Predictors of gambling and problem gambling in Victoria, Australia [open access policy paper]

Abstract: In 2016, the gambling habits of a sample of 3361 adults in the state of Victoria, Australia, were surveyed. It was found that a number of factors that were highly correlated with self-reported gambling frequency and gambling problems were not significant predictors of gambling frequency and problem gambling. The major predictors of gambling frequency were the degree to which family members and peers were perceived to gamble, self-reported approval of gambling, the frequency of discussing gambling offline, and the participant’s Canadian Problem Gambling Severity Index (PGSI) score. Age was a significant predictor of gambling frequency for certain types of gambling (e.g. buying lottery tickets). Approximately 91% of the explainable variance in the participant’s PGSI score could be explained by just five predictors: Positive Urgency; Frequency of playing poker machines at pubs, hotels or sporting clubs; Participation in online discussions of betting on gaming tables at casinos; Frequency of gambling on the internet, and Overestimating the chances of winning. Based on these findings, suggestions are made as to how gambling-related harm can be reduced. Access article online

Reference: Howe, P.D.L., Vargas-Sáenz, A., Hulbert, C.A., Boldero, J.M. (2019). Predictors of gambling and problem gambling in Victoria, Australia, PLoS ONE 14(1), e0209277. https://doi.org/10.1371/journal.pone.0209277

Advertisements

Excessive Gambling and Online Gambling Communities [subscription article]

Sirola, A., Kaakinen, M. & Oksanen, A. (2018). Excessive gambling and online gambling communities. Journal of Gambling Studies. doi.org/10.1007/s10899-018-9772-0

Abstract: The Internet provides an accessible context for online gambling and gambling-related online communities, such as discussion forums for gamblers. These communities may be particularly attractive to young gamblers who are active Internet users. The aim of this study was to examine the use of gambling-related online communities and their relevance to excessive gambling among 15–25-year-old Finnish Internet users (N = 1200). Excessive gambling was assessed by using the South Oaks Gambling Screen. Respondents were asked in a survey about their use of various kinds of gambling-related online communities, and sociodemographic and behavioral factors were adjusted. The results of the study revealed that over half (54.33%) of respondents who had visited gambling-related online communities were either at-risk gamblers or probable pathological gamblers. Discussion in these communities was mainly based on sharing gambling tips and experiences, and very few respondents said that they related to gambling problems and recovery. In three different regression models, visiting gambling-related online communities was a significant predictor for excessive gambling (with 95% confidence level) even after adjusting confounding factors. The association of visiting such sites was even stronger among probable pathological gamblers than among at-risk gamblers. Health professionals working with young people should be aware of the role of online communities in terms of development and persistence of excessive gambling. Monitoring the use of online gambling communities as well as utilizing recovery-oriented support both offline and online would be important in preventing further problems. Gambling platforms should also include warnings about excessive gambling and provide links to helpful sources. Access and article sources

Bet Anywhere, Anytime: An Analysis of Internet Sports Bettors’ Responses to Gambling Promotions During Sports Broadcasts by Problem Gambling Severity

Nerilee Hing, Alex Myles Thomas Russell, Matthew Lamont, Peter Vitartas.

Promotions for online sports betting during televised sports broadcasts are regularly viewed by millions of Australians, raising concerns about their impacts on vulnerable groups including at-risk and problem gamblers. This study examined whether responses to these promotions varied with problem gambling severity amongst 455 Australian Internet sports bettors participating in an online survey. Results indicated that young male Internet sports bettors are especially vulnerable to gambling problems, particularly if they hold positive attitudes to gambling sponsors who embed promotions into sports broadcasts and to the promotional techniques they use and this heightens the risk that alluring messages contribute to excessive gambling. As problem gambling severity increased, so too did recognition that these promotions have impacted negatively on their sports betting behaviour. Because a plethora of sports betting brands and promotions are now heavily integrated into sports coverage, social marketing efforts are needed to offset their persuasive appeal and counter the positive attitudes towards them that appear linked to excessive gambling amongst Internet sports bettors.

The cost of virtual wins: An examination of gambling-related risks in youth who spend money on social casino games (full text)

Methods An online survey was administered to 555 adolescents, including 130 SCG players (78 non-paying and 52 paying users).

Early exposure to digital simulated gambling: A review and conceptual model

Young people are increasingly exposed to interactive simulated gambling activities and promotions via digital and social media. However, the individual harms and social burdens associated with early exposure to simulated gambling activities currently are not well understood. This review presents a two-pathway model that conceptualizes the potential risks and benefits of early exposure to a variety of digital simulated gambling activities (e.g., ‘free-to-play’ online casinos, gambling-like video games, and social casino games). The catalyst pathway describes risk factors associated with early exposure to simulated gambling that may increase the risk of problem gambling…

Source: King, D. L., & Delfabbro, P. H. (2016). Early exposure to digital simulated gambling: A review and conceptual model. Computers in Human Behavior, 55, 198–206.