For the last 20 years, poker has been one of the most popular forms of gambling for adults. Although various studies have demonstrated the specific characteristics of these players, few studies have focused on teenagers playing poker. However, a better understanding of this activity among this vulnerable group would help to develop more effective strategies for preventing gambling problems. Thus, this study aims to identify, through latent class analysis, subgroups in that population and to assess co-occurrence across various characteristics typically associated with gambling behavior. The sample was constituted of 759 adolescents (70.8% boys; age = 15.44 years, range = 14-19) recruited in high schools and who had played poker in the last year. The statistical fit indices revealed a four-class solution. Class 1 almost exclusively played simulated poker. Class 2 played poker exclusively in the school context. Class 3 played poker almost exclusively at home. Class 4 showed a very diversified pattern regarding their modalities of poker playing. Results of the logistic regression suggested that gambling related variables (e.g., time spent playing, reading about gambling strategies and diversity of gambling funding) were significant predictors of class membership. This study shows that there is a variety of profiles among young poker players. Although one profile has few risk factors, others have more factors associated with adults' gambling problems. These profiles suggest that specific prevention strategies are probably appropriate to reach these different groups of young people. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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Data Brief
December 2024
Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor Bahru, Malaysia.
The dataset presents raw data on the egocentric (first-person view) and exocentric (third-person view) perspectives, including 47166 frame images. Egocentric and exocentric frame images are recorded from original iPhone videos simultaneously. The egocentric view captures the details of proximity hand gestures and attentiveness of the iPhone wearer, while the exocentric view captures the hand gestures in the top-down view of all participants.
View Article and Find Full Text PDFSci Rep
September 2024
Department of Psychology, University of British Columbia, Vancouver, BC, Canada.
Chasing refers to the escalation of betting behaviour. It is conventionally seen when losing but can also be seen after wins. Diagnostic and screening items for gambling problems describe chasing as returning 'another day' to gamble.
View Article and Find Full Text PDFJ Behav Addict
June 2024
1Department of Psychology, Centre for Gambling Research at UBC, University of British Columbia, 2136 West Mall, Vancouver, BC, V6T 1Z4, Canada.
Background And Aims: This study characterized chasing behaviour as the time to return to an online gambling website after a losing or a winning visit.
Methods: We analyzed a naturalistic dataset from an eCasino (PlayNow.com, the provincial platform for British Columbia, Canada), comprising 1,909,681 sessions from 15,544 individuals.
J Evid Based Med
December 2023
Institute of Hospital Management, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Objective: Post-traumatic stress disorder (PTSD) is a prevalent condition characterized by prolonged psychological distress following traumatic events encompassing serious health threats, domestic abuse, and natural disasters. The onset and persistence of PTSD can be attributed to advanced age. This study aimed to explore the occurrence and influencing factors of enduring post-traumatic stress disorder (PTSD) in survivors aged 60 years and over in the main area of the 2008 Wenchuan earthquake 10 years after the disaster.
View Article and Find Full Text PDFJ Behav Addict
September 2023
1Health and Well-Being Promotion Unit, Finnish Institute for Health and Welfare, P.O. Box 30, FI-00271, Helsinki, Finland.
Objective And Method: Electronic gambling machines are a prominent cause of significant gambling harms globally. We use simulations of a simplified video poker game to show how changes in game volatility, defined primarily by the size of the main prize, affect patterns of wins and losses as well as winning streaks.
Results: We found that in low- and medium volatility games the proportion of winning players quickly drops to zero after about 30 h of play, while in the high volatility game 5% of players are still winning after playing for 100 h.
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