Gambling behavior is not a unique behavior. There are certain differences in behavior, gambling habits, gambling beliefs, and their reflection in psychosocial life. We have compared three groups of adult male gamblers—sports gamblers (n = 41), machine gamblers (n = 36), and poker gamblers (n = 35)—in regard to measures of personal status and legal-social characteristics. We found no difference between groups in terms of the length of gambling behavior, personal status, or age. We found no legal difference between groups in terms of the number of court cases for debt, stealing, or family court cases. In terms of economic circumstances, sports gamblers suffered more losses than the other groups (p < 0.0001). There were higher rates of bankruptcy among sports gamblers compared with machine gamblers (p < 0.01). Sports gamblers were more likely to borrow money from the black market compared with the other groups (p < 0.01). In terms of mental health, sports and machine gamblers had more suicidal thoughts and gestures than poker gamblers (p < 0.05), whereas the rate of suicide attempts was higher in machine gamblers compared with poker players (p < 0.05). Our results indicated higher vulnerability in sports gamblers in terms of economic problems compared with the other groups, whereas machine gamblers had vulnerability to suicidal thoughts and suicidal attempts compared with poker gamblers.
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http://dx.doi.org/10.1007/s10899-014-9462-5 | DOI Listing |
Sci Rep
January 2025
Institute for the Future of Human Society, Kyoto University, Kyoto, Japan.
Objective digital measurement of gamblers visiting gambling venues is conducted using cashless cards and facial recognition systems, but these methods are confined within a single gambling venue. Hence, we propose an objective digital measurement method using a transformer, a state-of-the-art machine learning approach, to detect total gambling venue visitations for gamblers who visit multiple gambling venues using sounds in gamblers' environments. We sampled gambling and nongambling event datasets from websites to create a gambling play classifier.
View Article and Find Full Text PDFJ Prev (2022)
November 2024
Addictive Behaviors Research Group (GCA), Department of Psychology, Faculty of Psychology, University of Oviedo, Plaza Feijoo S/N, 33003, Oviedo, Spain.
Soc Sci Med
December 2024
Epidemiology and Health Research Lab, Institute of Clinical Physiology of the Italian National Research Council (CNR-IFC), Via Giuseppe Moruzzi 1, Pisa, 56124, Italy. Electronic address:
Objective: This study aims to identify risk factors associated with gambling engagement and the likelihood of problem behavior, distinguishing by type of gambling activity and examining the impact of online gambling.
Methods: Data about 85,420 students aged 16 from 33 countries participating in the 2019 European School Survey Project on Alcohol and Other Drugs (ESPAD) were analyzed through a three-stage sequential probit model, specifically focusing on four types of activity: lotteries, slot machines, cards, and betting. Furthermore, predicted probabilities were calculated for subsamples of students engaging in different types of gambling activities to explore their influence on the likelihood of problem gambling behavior, conditioned on online gambling involvement.
J Gambl Stud
October 2024
Department of Nursing Management and Education, The University of Dodoma, Dodoma, Tanzania.
Lifetime gambling activities and behaviors are considered as potentially addictive behaviors that may impact a student's performance. According to a survey conducted in Tanzanian's higher training institutions, for example, 37.2% of sports gamblers were students.
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