Measuring treatment outcomes in gambling disorders: a systematic review

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Gambling addiction measure

Postby Gardaran В» 18.07.2019

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Traditionally, gambling and problem gambling research relies on cross-sectional and retrospective designs. This has compromised identification of temporal relationships and causal inference.

The JAS has 11 items and seeks to identify early indicators, examine relationships between indicators and assess their capacity to predict future problem progression. The aims of the study were to examine psychometric properties of the JAS internal consistency and dimensionality and predictive validity with respect to increased gambling risk and problem gambling onset.

The results are based on repeated interviews with participants. The original sample consisted of a random, stratified selection from the Swedish population register aged between 16 and Reinforcers, Over consumption and Gambling fallacies were significant predictors of gambling risk potential and Gambling fallacies and Over consumption were significant predictors of problem gambling onset incident cases at 12 month follow up.

When controlled for risk potential measured at baseline, the predictor Over consumption was not significant for gambling risk potential at follow up. For incident cases, Gambling fallacies and Over consumption remained significant when controlled for risk potential.

Implications of the results for the development of problem gambling, early detection, prevention, and future research are discussed. Gambling availability has increased markedly in recent decades Arvidsson et al.

This increase has been associated with growth in gambling participation and expenditure. In most jurisdictions where general population surveys have been conducted a majority of adults report taking part in one or more gambling activities on an annual or more frequent basis Williams et al. A minority has higher levels of engagement and is at greater risk of developing gambling-related problems. The prevalence of gambling disorder or serious problem gambling usually ranges from 0.

Substantially, more people experience some loss of control over gambling and subclinical gambling-related harm Williams et al. There is no unitary theoretical model for the development of gambling disorder or less serious gambling problems.

Clinical and epidemiological studies have found strong associations between involvement in some forms of gambling and problem gambling Stevens and Young, Some sociodemographic groups including males, young adults, low-income people and single status are almost universally found to be at high risk Abbott et al.

In Sweden, people born outside the country also have elevated risk Abbott et al. In addition to some gambling and sociodemographic factors, there are strong associations with personality characteristics including impulsivity, mental health disorders and substance use and misuse Bruneau et al.

The emergence of a body of longitudinal research in the gambling field makes it possible to assess whether or not cross-sectional correlates of problem gambling prevalence precede and predict the development and onset incidence of gambling problems. During the past decade five large-scale prospective studies have been conducted Swedish National Institute of Public Health, , ; Billi et al.

The Swedish and New Zealand studies are still in progress. The foregoing prospective studies have found that gambling-related factors are the strongest predictors of problem gambling development.

People who experienced past gambling problems were also prone to relapse. Mental health variables including mental health disorders, substance abuse or dependence and behavioral addictions also predicted future problem gambling. In New Zealand, in addition to gambling-related and mental health factors, ethnicity was a strong risk factor. Maori, Pacific Islanders and Asian people were at particularly high risk. High deprivation, experiencing major life events, lower quality of life and psychological stress were further risk factors and high family income and usually gambling with others were protective Abbott et al.

Ethnicity was also a risk factor in the Canadian studies, with non-Caucasians being at higher risk. The prevention of gambling problems and harm has received increased attention in recent years see Williams et al. Prevention measures include public awareness raising and education, policy initiatives, restrictions on who can gamble and restrictions and alterations to how gambling is provided.

Early intervention, engaging people before they develop a gambling problem, is an important part of a comprehensive prevention strategy. This calls for the identification of early indicators of problem gambling. As mentioned, heavy gambling engagement is a major risk factor for problem development. To date the role of heavy engagement, consumption and overconsumption in developing problems has not received much attention in its own right Williams and Volberg, It has received some consideration as an aspect of loss of control and Currie et al.

The measures include gambling frequency, gambling expenditure and gambling expenditure as a percentage of gross income. While promising, the predictive validity of low-risk limits is yet to be assessed using prospective data.

Gambling-related cognitive distortions and fallacies are relatively commonplace and appear to be risk factors for the development of problem gambling Leonard and Williams, Gambling fallacies predicted future problem gambling in the two Canadian prospective studies. Challenging false beliefs about the nature of randomness, over-estimation of skill components in gambling activities and superstitious views about ways to control gambling outcomes through public education campaigns and education programs in schools or at gambling sites may contribute to reducing the incidence of at-risk and problem gambling.

Motives for gambling may also be relevant to problem development and early intervention. As mentioned life events, psychological distress and mental health disorders are risk factors for the development of problem gambling.

It is likely that participation in some forms of gambling provide an escape from negative emotions and this could increase gambling exposure and the psychological salience, e. Gambling for escape or distraction was a risk factor for problem development in one of the two Canadian prospective studies. Performance on two Gambling Motives Questionnaire Stewart and Zack, subscales, enhancement and emotional coping, have been found to be associated with problem gambling MacLaren et al.

In a recent Swedish study, moderate risk gamblers participated for challenge and coping reasons more often than low risk gamblers Sundqvist et al. Existing problem gambling screens have covered aspects of gambling fallacies and the reinforcing aspects of gambling, although not used in longitudinal research.

The Victorian Gambling Screen includes three items on the enjoyment of gambling among its twenty items Tolchard and Battersby, The full Canadian Problem Gambling Inventory has two items on faulty cognitions and three items on gambling as self-medication Ferris and Wynne, One purpose of the Swedish Longitudinal Gambling Study Swelogs is to advance understanding of the early development of problem gambling.

The research team sought to identify early indicators, examine relationships between indicators and assess their capacity to predict future problem progression. To this end, two team members developed the Jonsson-Abbott Scale JAS , including items designed to assess the three domains of gambling reinforcements, gambling over-consumption and gambling fallacies Romild et al.

The theoretical definition of Reinforcers is that the gambling behavior is psychologically reinforcing in some way. The items were chosen to reflect positive reinforcement as excitement and joy, negative reinforcement as forgetting everything else for a while and a socially rewarding aspect.

Over consumption is defined as gambling more than intended and experiencing difficulties in refraining from gambling. The items were chosen to mirror that. Gambling fallacies is defined as the misconception that gambling is a way to make money in the long run and that winnings is related to skill.

The rationale for developing a new scale was the lack of an existing short screen covering these three areas. Furthermore, the problem gambling group showed significantly higher scores on all three JAS-domains compared with the controls Public Health Agency of Sweden, The aims of this study are to further examine the psychometric properties of the JAS and assess the predictive validity of this new measure.

More specifically, it seeks to assess the capacity of identified JAS dimensions to predict increases in problem gambling risk level and problem gambling over 1 year. Data were collected within the Swelogs epidemiological track — a prospective study with four waves of data-collection from Swedish citizens aged 16—84 years at baseline. A stratified random sampling procedure was applied for drawing individuals from the Swedish register of the total population.

Data from the two first waves are used in this study. The baseline wave 1, performed between October and August , included participants. In wave 2 participants were reassessed between December and August Interview and questionnaire data were supplemented by register data. Computer-supported telephone interviews were used as the primary method with postal questionnaires used to follow-up those not reached by telephone.

Swelogs design, sampling and methodological details are provided in Romild et al. The participants out of who gambled at least yearly in wave 1 were included in this study. The mean age was The sample reduced to when only participants who reported gambling in both wave 1 and wave 2 were included.

Participants were asked about their past 12 months gambling participation in wave 1 and wave 2. Questions covered gambling frequency, time and money spent and modality for nine groups of gambling types. The risk potential for each of the various gambling types was assessed using 7 out of 10 criteria suggested by Meyer et al. On this basis gambling types were classified as being of low, medium high or high risk Swedish National Institute of Public Health, Examples of low risk activities are lotteries except scratch cards online and number games at retailers.

Medium high risk activities include sports betting not online , horse betting and online number games. High-risk activities include online bingo, VLTs, casino games and online poker. In the Swelogs study, the medium-high and the high-risk groups both showed distinctly a higher connection with problem gambling than the less than monthly and low-risk-groups that both had very weak connection with gambling problems Public Health Agency of Sweden, a. All participants were assigned a risk level based on their highest monthly risk gambling participation in wave 1 and wave 2.

It employed the response format Never 0 , Seldom 1 , Often 2 , and Always 3. Participants with an overall PGSI score of were classified No problem and those with a score of were classified Gambling problem. The 11 JAS-items were asked in wave 1 only.

The directions for objective scale development outlined by Clark and Watson served as a guide when developing the scale. We investigated if the scales represented three different constructs by subjecting the items to a confirmatory factor analysis CFA; Bollen, The postulated three-factor representation of the item gambling scale was empirically tested using the CFA procedures with maximum likelihood estimation in Lavaan Rosseel, We relied on MacCallum et al.

To address the question concerning predictive power of JAS we used logistic regression analyses Menard, with gambling risk potential and incident cases as dependent variables and the three factors i. The likelihood ratio test was used to test our models. It is a test of the significance of the difference between the likelihood ratio -2 log likelihood for our model with predictors called model chi square minus the likelihood ratio for baseline model with only a constant in the model.

Significance at the 0. It measures the improvement in fit that the explanatory variables make compared to the null model. Chi square is used to assess significance of this ratio. Both are based on - 2 Log Likelihood.

The value of AIC and BIC can be used to compare various models for the same data set to determine the best-fitting model.

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Re: gambling addiction measure

Postby Kigazahn В» 18.07.2019

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Re: gambling addiction measure

Postby Daijas В» 18.07.2019

Maori, Pacific Islanders and Asian people were at particularly high risk. Google Scholar Hidayat, B. Blaszczynski, A.

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Re: gambling addiction measure

Postby Bakasa В» 18.07.2019

Romild, U. Psychiatr Rehabil J ; 35 : Published : 25 October Neale J. What are the most harmful forms of gambling?

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