Login / Signup

Gambler clusters and problem gambling severity: A cluster analysis of Swedish gamblers accessing an online problem gambling screener.

Håkan WallAnne H BermanNitya Jayaram-LindströmClara HellnerIngvar Rosendahl
Published in: Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors (2020)
It has been proposed that overall gambling involvement has a stronger association with problem gambling (PG) than any specific game type. However, few studies have used multiple analytic approaches on the same data set to assess these relationships. The aims of the current study were to identify patterns of gambling activity (PGAs) and to assess the relationships between different game types, PGAs, gambling involvement, and PG as measured by the Problem Gambling Severity Index (PGSI), using two different approaches. In a sample of Swedish gamblers who screened their gambling habits at the Swedish national gambling helpline website (N = 7,463, 79% males), seven different PGAs were identified. Increased gambling involvement was associated with PG severity, and the strength of the association varied by game type. Online casino games and electronic gambling machines had the weakest involvement effect and lotteries the strongest. Almost 50% of the gamblers belonged to the online casino PGA, characterized by online casino gambling. Gamblers in this PGA showed higher PGSI scores compared to three PGAs: online sports/online casino, horse/lottery, and online sports, and they had lower PGSI scores compared to the diverse PGA, characterized by engagement in all game types. No differences in PGSI scores were found between gamblers in three PGAs with high probability of online casino gambling but with varying engagement levels in other game types. In a Swedish context, the results from this study indicate that the focus of prevention and regulation should be on game types with the strongest associations with PG, namely, online casino games. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Keyphrases
  • social media
  • health information
  • emergency department
  • healthcare
  • machine learning
  • artificial intelligence