between professional networks and intervention effectiveness of community-based obesity prevention.

show abstract

The Role of Gender in Adolescents' Social Networks and Alcohol, Tobacco, and Drug Use: A Systematic Review

Abstract: School-based prevention programs could be strengthened by (1) selecting and targeting peer leaders based on sex, and (2) leveraging other types of social networks beyond simply friendships.

Cited by 32 publications

References 46 publications

The Spread and Utility of Social Network Analysis across a Group of Health Behavior Researchers

The Spread and Utility of Social Network Analysis across a Group of Health Behavior Researchers

Not getting high with a little help from your friends: Social versus drug network correlates of marijuana use among YMSM

Networks for prevention in 19 communities at the start of a large-scale community-based obesity prevention initiative

Contact Info

Product

Resources

About