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Social Drinking Groups and Risk Experience in Nightclubs: Latent Class Analysis.

Beth BourdeauBrenda A MillerRobert B VoasMark B JohnsonHilary F Byrnes
Published in: Health, risk & society (2017)
Nightclubs are a setting in which young adults purposefully seek out experiences, such as drug use and alcohol intoxication that can expose them to physical harm. While physical harm occurs fairly frequently within clubs, many patrons have safe clubbing experiences. Further, not all patrons experience potential harms the same way, as there are differences in aggression and intoxication. In this article we draw on data from a research study in which we sought to better understand the role of social drinking groups in experiences of risk within nightclubs, as the majority of patrons attend with others. We collected data from 1,642 patrons comprising 615 social drinking groups as they entered and exited nightclubs in a major U.S. city. We focused on six experiences that might cause physical harm: alcohol impairment, alcohol intoxication, drug use, physical aggression, sexual aggression, and impaired driving. We aggregated patron responses across social groups and used latent class statistical analysis to determine if and how experiences tended to co-occur within groups. This analysis indicated there were five distinct classes which we named Limited Vulnerability, Aggression Vulnerability, Substance Users, Impaired Drivers and Multi-Issue. We assessed the groups within each class for distinctions on characteristics and group context. We found differences in the groups in each class, such as groups containing romantic dyads experienced less risk, while those groups with greater familiarity, greater concern for safety, and higher expectations for consumption experienced more risk. Group composition has an impact on the experiences within a club on a given night, in particular when it comes to risk and safety assessment.
Keyphrases
  • mental health
  • young adults
  • healthcare
  • physical activity
  • alcohol consumption
  • climate change
  • big data