Gambling problem severity index
The Problem Gambling Severity Index (PGSI) is a widely used nine item scale for measuring the severity of gambling problems in the general population. Of the four. Australian Capital Territory (02) New South Wales (Regional) (02) (Metropolitan) Northern Territory. A large, integrated survey data set provided by the Ontario Problem Gambling Centre was used to investigate psychometric properties of the Problem Gambling Severity.
Validity of the Problem Gambling Severity Index Interpretive Categories
Psychology of Addictive Behaviors, 21 2 , — For most analyses this resulted in an overall Nof 12, The Canadian Community Health Survey: Google Scholar Strong, D. The present data set afforded such an opportunity. Each survey used a random sampling procedure with various constraints e. Particularly important are brief, self-report instruments that can be used for research purposes e.
Problem Gambling Severity Index (PGSI)
This nine-item self-report instrument was designed to measure a single, problem gambling construct. The present analyses demonstrated that the PGSI does assess a single, underlying, factor, but that this is complicated by different, multiple factor structures for respondents with differing levels of problem gambling severity. The PGSI also demonstrated small to moderate correlations with measures of gambling frequency and faulty cognitions.
Particularly important are brief, self-report instruments that can be used for research purposes e. The SOGS and related measures were developed primarily for use as problem gambling screens in clinical contexts. This information served as the basis for the development of a set of questions that were then administered to a second group of patients. During this phase of the research, low frequency and redundant items were eliminated and a discriminant analysis reduced the number of items to These 20 items constituted the original SOGS.
In this version, a lifetime time-frame was used for each question e. The SOGS has been demonstrated to provide a reliable means of identifying people who are likely problem gamblers Duvarci and Varan ; Lesieur and Blume ; Shaffer et al. Moreover, in clinical contexts its psychometric properties are adequate to good Shaffer et al. The reliance on problem gamblers for the generation of the SOGS items allowed for the development of a scale that captures important features of problem gambling, and in this way the SOGS is useful for identifying people who are currently problem gamblers.
However, the SOGS does not include less severe behavioral items and hence may not do well at identifying people who are in the process of becoming problem gamblers Strong et al. Similarly, the SOGS does not appear to perform well at identifying prevalence rates in a general populations; it tends to produce a relatively high number of false positives Culleton ; but see Abbott and Volberg In this regard, the measure was more theoretically derived than the exclusively empirical approach guiding the development of the SOGS Ferris and Wynne ; Ferris et al.
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Kahn, each dollar spent on high-risk populations prevents 50 to 70 times as many new infections as the same money spread out among low-risk groups. I came, and as I did, she screamed out. 427.
This is due to the symptomatology of the disorder resembling an addiction not dissimilar to that of substance-abuse. Subjects' reactions were measured using fMRI , a neuroimaging technique.
And according to Hans Breiter, co-director of the Motivation and Emotion Neuroscience Center at Massachusetts General Hospital , "monetary reward in a gambling-like experiment produces brain activation very similar to that observed in a cocaine addict receiving an infusion of cocaine.
Pathological gambling, as the part of obsessive-compulsive disorder, requires the higher doses of antidepressants as it usually required for depressive disorders.
In cases where participants do not have or have minimal symptoms of anxiety or depression, antidepressants still have those effect. A limited study was presented at a conference in Berlin, suggesting opioid release differs in problem gamblers form the general population, but in a very different way from alcoholics or other substance abusers. These authors point out that social factors may be a far more important determinant of gambling behaviour than brain chemicals and they suggest that a social model may be more useful in understanding the issue.
There is a partial overlap in diagnostic criteria; pathological gamblers are also likely to abuse alcohol and other drugs. The "telescoping phenomenon" reflects the rapid development from initial to problematic behavior in women compared with men. This phenomenon was initially described for alcoholism, but it has also been applied to pathological gambling.
This equation applies in the absence of any transaction costs or minimum bets. When these constraints apply as they invariably do in real life , another important gambling concept comes into play: Note that even food, clothing, and shelter can be considered fixed transaction costs and thus contribute to the gambler's probability of ultimate ruin.
This equation was the first application of Shannon's theory of information outside its prevailing paradigm of data communications Pierce. Applications for self-information[ edit ] Surprisal and evidence in bits, as logarithmic measures of probability and odds respectively.
Its two primary strengths are that surprisals: For example, one might say that "the number of states equals two to the number of bits" i. Here the quantity that's measured in bits is the logarithmic information measure mentioned above. Hence there are N bits of surprisal in landing all heads on one's first toss of N coins. The additive nature of surprisals, and one's ability to get a feel for their meaning with a handful of coins, can help one put improbable events like winning the lottery, or having an accident into context.
For example if one out of 17 million tickets is a winner, then the surprisal of winning from a single random selection is about 24 bits. Tossing 24 coins a few times might give you a feel for the surprisal of getting all heads on the first try.
The additive nature of this measure also comes in handy when weighing alternatives. For example, imagine that the surprisal of harm from a vaccination is 20 bits. Whether or not you decide to get the vaccination e. As suggested above, this is mainly useful with small probabilities.