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In Begun, A & Murray, A (Eds.), The Routledge Handbook of Social Work and Addictive Behaviors (forthcoming).
In the United States, the expansion of legalized gambling beyond Las Vegas and Atlantic City
began in the 1990s with pooled lotteries and oating casinos but escalated signicantly with
the growing popularity of the Internet and smart phones. Most individuals who gamble do so
for recreation only. However, a meaningful proportion of individuals who gamble experience
a wide range of adverse consequences, including stress-induced health problems (Morasco &
Petry, 2006); criminality and bankruptcy (see Nower & Blaszczynski, 2013, for a review);
and elevated rates of family violence (A, Brownridge, MacMillian, & Sareen, 2010; Suomi
et al., 2013), sometimes resulting in suicide or familicide (Anderson, Sisask,& Varnik, 2011).
Children of individuals experiencing gambling problems are likely to d evelop addictions,
mental health problems such as anxiety and depression (See K ourgiantakis, Saint-Jacques, &
Tremblay, 2013, for a review), and to act out at school. All of these consequences occur in
systems where social workers practice.
Unfortunately, social work is largely absent from advocacy and prevention eorts around
gambling. Practitioners in social work and other elds view gambling as harmless recreation
(Sansanwal, Derevensky, & Gavriel-Fried, 2016), and schools of social work continue to
train students in identifying and treating only substance use disorders, despite changes in
diagnostic classications to acknowledge gambling disorder as an addiction. This chapter
provides an overview of problem gambling and gambling disorder for social workers, in-
cluding: (a)terminology and history of the disorder; (b) prevalence of problem gambling and
gambling disorder; (c) etiological inuences; (d) comorbidity; (e) diagnosis and treatment;
and (f ) implications and emerging issues.
Terminology and history of the disorder
Gambling disorder is a “spectrum” disorder, with individuals shifting from lower (re-
creational) to higher (disorder) levels of pathology across their lifetimes, and, sometimes, back
again (Custer & Milt, 1985). The progression across the spectrum typically occurs in three
phases: the winning phase, the losing phase, and the desperation phase (Custer&Milt,1985).
Gambling disorder
The rst behavioral addiction
Lia Nower, Devin Mills and Wen Li (Vivien) Anthony
Pre-Publication copy
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Typically, an early win, series of wins, or positive emotional state resulting from gambling
fuel continued play in the “winning phase,” which invariably leads to mounting losses in the
“losing phase,” and negative consequences, including criminality, in the “desperation phase.”
There exists a lack of clarity in gambling research around terms used to refer to non-
recreational gambling. In social work, we conscientiously attempt to avoid labels, opting
instead for person-rst language. However, gambling studies has largely been a domain
inhabited by scholars in psychiatry, psychology, and public health with a disease-focus,
and, therefore, individuals who gamble are referred to as “gamblers” and those who en-
dorse problem symptomatology as “problem gamblers.” The absence of clinical criteria for
individuals who report symptoms of problem gambling but fail to meet clinical criteria
provides an added confound. As a result, “problem gambler” is typically used in the lit-
erature as a catch-all phrase, applied to sub-threshold problem gamblers and, sometimes,
to those who meet criteria as well. Where possible, we will opt to substitute person-rst
language in this chapter, however, for brevity and accuracy, we will also use the existing
terminology in the eld.
Gambling disorder is a secret addiction. Most problem gamblers can hide the scope of
their nancial devastation from friends and families until it is too late to remediate the
damage. Thus, problem gambling has remained largely under-recognized since it was rst
identied in the writings of Freud (1928). Gambling to excess has long been viewed as a
vice, with those who develop problems as “degenerates,” lacking in self-control; for ex-
ample, Moran (1970) argued that gambling only becomes a problem when the money runs
out. Those who experience gambling problems, however, dispute that characterization,
referring to their behavior as “compulsive” and beyond their volitional control (Custer &
Milt, 1985).
Tension in the eld is reected in the evolution of the diagnostic criteria for gambling
disorder. Sigmund Freud (1928) was the rst to identify excessive gambling as a treatable
illness. However, the medical community largely ignored the disorder until 1979 when the
World Health Organization (WHO) identied “pathological gambling” as a psychiatric ill-
ness in the International Classication of Diseases (ICD-9; WHO, 1979). The next year, the
American Psychiatric Association (APA) added pathological gambling to the third edition of
the Diagnostic and Statistical Manual of Mental Disorders (DSM-III; APA, 1980).
Initially, the APA classied pathological gambling as an impulse control disorder, along
with kleptomania, trichotillomania, explosive temper disorder, and pyromania. Counselors
working in the eld disputed this classication, arguing that gambling was an addictive be-
havior analogous to substance use disorders. While the classication remained unchanged,
criteria evolved in the DSM-III-R (APA, 1987) to include parallels with substance addictions
(i.e. preoccupation, tolerance, withdrawal, loss of control). All versions of the diagnostic
criteria have retained two hallmark characteristics of gambling disorder: (a) “chasing,” that
is,repeated attempts to relive a win or recoup a loss, and (b) “bailouts,” borrowing from
family, friends and others to address growing debt. Subsequent publications of the DSM,
editions IV (APA, 1994) and IV-R (APA, 2000), retained a hybrid-criteria of addiction and
behavioral criteria.
In subsequent years, psychiatric researchers began documenting the neurobiological sim-
ilarities between individuals experiencing gambling and those experiencing substance use
disorders, which share similar clinical expression, brain origin, comorbidity, and physio-
logy (Goudriaan, Oosterlaan, de Beurs, & Van den Brink, 2004, 2006; Grant, Potenza,
Weinstein, & Gorelick, 2010; Potenza, 2006, 2008). These ndings informed a conceptual
shift in DSM-5 (APA, 2013), which reclassied “Gambling Disorder” as an addictive disorder
Gambling disorder
in the “Substance-Related and Addictive Disorders” chapter and removed the commission
of criminal acts from the criteria. This change in classication failed to provide guidance
on how to classify sub-threshold problem gamblers, individuals who fail to meet full clinical
criteria for gambling disorder but manifest signicant problems with the behavior.
Prevalence across populations
In the United States, epidemiological studies have consistently reported that 80–90% of
adults gamble at some point in their lives and 60–80% report gambling within the past year,
with rates of gambling disorder of about 2% (past year to 3% lifetime) (Kessler et al., 2008;
National Opinion Research Center, 1999; Welte, Barnes, Wieczorek, Tidwell, & Parker,
2002). However, in states like New Jersey, which has a long history of gambling and a
wide range of gambling opportunities, rates are signicantly higher. For example, a recent
NewJersey prevalence study found that about 6% of residents who gamble would likely meet
criteria for gambling disorder with an additional 15% reporting symptoms of problem gam-
bling (Nower, Volberg, & Caler, 2017).
Attempts to reconcile disparities in prevalence rates are confounded by the lack of classi-
cation for sub-threshold problem gamblers as well as the wide variation in clinical versus
epidemiological assessment instruments. However, the breadth of studies in both the United
States and other countries suggest that U.S. rates are around the median for worldwide rates,
which range from 0.12% to 7.6%, depending on methodologies, screening instruments, and
time frames (Calado & Griths, 2016; Williams, Volberg, & Stevens, 2012). In addition, a
number of studies have consistently reported higher rates of gambling problems and disorder
among racial and ethnic minorities: Native Americans (Alega et al., 2009; Martins, Lee,
Kim, Letourneau, & Storr, 2014); Asians (Kong et al., 2013; Toyama et al., 2014); Hispanics/
Latinos (Barry, Stefanovics, Desai, & Potenza, 2011a; Caler, Garcia, & Nower, 2017), and
African American/Blacks (Alega et al., 2009; Barry, Stefanovics, Desai, & Potenza, 2011b;
Welte et al., 2002). In addition, homeless individuals (Matheson, Devotta, Wendaferew, &
Pedersen, 2014; Nower, Eyrich-Garg, Pollio & North, 2015) and war veterans (See Levy &
Tracy, 2018, for a review) who gamble at problem levels have exhibited signicantly higher
rates of gambling disorder, as well as mental health, substance use, and personality disorders.
Decades of research demonstrate that gambling typically begins at home and early gam-
bling correlates with later onset of gambling (Kundu et al., 2013; Nower, Derevensky, &
Gupta, 2004), as well as other problems: sexual behavior before age 18 (Martins et al., 2014),
substance misuse (Nower, Derevensky et al., 2004), decreased academic performance ( Vitaro,
Brendgen, Girard, Dionne, & Boivin, 2018), and delinquent behaviors (Vitaro, Brendgen,
Ladouceur, & Tremblay, 2001). In the United States and Canada, between 70% and 85% of
underage youth report past-year gambling and 46% endorse serious symptoms of disor-
der (Blinn-Pike, Worthy, & Jonkman, 2010; Chalmers & Willoughby, 2006). Worldwide,
rates of both gambling participation and gambling problems vary widely, due primarily to
methodo logical inconsistencies including the lack of sub-clinical cut-scores. One recent re-
view estimated that 0.2–12.3% of youth meet diagnostic criteria for problem gambling across
ve continents (Calado, Alexandre, & Griths, 2017). Recent legalization of online sports
wagering, as well as the pervasive availability of gambling through smart phones, could in-
crease the risk of youth gambling, as youth can wager with parents and friends or utilize the
online accounts of those who are of legal gambling age if an adult grants them access. This is
particularly troubling because rates of problem gambling typically peak in young adulthood
with youth of college age. A meta-analysis of college-age gambling studies reported that
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more than 6% of college students met clinical criteria for gambling disorder, and an additional
10% reported serious gambling problems—rates nearly three times that of adults (Nowak,
2018). These and other studies portend serious implications from the increasing availability of
online gambling opportunities, as tech-savvy youth are able to evade geo-fencing and other
platform safeguards designed to protect them.
Etiological inuences
Researchers have proposed a number of etiological models to explain the development of
ga mbling pr oblems, amon g them: soci al reward (Oce an & Smit h, 1993), behav ioral ( Weatherly&
Dixon, 2007), cognitive behavioral (Sharpe, 2002), and neurobiological and genetic factors
(Ibáñez, Blanco, & Sáiz-Ruiz, 2002; Ibáñez, Blanco, de Castro, F ernandez-Piqueras, &
Sáiz-Ruiz, 2003; Potenza, 2013). The most widely cited etiological model is the pathways
model (Blaszczynski & Nower, 2002), a common conceptual framework for research stud-
ies (Allami et al., 2017; Balodis, Thomas, & Moore, 2014; Nower, Martins, Lin, & Blanco,
2013; Valleur et al., 2016). The pathways model asserts that the accessibility, availability, and
acceptability of gambling opportunities contribute to the initiation of gambling. In addition,
positive experiences in the gambling environment as well as erroneous cognitions about the
nature of randomness, odds, and probabilities fuel persistence in play.
Apart from these common factors, the pathways model asserts that the development of
gambling problems is distinguished by distinct sub-groups of risk factors that lead to problem
gambling: problem gamblers in Pathway 1 typically begin gambling for social or other reasons
and develop problems due primarily to the conditioning eects of continued gambling over
time. In contrast, Pathway 2 problem gamblers report pre-morbid mental health comorbidity
and gamble primarily to cope with stress or to escape aversive mood states. Gamblers in Pathway
3 typically present with high levels of impulsivity, risk-taking, and antisocial traits; therefore,
gambling is but one of many pleasure-seeking activities. These factors were comprehensively
explored by the authors of the Gambling Pathways Questionnaire (GPQ; Nower & Blaszczynski,
2017), cited in a later section, which provided empirical support for the model but suggest that
motivational factors (e.g. coping with stress, searching for meaning) and child maltreatment and
trauma are key factors in the development and maintenance of gambling problems.
As suggested by the pathways model, a proportion of individuals experiencing gambling
problems have genetic and/or biological vulnerabilities that predispose them to sensation seek-
ing and risk-taking, fueled by the release of dopamine which mediates pleasure responses of
the brain (Clark & Dagher, 2014; Comings et al., 2001). Studies cite evidence for the famil-
ial transmission of problem gambling, alcohol use disorder (Slutske, Ellingson, Richmond-
Rakerd, Zhu, & Martin, 2013), and negative emotionality in family members (Slutske, Cho,
Piasecki, & Martin, 2013; Slutske et al, 2014), suggesting that environmental factors and inher-
ited genetic traits such as impulsivity (Clark et al., 2012) likely play a role in the development
of gambling disorder in a proportion of gamblers (Slutske, Mott, Poulton, & Caspi, 2012).
Specically, several studies reported that parental gambling is a key predictor of problem
gambling behavior among youth (Kundu et al., 2013; Nower, Derevensky et al., 2004).
Parents who gamble at problem levels are signicantly more likely to have children who
demonstrate impulsive, hyperactive, and/or inattentive behaviors (Carbonneau, V itaro,
Brendgen, & Tremblay, 2018). Youth who gamble with their parents or believe their
parents have a problem, even if untrue, have the highest rates of gambling problems (King,
Abrams,& Wilkinson, 2010; Leeman et al., 2014; Vitaro & Wanner, 2011). These ndings
are particularly troublesome in light of the fact that gambling is generally viewed by parents
Gambling disorder
and educators as harmless activity (Campbell, Derevensky, Meerkamper, & Cutajar, 2011;
Derevensky, St-Pierre, Temche, & Gupta, 2014). For example, one study of parents with
children ages 13–18 found that most parents had little knowledge of gambling-related harms
and viewed gambling as a relatively unimportant issue compared to other potentially risky
behaviors (Campbell et al., 2011). Similarly, teachers of middle and high school students in
another study viewed gambling as the least serious of a list of activities aecting youth, and
half of the participants indicated that gambling in school can constitute a good learning ac-
tivity (Derevensky et al., 2014).
Of particular interest to social workers, childhood maltreatment and trauma appear to
serve as an underlying motivation for gambling in some groups, fueling a desire for escape
and mental disengagement. Studies report that problem gamblers, compared to recreational
gamblers, are more likely to report childhood sexual, physical, and emotional abuse (Black,
Shaw, McCormick, & Allen, 2012; Hodgins et al., 2010; Petry & Steinberg, 2005). Those
gamblers, in turn, report higher rates of comorbid disorders (Leppink & Grant, 2015) and use
gambling as a stress-coping strategy which, in turn, compounds stress due to mounting debts.
Finally, a common element across all individuals experiencing gambling problems is the
presence of cognitive distortions during play. Gamblers typically misperceive their chances
of winning based primarily on three erroneous beliefs: the “illusion of control,” the belief
that they can control a random outcome (Langer, 1975); “gambler’s fallacy,” the belief that, as
losses increase, wins also will increase (Tversky & Kahneman, 1971); and “biased evaluation,”
the tendency to accept wins at face value but explain away losses (Gilovich, 1983). Those be-
liefs support the notion that a number is “due” or “hot,” that machines on casino aisles pay
more than others, or that small credit increases on a machine are wins, even though, overall,
the player is losing money. Combined with other etiological risk factors, these cognitions
encourage continued play and lead to the development of disorder.
It is well established in the gambling literature that a majority of problem gamblers report
comorbid mental health and substance use disorders, particularly alcohol and/or drug use dis-
orders, nicotine dependence, anxiety and/or depression, post-traumatic stress disorder, and/
or personality disorders, even after controlling for gender, race, and other socio demographic
factors (Håkansson, Karlsson, & Widingho, 2018; Nower et al., 2013; Schluter etal.,
2019; for a review, see Dowling et al., 2015). Of particular concern, a number of studies
have reported that problem gamblers experience higher rates of suicidality than any other
addiction—as high as 81% in one study—and are three times more likely to report sui-
cidality than the general population (Newman & Thompson, 2007; Wong, Kwok, Tang,
Blaszczynski,& Tse, 2014). These ndings are consistent across studies with youth (Nower,
Gupta, et al., 2004), college students (Stuhldreher, Stuhldreher, & Forrest, 2007), and adults
(Nower & Blaszczynski, 2008). By the time families learn of a member’s gambling losses, it
is often too late to save their cars, homes, or jobs. Faced with the prospect of facing the loved
ones they have betrayed problem gamblers often contemplate or resort to self-harm.
Diagnosis and treatment
Given that Gambling Disorder is a relatively novel addiction to most social workers, many
educators and students are unfamiliar with current screening tools used in gambling research
and practice. As outlined above, the diagnosis has undergone signicant changes since its
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introduction in 1980, and commonly used tools such as the South Oaks Gambling Screen
(SOGS; Lesieur & Blume, 1987) are long outdated. Increasing identication of gamblers
in social work settings will require a two-step approach: introducing a very brief screen
to “ag” those with potential gambling problems, and following positive screening with a
problem severity measure for classication and an etiology measure to assist in individual-
izing treatment. The following instruments are currently used by gambling researchers and
treatment providers internationally.
Screening instruments
For decades, there was only one measure of problem gambling and gambling disorder: the
SOGS (Lesieur& Blume, 1987), a 20-item screen based on the DSM-III. The SOGS had
satis factory reliability but yielded high rates of false positives in some populations (Stinch-
eld, 2013). As a result, other measures were developed with greater reliability, particularly
in dierentiating among levels of problem gambling (for a detailed review of all measures, see
Caler, Garcia, & Nower, 2016). Currently utilized measures include:
The Problem Gambling Severity Index (Ferris & Wynne, 2001): a nine-item screen for
problem severity, demonstrating high internal consistency and validity, used interna-
tionally for both epidemiological and clinical populations.
The NODS—CliP (National Opinion Research Center DSM-IV Screen for Gambling
Problems Control, Lying, and Preoccupation brief screen) (Toce-Gerstein, Gerstein, &
Volberg, 2009) and NODS PERC (NODS Preoccupation, Escape, Risked Relation-
ships, and Chasing brief screen) (Volberg, Munck, & Petry, 2011): validated three- and
four-item screening instruments, based on a longer instrument used in the 1998 U.S.
general population survey and based on the DSM. The CLiP is intended for use in men-
tal health settings and PERC is more discriminating for those experiencing comorbid
substance use disorders (See Himelhoch et al., 2015).
The Gambling Pathways Questionnaire (GPQ; Nower & Blaszczynski, 2017): a 48-item
valid and reliable etiological screening instrument, used to group gamblers into sub-
groups by risk factors. It is intended for use with a problem severity measure.
Treatment outcomes
Published systematic reviews and meta-analyses have evaluated gambling treatment outcomes
(Petry, Ginley, & Rash, 2017; Pickering, Keen, Entwistle, & Blaszczynski, 2018; Yakovenko &
Hodgins, 2016). Cognitive behavioral therapy (CBT) remains the most empirically supported
approach (see reviews by Blaszczynski & Nower, 2013; Fortune & Goodie, 2012). Seminal
studies on the eectiveness of CBT and gambling are by Ladouceur, Sylvain, Letarte, Giroux,
and Jacques (1998), who established that individuals restructure gambling-related cognitions,
primarily through education about concepts of randomness and the independence of events,
the odds of winning, and the futility of common cognitive fallacies that initiate or maintain
problem gambling behavior. They concluded that interventions that also incorporate problem
solving and relapse-prevention along with CBT are the most successful at decreasing gambling
severity (Ladouceur et al., 1998; Sylvain, Ladouceur & Boisvert, 1997).
Other treatment approaches have demonstrated mixed results, including motivational
enhancement (Hodgins, Currie, & el-Guebaly, 2001; Ledgerwood et al., 2013) and brief
Gambling disorder
motivational treatments (Hodgins, Currie, el-Guebaly, & Peden, 2004); motivational
interviewing, which is more eective when combined with CBT (Petry et al., 2017);
personalized feedback (Marchica & Derevensky, 2016; Yakovenko & Hodgins, 2016);
mindfulness-enhanced cognitive therapy (Toneatto, Pillai, & Courtice, 2014); meditation
awareness training (Shonin, Van Gordon, & Griths, 2014); and imaginal desensitization
(Blaszczynski & Nower, 2013). Studies fail to provide strong support for the use of specic
medications for the treatment of gambling disorder (Bartley & Bloch, 2013; Yakovenko &
Hodgins, 2016).
Implications and emerging issues for social work
A signicant emerging issue for social work is the advent of interactive technologies that
allow 24/7 access to gambling opportunities, irrespective of age. This is particularly criti-
cal in light of the recent legalization of sports wagering in the United States, which appeals
to youth, and a growing overlap between gambling and video gaming (see Chapter9).
Historically, the gambling industry uses the term “gaming” for “gambling,” which tradi-
tionally involves wagering on chance outcomes for money. In contrast, researchers typi-
cally reserve the term “gaming” for video game play without money bets. However, video
game developers increasingly blur the lines between gaming and gambling by embedding
gambling elements in video games to generate more income. Such monetization tactics
Social casino games: gambling-themed video games, which are free to play and hosted
by social media sites (e.g. Facebook), casual games with gambling elements, and practice
modules of online casino games, which “prime” players to transition to a gambling en-
vironment (Gainsbury, Hing, Delfabbro & King, 2014).
Loot boxes (also called “loot crates” or “loot chests”): virtual items that can be purchased
with real money for a chance to win valuable in-game items to enhance play. As with
gambling, loot box contents are determined by chance. Preliminary data suggest loot
box purchases are related to higher frequency of video gaming and online gambling
engagement, which, in turn, increase the risk of developing problem video gaming and
gambling (Li, Mills, & Nower, 2019).
“Skin” betting: virtual, decorative items in video games, which can be purchased and
traded with money through an online market place or a third-party service, as a stake
in a bet on the outcome of gambling activities or professional video game matches (i.e.
eSports) based on chance. Skin betting has been associated with higher problem gam-
bling severity (Macey & Hamari, 2019).
These new technologies introduce children and youth to gambling elements at a very young
age and prime them for the crossover to gambling with money. The conditioning eects of
such reinforcement schedules can fuel persistence and the initiation of problem Internet/
video gaming behavior (see Chapter 9). King, Delfabbro, Kaptsis, and Zwaans (2014) ex-
amined the relationship of adolescent gambling to free gambling sites and social media and
reported that adolescents aged 12–17 with a history of gambling in simulated activities were
at highest risk of developing gambling disorder. Social workers should be well educated on
emerging technologies and their potential impact on the early onset of addictive behaviors
among children, adolescents, and young adults.
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Future directions for advocacy and practice
Gambling is a popular and poorly understood behavior with the potential to cause irrepa-
rable harm to individuals and their families, particularly those who lack the nancial abil-
ity to recover from signicant losses. Social workers are a critical but largely absent voice
in advocacy and policymaking eorts around this issue, even though gambling disorder
adversely aects populations of central interest to social work. Few social work programs
oer gambling-specic training, incorporate gambling into courses focused on addiction,
or prepare practitioners to deliver evidence-based screening or treatment. Social workers in
child welfare, health/mental health, and school settings generally lack the skills to identify
and address problem gambling, and, as a result, are largely unable to help children of problem
gamblers who may develop addictions themselves. In the United States, for example, orga-
nizations focused on social work practice and research have yet to make gambling disorder
a priority or to broaden their focus from substance misuse to include all addictive behaviors.
In contrast, the Australia Association of Social Work has published a position paper and reg-
ularly conducts advocacy and training on problem gambling; notably, gambling tops the list
of social problem targets on their website ( Given that social workers are
at the forefront of many care systems, it is critical to broaden our focus and train new social
workers to anticipate and address problems that accompany rapidly evolving technology.
Social work is well positioned to partner with those most aected by the unintended
adverse consequences of gambling in settings where they will likely encounter them: child
welfare agencies, mental health settings, emergency rooms, schools, family violence shelters,
human service organizations, community agencies, homeless shelters, and the criminal jus-
tice system. As interactive forms of gambling and gaming continue to expand, and players
become engaged at younger ages, it is critical for social workers to become stakeholders in
this growing area of public health concern.
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To review the scientific literature examining gambling behavior in military veterans in order to summarize factors associated with gambling behavior in this population. Database searches were employed to identify articles specifically examining gambling behavior in military veterans. Cumulative search results identified 52 articles (1983–2017) examining gambling behavior in veteran populations. Articles generally fell into one or more of the following categories: prevalence, psychological profiles and psychiatric comorbidities, treatment evaluations, measurement, and genetic contributions to gambling disorder. Results from reviewed articles are presented and implications for future research discussed. Research to date has provided an excellent foundation to inform potential screening, intervention and research activities going forward. The authors suggest that a public health approach to future research endeavors would strengthen the evidence base regarding gambling in veteran populations and better inform strategies for screening, prevention and treatment.
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Gambling participation and low academic performance are related during adolescence, but the causal mechanisms underlying this link are unclear. It is possible that gambling participation impairs academic performance. Alternatively, the link between gambling participation and low academic performance could be explained by common underlying risk factors such as impulsivity and socio-family adversity. It could also be explained by other current correlated problem behaviors such as substance use. The goal of the present study was to examine whether concurrent and longitudinal links between gambling participation and low academic performance exist from age 14 to age 17 years, net of common antecedent factors and current substance use. A convenience sample of 766 adolescents (50.6% males) from a longitudinal twin sample participated in the study. Analyses revealed significant, albeit modest, concurrent links at both ages between gambling participation and academic performance. There was also a longitudinal link between gambling participation at age 14 and academic performance at age 17, which persisted after controlling for age 12 impulsivity and socio-family adversity as well as current substance use. Gambling participation predicts a decrease in academic performance during adolescence, net of concurrent and antecedent personal and familial risk factors.
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The problem of gambling addiction is especially noteworthy among college students, many of whom have the resources, proximity, free time, and desire to become involved in the myriad options of gambling now available. Although limited attention has been paid specifically to college student gambling in the body of literature, there have been three published meta-analyses estimating the prevalence of probable pathological gambling among college students. The research presented is the largest and most comprehensive, presenting an up-to-date proportion of those students worldwide exhibiting gambling pathology as assessed by the South Oaks Gambling Screen, and is the first to include estimates of sub-clinical problem gambling. A thorough literature review and coding procedure resulted in 124 independent data estimates retrieved from 72 studies conducted between 1987 and the present, surveying 41,989 university students worldwide. The estimated proportion of probable pathological gamblers among students was computed at 6.13%, while the rate of problem gambling was computed at 10.23%. Statistical significance was found in the influence of the percentage of non-white students on pathological gambling rates. The implications of this and other moderator analyses such as age and year of studies, as well as recommendations for future practice in dealing with college students and gambling disorder on campus are outlined and described in detail. Suggestions and rationales for future avenues of research in the area are also described.
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A few studies have examined gambling behavior and problem gambling among minorities and reported higher rates of both participation and gambling problems among particular minority groups in comparison to Whites who gamble. The present study utilized a representative, epidemiological sample of adults in New Jersey to explore gambling behavior, gambling problem severity, substance use, problem behavior, and mental health issues among minorities. Univariate analyses were conducted, comparing Whites (n = 1341) to respondents who identified as Hispanic (n = 394), Black (n = 261), or Asian/other (n = 177). Overall, the highest proportion of Hispanics were high-risk problem gamblers. Hispanic participants were also significantly more likely than other groups to use and abuse substances and to report mental health problems in the past month, behavioral addictions, and/or suicidal ideation in the past year. Primary predictors of White high risk problem gamblers were being young and male with friends or family who gambled, fair to poor health status, substance use, gambling once a week or more both online and in land-based venues, and engaging in a number of gambling activities. In contrast, gender was not a predictor of minority high risk problem gamblers, who were characterized primarily by having friends or family who gambled, gambling online only, having a behavioral addiction and playing instant scratch-offs and gaming machines. Implications for research and practice are discussed.
Loot boxes are virtual items in many video games that let players to “gamble” on an item of chance. Loot boxes bring an element of gambling into video games, which might prime video game users to engage in online gambling activities. However, few studies have focused on this emerging issue. The present study investigated the relationships between loot box purchases and problem video gaming and problem gambling severity. Cross-sectional, self-report data were collected from 618 adult video gamers (M = 27 years of age, SD = 8.9, 63.7% male) via an online survey. Nearly half of the sample (44.2%) spent money on loot box purchases in the past year. Loot box purchasers played video games and gambled online more frequently, reported more extended gaming and online gambling sessions, and endorsed higher levels of problem video gaming and problem gambling severity as well as greater mental distress relative to those who did not buy loot boxes. Results from a series of path analyses revealed that loot box purchasing was directly related to problem video gaming and problem gambling severity as well as indirectly through an increased video gaming/online gambling engagement, which in turn is related to elevated psychological distress. The present findings provide insight into the role of loot box purchasing in the transition from recreational engagement in video gaming and online gambling to problem video gaming and/or problem gambling.
Background and objectives: Symptoms of depression are highly prevalent among individuals with gambling disorder, and severity of depression is associated with severity of gambling problem. Yet, little is known about the psychological mechanisms by which symptoms of depression lead to greater gambling severity. In this study, we tested whether cognitive distortions represent one such mechanism, as cognitive distortions are key characteristics in both depression and gambling disorder and have been shown to be associated with gambling severity. Methods: A mediation model was tested among 345 treatment-seeking individuals with gambling disorder in Sao Paulo, Brazil. The diagnosis of gambling disorder was made using semi-structured clinical interviews and participants completed psychometrically sound self-report measures of depression symptoms (Beck Depression Inventory-I), gambling-related cognitive distortions (Gamblers’ Beliefs Questionnaire), and gambling severity (Gambling Symptom Assessment Scale). Results: As hypothesized, increased symptoms of depression were significantly associated with both increased disordered gambling severity and increased gambling-related cognitive distortions. Further, gambling-related cognitive distortions predicted greater disordered gambling severity when controlling for depression symptomology. Results from the bootstrapping method indicated that the relationship between symptoms of depression and increased disordered gambling severity is mediated by gambling-related cognitive distortions. Conclusions: Consistent with our predictions, gambling-related cognitive distortions mediated the relationship between depression symptoms and gambling severity among a sample of treatment-seeking disordered gamblers. These results suggest that cognitive distortions may be a key intervention target for the treatment of concurrent depression and gambling disorder.
Despite the well-established association between problem gambling and ADHD core categories of impulsivity-hyperactivity and inattention, the link between parents’ problem gambling and impulsivity-hyperactivity/inattention (IH/I) behaviors in children has not been investigated. This study investigated the association between parents’ problem gambling and children’s IH/I behaviors while controlling for potential confounding variables. A population-based prospective cohort followed-up from kindergarten to age 30, the Quebec Longitudinal Study of Kindergarten Children (QLSKC), provided data over three generations. Among 1358 participants at age 30, parents with a child aged 1 year or older (N = 468; Mean age = 4.65 years; SD = 2.70) were selected. Generalized Linear Models included measures of grandparents’ and parents’ problem gambling, parents’ IH/I behaviors in childhood, and a host of risk factors and comorbidities to predict IH/I in children. Intergenerational bivariate associations were observed between grandparents’ problem gambling, parents’ IH/I in childhood and problem gambling at age 30, and between parents’ IH/I, problem gambling, and children’s IH/I behaviors. Parents’ problem gambling predicted children’s IH/I behaviors above and beyond the effects of covariates such as family and socioeconomic characteristics, alcohol and drug use, depression symptoms and parents’ gambling involvement. Parents’ IH/I behaviors in childhood also predicted children’s IH/I and had a moderating, enhancing effect on parents’ problem gambling association with their offspring’s IH/I behaviors. Problem gambling is a characteristic of parents’ mental health that is distinctively associated with children’s IH/I behaviors, above and beyond parents’ own history of IH/I and of typically related addictive, psychopathological or socioeconomic risk factors and comorbidities.