J Gambl Stud (2014) 30:819–843

DOI 10.1007/s10899-013-9399-0

ORIGINAL PAPER

The Prevalence of Pathological Gambling Among College

Students: A Meta-analytic Synthesis, 2005–2013

Donald E. Nowak • Ariel M. Aloe

Published online: 11 July 2013

 Springer Science+Business Media New York 2013

Abstract The problem of gambling addiction can be especially noteworthy among col-
lege and university 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 two published meta-analyses estimating the prevalence of
probable pathological gambling among college students. This present study aims to be the
third, presenting an up-to-date proportion of those students exhibiting gambling pathology,
and is the first to include international studies from outside the United States and Canada.
The purpose of this study was to use the most up-to-date meta-analytical procedures to
synthesize the rates of probable pathological gambling for college and university students
worldwide. A thorough literature review and coding procedure resulted in 19 independent
data estimates retrieved from 18 studies conducted between 2005 and 2013. To synthesize
the studies, a random effects model for meta-analysis was applied. The estimated pro-
portion of probable pathological gamblers among the over 13,000 college students sur-
veyed was computed at 10.23 %, considerably higher than either of the two previously
published meta-analyses, and more than double the rate reported in the first meta-analysis
of this type published in 1999. Implications and recommendations for future practice in
dealing with college students and gambling addiction are outlined and described for both
administrators and mental health professionals.

Keywords Pathological gambling  College students  Prevalence  Meta-analysis

Introduction

The economic downturn and volatility of the past decade have seen an unprecedented
number of cash-strapped state and national governments turn to and rely more heavily

D. E. Nowak (&)  A. M. Aloe

Department of Counseling, School, and Educational Psychology, Graduate School of Education,
State University of New York at Buffalo, Buffalo, NY, USA
e-mail: denowak@buffalo.edu

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upon gambling revenue, expanding lotteries, approving more and more casinos, and
establishing slot machines at existing racetracks (Stuart 2011). Even countries that do not
officially permit gambling for its citizens because of cultural or religious reasons often
sanction gambling venues for foreign visitors (e.g., Malaysia and South Korea) (Hodgins
et al. 2011). This rapid growth is not only driving up profits, it is also resulting in a
dramatic increase in gambling addiction. The concomitant cost to society is staggering, as a
Baylor University researcher estimated that addicted gamblers cost the United States alone
between $32.4 billion and $53.8 billion a year (Stuart 2011).

Theories of Gambling Addiction

The concept of gambling as an addiction not unlike drug and alcohol addiction, has been
hotly debated over the past 30 years since the topic started receiving serious research and
academic attention. Researchers in the 1980s had been mainly concerned with determining
general population prevalence estimates and were not necessarily focused on an emergent
theory of pathological gambling as they attempted to ascertain the extent of the addiction
(Ladouceur 2004). Many writing on the subject had indicated that because of the fact that
nothing specific was ingested, such as a drug or an alcoholic drink (i.e., a ‘‘psychoactive
agent’’), gambling could not be considered in the same vein as those addictions. The
various neurochemical explanations attributed to the action of drugs was not satisfactorily
generalized to the milieu of pathological gambling (Walker 1989). Some researchers,
however, despite the absence of a psychoactive agent, argued that continued gambling
research could support an entirely psychological account of addiction in general (Dick-
erson 2003).
Numerous articles have attempted to integrate Jacobs’ (1986) General Theory of
Addictions with regard to ‘‘excessive’’ gambling, and to ascertain if it is indeed applicable.
Jacobs (1989) defines an addiction as ‘‘a dependent state acquired over time by a pre-
disposed person in an attempt to relieve a chronic condition’’ (p. 35). Additionally, he
postulates that a ‘‘conducive’’ environment accompany the predisposing factors in order for
an addictive behavioral pattern to emerge. Thus, it is likely that by chance, the predisposed
person may happen upon a novel experience (in this case, gambling), and this ‘‘chance
triggering event’’ could motivate the person to actively seek out this activity in the future
(Gupta and Derevensky 1998). These predisposing factors include abnormal physiological
resting states, evidence of emotional distress, greater levels of dissociation, and comor-
bidity with other addictive behaviors.
In fact, Gupta and Derevensky (1998) provided good empirical evidence of Jacobs’
General Theory of Addiction as applied to gambling in their path analysis of gambling
among adolescents. The model tested by the two researchers showed a strong path from the
physical and emotional predispositions, to a desired need for escapism, to the severity of
gambling behavior. They concluded that ‘‘gambling severity was empirically found to be
caused by the need to escape, or dissociate, which is fueled by aversive physiological and
emotional states’’ (p. 41), giving strong support for gambling’s applicability to Jacob’s
General Theory of Addictions as a coping response, albeit a negative one, to aversive life
situations.
Blaszczynski and Nower (2002) proposed and outlined a detailed pathways model of
problem and pathological gambling which is defined by three distinct subgroups of
gamblers: those who are behaviorally conditioned, those who are emotionally vulnerable,
and those who are antisocial and impulsivist gamblers. Their model attempts to ‘‘integrate

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biological, personality, developmental, cognitive, learning theory, and environmental

factors … into a theoretical framework’’ (p. 491). Incorporating much of Jacobs’ General
Theory of Addiction, Blaszczynski and Nower’s model has some shared aspects common
to the three subgroup pathways described: availability and access to gambling, classical
and operant conditioning leading to the development of a habitual pattern of gambling, and
the appearance of strong biased and distorted cognitive schemas. These schemas involve
faulty beliefs about personal skill and control over outcome, attribution, superstitious
thinking, and probability theory, among others.
More recently, medical research has shed light on an illness theory for gambling
addiction, and has found that the most afflicted have the kinds of brain disorders found
among drug and alcohol abusers. Using functional magnetic resonance imaging (fMRI),
the brains of addicted gamblers have been scanned while viewing gambling-related images
or gambling with real money. The results have shown that dopamine, a chemical that
regulates human behavior, including weighing relative rewards and anticipation of those
rewards, floods the nucleus accumbens (midbrain) of these gamblers. This rush of dopa-
mine created something not found in non-addicts, namely, intense feelings of excitement
and interest in the gambling addicts (Benston 2009).
What is still not known is exactly why gambling behavior stimulates dopamine pro-
duction in some people. In order to get the dopamine rush, the gambling addict seeks out
opportunities to gamble not for pleasure, but for the chemical rewards. This causes a
vicious circle in which the person focuses on gambling at the expense of everything else, a
hallmark of gambling addiction. Supporting this illness theory is the discovery and use of
naltrexone, a drug used to treat drug addicts and alcoholics, in the treatment of pathological
gamblers. This drug, which blocks dopamine release and reduces the addict’s cravings, has
been used in combination with counseling therapy to successfully treat gambling addicts
(Grant et al. 2008).

Pathological Gambling

Gambling addiction as a psychiatric diagnosis is medically defined as pathological gam-

bling in both the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text
revision (DSM-IV-TR) (APA 2000) and the International Classification of Diseases, 10th
revision (ICD-10) (WHO 1992). Both classification systems describe objective and
behavioral diagnostic criteria, and classify pathological gambling as an impulse control
disorder. Currently, the American Psychiatric Association is in the process of determining
exactly how pathological gambling might be classified in the upcoming fifth edition of the
DSM (Petry 2010), scheduled to be released in May 2013 (www.dsm5.org). Both the
DSM-IV-TR and ICD-10 criteria are included here as Appendix 1.
As stated, with the venues for legalized gambling ever increasing, the need to identify,
diagnose, and treat gambling addiction issues also grows exponentially. Conservative
estimates of the number of individuals who gamble socially who may qualify for diagnosis
as a pathological gambler vary from 2 to 5 %, thereby affecting millions of people in the
United States alone (Lesieur and Blume 1987). Because of this, the ability to accurately
screen for and treat these large numbers of people is of paramount importance. For the
purposes of this paper, pathological gambling (also referred to in the literature and else-
where as disordered gambling or compulsive gambling) can be very briefly defined as a
condition in which the gambling behavior cannot be controlled, is compulsive as well as

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destructive, with substantially negative consequences personally, socially, financially,

psychologically, somatically, and vocationally.
Pathological gamblers have little to no control over their behavior and are often pre-
occupied with thoughts about gambling. Withdrawal symptoms, similar to those commonly
experienced by those addicted to drugs or alcohol, may manifest themselves when the
individual attempts to curtail or stop his or her gambling behavior. It is worth noting the
discrepancies in the body of literature which often confuse the term problem gambling, a
precursor to pathological gambling along the diagnostic continuum of gambling behavior,
with pathological gambling as described above and diagnostically outlined by the DSM-
IV-TR and ICD-10. They are most decidedly not interchangeable terms, even though some
researchers (including a few cited in this paper and whose work is analyzed in this meta-
analysis), tend to do so erroneously.

Prevalence and Risk Factors

Researchers estimate that approximately 75 % of college students have gambled legally or

illegally during the past year (Barnes et al. 2010), and in addition, college students also
appear to have the highest prevalence estimates of pathological gambling (Blinn-Pike et al.
2007). The addiction prevalence has often been surmised to be higher in this particular
group than in the general population as referenced above, specifically, greater than the 5 %
figure considered to be the upper limit overall, with various estimates computed between 6
and 8 % (Lesieur et al. 1991; Stuart 2011). Regardless of the various estimates of path-
ological gambling in the college student population, society is still ultimately faced with an
affected number of young people which amounts to hundreds of thousands, and perhaps
millions worldwide. As seen above, the societal cost is considerable, and in a global
context, may be inestimable.
College students are particularly susceptible to falling into gambling addiction because
of the confluence of several different factors, creating a so-called ‘‘perfect storm’’ in what
we term as ‘‘The Five A’s’’: age, with the college years being associated with a wide range
of risky behaviors (LaBrie et al. 2003); availability of wide-scale legal (and illegal)
gambling, including online gambling; acceptability of gambling operated by various
government entities and integrated into mainstream culture; advertising and media which
promote, glorify, and glamorize gambling as a sport; and access to monetary funds,
especially from student loans and through numerous credit card solicitations. The result is a
population group specifically targeted by the media, a vast number of whom have the
resources, proximity, and free time to become involved in the myriad options of gambling
available, such as casinos, Internet gambling, poker games on- and off-campus, state
lotteries and numbers games, instant scratch-off tickets, and sports gambling (Blinn-Pike
et al. 2007). This does not begin to include the vast number of illegal and informal modes
of gambling (often involving a bookmaker) that may expose a student to personal safety
issues, as well as the obvious and inevitable monetary losses.
Although most of the research in the field focuses on risk factors for college students,
LaBrie et al. (2003) cited two specific protective factors: one’s belief that religion and the
arts are personally important, and having a parent who has obtained a college degree.
Clearly, much more research is needed in this important area of identifying protective
factors that may help buffer college students from the numerous negative consequences of
problem gambling (Stinchfield et al. 2006).

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Student-Athletes

The aforementioned personal safety issues with regard to gambling addiction (among a
litany of other activities) is no more apparent than with a certain subgroup of the college
student population, specifically student-athletes, who may find themselves as easy prey for
bookmakers and other undesirable figures, which may put them at risk for physical harm.
This is evidenced by the 2003 murder of a college student at the University of Wisconsin-
Madison, and the 2007 murder of a varsity athlete at the University of Memphis. Both of
these students’ untimely deaths were ultimately linked back to gambling (McComb and
Hanson 2009).
The area of pathological gambling in college students and student-athletes was gener-
ally ignored in favor of an initial focus on treatment of pathological gambling in adults and
on addiction and prevention in adolescents. Gambling scandals in the late 1990s at Arizona
State University, Boston College, and Northwestern University caught the attention of the
National Collegiate Athletic Association (NCAA), its member institutions, the press, and
fans of intercollegiate athletics. Other researchers began to believe that college students
might represent the segment of our population with the highest rate of pathological
gambling (Weiss and Loubier 2008); thus, interest in gambling behavior of student-athletes
began to get some attention in the body of gambling addiction literature (Ellenbogen et al.
2008).
College student-athletes seem to be particularly at risk for developing serious gambling
problems, perhaps in part due to their inherent competitive nature and willingness to
engage in risky behaviors, as well as the culture of athletics in general (Weiss and Loubier
2008). In light of this, and because of the growing number of gambling-related scandals
among student-athletes at member institutions, the NCAA has identified gambling by
athletes as a major threat to the integrity of intercollegiate athletics and responded with the
development of a comprehensive education program for student-athletes at NCAA member
schools, and through the website www.dontbetonit.org (NCAA 2003).
One study that looked at gambling and other high risk behaviors in college students
(Stuhldreher et al. 2007) surveyed over 1,000 Pennsylvanian college students, with part of
their research devoted to noting patterns of gambling among student-athletes as compared
to non-athletes. The researchers found that significantly more athletes (17 %) than non-
athletes (9 %) reported frequent gambling (p \ .01) and also had more gambling debt
(5 %) than did non-athletes (1 %; p \ .001). However, a significantly higher percentage of
athletes actually sought help for gambling problems compared with non-athletes (7 vs.
4 %; p \ .05). They also found that these significant differences were gender-specific to
the men in the sample only.
Ellenbogen et al. (2008) used the 2003 NCAA survey data of over 20,000 student-
athletes to determine whether certain student-athletes were more prone to frequent or
problem gambling behavior. Looking at gender, race, type of sport played, and gambling
mode, among many correlates, Ellenbogen and colleagues reported several interesting
results. First, they found that Hispanic males reported the highest problem and pathological
rates and that the percentage of gamblers was highest among Division III student-athletes,
followed by Divisions II and I. In addition, the only significant difference between student-
athletes in team and individual sports was that members of team sports were more likely to
gamble. Student-athletes in high profile sports (i.e., football and basketball) were more
likely that other student-athletes to gamble, gamble weekly, be at-risk gamblers, be
pathological gamblers, and place more money on sports wagers.

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Symptomology

Some of the symptomology of pathological gambling specific to college students often

include substantially decreased academic performance, as well as physically and socially
isolating behavior (McClellan et al. 2002; Petry and Weinstock 2007). This is in addition to
considerable debt, missed classes, interpersonal relationship difficulties, and suicide
(Stinchfield et al. 2006). College students who exhibit pathological gambling have also
displayed traits of other high risk factors such as promiscuous and unsafe sexual behavior,
alcohol abuse, and increased use of both tobacco and marijuana (LaBrie et al. 2003). Since
limited attention has been paid specifically to college student gambling (Takushi et al.
2004) for various reasons, not the least of which is college students’ perceived status as an
intermediary group inconveniently sandwiched between adolescents and adults, there is a
gap in the literature that has resulted in two limited published meta-analyses estimating the
prevalence of pathological gambling among college students.
Today’s college students are exposed not only to alcohol and drug use but also to
gambling, both on campus and in the surrounding community. As mentioned, gambling
disorders are associated with numerous negative consequences and are often highly cor-
related with other high-risk behaviors in the college student population (Engwall et al.
2004). To compound this, the proximity of a college or university to a casino appears to
have a direct influence on an increased rate of gambling addiction in the college student
population (Bailey et al. 1997; Adams et al. 2007), as is similarly reflected in the adult
population whose rate doubles with a relatively ‘‘close’’ proximity to gambling venues
(Sumitra and Miller 2005). Despite the prevalence of on-campus gambling, and while
almost all schools have alcohol and drug policies, only 22 % of U.S. colleges and uni-
versities have formal policies on gambling (LaBrie et al. 2003) and only 7 % of admin-
istrators reported receiving in-service education about gambling-related issues for faculty
and staff (McComb and Hanson 2009).

Comorbidity

Gambling disorders are highly comorbid with other psychiatric disorders, particularly
those related to substance use (Petry 2005). In 2005, the National Epidemiologic Survey on
Alcohol and Related Conditions (NESARC), the largest study of its kind to date, found that
pathological gamblers had a six times greater risk of having a diagnosis of alcohol abuse
and a increased risk of 4.4 times for a substance use disorder, compared with non-
gamblers. Additionally, rates of major depression and dysthymia were each about three
times higher in pathological gamblers than in non-gamblers. Other comorbid disorders
more common in pathological gamblers included generalized anxiety disorder, panic
disorder, manic episodes, and specific phobias, including social phobia. In the NESARC
sample, pathological gamblers also had an increased risk of having a personality disorder
by a factor of eight (Hodgins et al. 2011).
With regard to substance use disorders, it appears that pathological gambling more often
was a predictor of the subsequent onset of substance use disorders than vice versa (Kessler
et al. 2008), bringing the idea of a bidirectional nature of the association between path-
ological gambling and psychiatric disorders into question, and introducing an avenue for
further research. Clearly, college students, who are already at various risks for mental
health issues due to the increased stressors of college life and academic challenges, as well
as social adjustments, need to be paid special attention when it comes to ascertaining their
mental health status, especially when presenting at counseling centers for gambling issues.

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Motivations

All of this information begs the question ‘‘Why do college students gamble?’’ The reasons
and motivations for student gambling are varied, and not just as simplistic as wanting to
win some ‘‘easy’’ money. The limited research in this particular area has concluded that
college students engage in gambling activity for a myriad of reasons, including for fun and
excitement, competition, and to experience the thrill of winning (Neighbors et al. 2007), as
well as to escape relationship problems, boredom, and perceived life stressors (Lesieur
et al. 1991). Additionally, students gamble to take risks, experience uncertainty, and test
their perceived skill, as well as to conform because of peer pressure and ‘‘because everyone
else is doing it’’ (Burger et al. 2006, p. 710).
It is important to note the most recent trends in college student gambling behavior,
namely, the poker craze on college campuses and the tremendous increase of Internet
gambling among college students (McComb and Hanson 2009). Internet gambling is, by its
very nature, particularly prevalent on college campuses, especially for technologically-
savvy students who can easily engage in increasingly attractive online gaming environ-
ments in the privacy of their own dorm rooms and residence halls. As indicated above,
though, this can result in a substantial increase in both social and physical isolation (Petry
and Weinstock 2007). Additionally, the current poker craze, as evidenced by television
shows on various networks, including the World Series of Poker’s Main Event on ESPN, as
well as university-sponsored ‘‘poker nights’’ for undergraduates, establishes this form of
gambling as a notable (and implicitly acceptable) presence on college campuses (McComb
and Hanson 2009). To compound this, it is relevant to note that university students are a
specific population that is currently and actively being singled out and targeted by poker
marketing campaigns (Hardy 2006). In certain college residence halls, the poker boom is
visible virtually everywhere, and its presence is practically ubiquitous, as can be noted
from several students’ perspectives on the issue (Caswell 2006): one student, working as a
residence assistant, was quoted as saying, ‘‘It [poker playing] is everywhere, all the time’’
(p. 29).

Previous Meta-Analyses

The first published synthesis of the research material by Shaffer, Hall, and Vander Bilt in
the American Journal of Public Health in 1999 examined prevalence rates of disordered
gambling among adults, adolescents, and college students in the United States and Canada.
The authors employed 119 prevalence studies overall, but were only able to locate 14
studies representing 8,918 students between 1987 and 1997 specifically related to identi-
fying the percentage of college students with a pathological gambling addiction. They
computed an overall estimated rate of 5.05 % [3.55 %, 6.56 %], as determined by the
South Oaks Gambling Screen (SOGS), the most commonly used assessment. Different
instruments were also used in the synthesis to ascertain the prevalence in the college
population studied, but the authors only reported the estimate obtained from the SOGS
specifically. Compounding this problem, the authors utilized the Kruskal–Wallis test for
their synthesis, which implicitly assumes a fixed effects model (Blinn-Pike et al. 2007), and
is arguably inappropriate for this type of single-group meta-analytical synthesis. Finally,
and curiously enough, this meta-analysis did not include a list of any of the articles
synthesized in its references section or elsewhere, thus making it virtually impossible to

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verify any of the published results, including the prevalences estimated for college
students.
The other meta-analysis published in this milieu was authored by Blinn-Pike, Worthy,
and Jonkman in 2007 in the Journal of Gambling Studies, and identified 15 studies between
1999 and 2005 examining college student gambling rates among the 9,848 students rep-
resented. This particular meta-analysis had a much more stringent set of inclusion rules
than the Shaffer meta-analysis. Most of the Blinn-Pike inclusion rules were also mirrored
in this present synthesis, including the use of the SOGS (and in one case the SOGS-CT, a
modified version) as the primary assessment instrument. The authors, using a random
effects model for synthesizing the prevalence estimates, calculated an estimated proportion
of disordered gambling among college students to be 7.89 % [5.37 %, 10.41 %], citing, in
part, ‘‘the meteoric rise in gambling in the last two decades’’ (p. 175). Blinn-Pike, Worthy,
and Jonkman also indirectly set the stage for this author’s current research by stating that
‘‘college students are on a trajectory to report increased rates of disordered gambling’’ as
gambling opportunities are predicted to increase (p. 181). Unlike the previous meta-
analysis, the authors did manage to list the articles synthesized in the references section,
but did not explicitly make it clear in the section (by asterisk, or otherwise) which studies
were used, among the many articles cited as references.
The most recent prevalence meta-analyses in the last several years have appeared in the
journal International Gambling Studies and were mostly concerned with general popula-
tion estimates in Australia and New Zealand in respect to another assessment interest, the
Problem Gambling Severity Index (PGSI) and also proximity to gambling venues.

The South Oaks Gambling Screen

Both aforementioned syntheses used the South Oaks Gambling Screen (SOGS), the de
facto preferred instrument of choice in the literature. Developed in 1987 by Henry Lesieur
and Sheila Blume, the SOGS is about as close to a proxy gold standard as is available in the
literature (Toneatto 2008). The SOGS test items were selected based on a modification of
the DSM-III diagnostic criteria for pathological gambling, and after several years of sta-
tistical research, were narrowed down to 20 items (Lesieur and Blume 1987; Shaffer and
Hall 2001).
The SOGS was originally intended to screen for pathological gambling in clinical
settings, but over the past 30 years, has since expanded to other purposes, populations, and
settings, including prevalence estimate studies of pathological gambling in the general
population (Stinchfield 2002). The past-year self-report version has indicated good overall
classification accuracy (0.96), with better sensitivity (0.99) than specificity (0.75), indi-
cating that the SOGS tends to more often identify false positives (Stinchfield 2002). The
SOGS has demonstrated good validity and reliability among university students (Beaudoin
and Cox 1999; Lesieur et al. 1991).
The SOGS is a self-report questionnaire with many of the items centered on financial
issues, as well as the amount and type of gambling involved in by the individual. Response
format is primarily dichotomous (yes/no) with some Likert-scale items and should take no
more than 10 min to complete and score (Lesieur & Blume 1987). Questions such as ‘‘Did
you ever gamble more than you intended to?’’ and ‘‘Have you ever felt like you would like
to stop betting money on gambling, but didn’t think you could?’’ are typical of the SOGS
questionnaire. A score sheet is provided with easy instructions, with 0 indicating ‘‘no
problem with gambling,’’ 1–4 indicating ‘‘some problems with gambling,’’ and 5 or more
indicating a ‘‘probable pathological gambler.’’ As the SOGS has been employed in the vast

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majority of previous student gambling studies (Shaffer et al. 1999) as well as most current
research, the use of the SOGS as the principal measurement instrument is a primary
inclusion criterion for this research paper.

Purpose

This current review aims to analyze and synthesize the recent literature in this important
area of addiction studies and contribute to the overall knowledge base of college students
and the prevalence of probable pathological gambling, as well as fill the existing gap in the
body of literature. By examining the research done between 2005 and the present, this
writer is interested to discover if the overall rate reported by Blinn-Pike, Worthy, and
Jonkman has changed measurably in the past 8 years, as these same researchers predicted
in their 2007 meta-analysis. Because of the intense, systematic, and exponential prolifer-
ation of gambling venues and outlets, as well as the concomitant amount of advertising in
various media, not only in the United States and Canada, but worldwide, it is not unrea-
sonable to suspect that the previously reported rates may have increased appreciably since
the publication of the Blinn-Pike, Worthy, and Jonkman meta-analysis. Also of note for
this review is the influence of gender, specifically the male student percentage influence, on
the reported rates, and whether the rates in public or private academic institutions differ
significantly. Additionally, the influence of the age of the students surveyed on the reported
rates is of interest, as well as whether the rates have increased in any noticeable fashion on
a yearly basis from the period 2005–2013.

Hypotheses

The first hypothesis of this research synthesis is that the overall prevalence estimate of
probable pathological gambling in college students will show an increase from the Blinn-
Pike meta-analysis estimate of 7.89 %, based on the rationales explained above and out-
lined in the literature review. In the same vein, the second hypothesis is that this rate will
show an increase on a biennial basis from 2005 to 2013. The third hypothesized result is
that the prevalence rate will be directly related in a positive way to the percentage of male
students in each sample. A fourth hypothesis is that the prevalence rates in public academic
institutions, where students may come from less affluent families, and have more predi-
lection towards gambling for monetary gain, will be higher in those student populations
than in private institutions. Finally, it is hypothesized that the age of the student will have a
substantial impact of the rate of probable pathological gambling, namely, that as the
student ages and matures, the prevalence rates will decrease.

Data Collection

The process involved in obtaining information about probable pathological gambling and
its prevalence among college and university students was thorough and exhaustive. In
order to identify all possible studies between the years 2005–2013 inclusively, and using
the search terms ‘‘gambling’’ and ‘‘college students’’ as the primary search terms, as well
as using other synonyms such as ‘‘gaming’’ for ‘‘gambling,’’ ‘‘disordered’’ for ‘‘patho-
logical,’’ and ‘‘university students’’ for ‘‘college students,’’ PsycINFO returned 340 arti-
cles, PsycARTICLES resulted in 36 articles, ERIC identified 50 articles of interest, and

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MEDLINE contributed 18 additional papers. Dissertation Abstracts International was also

examined and contributed two completed dissertations which were ultimately used in this
meta-analysis. Bibliographies of the past two published syntheses outlined above were not
examined for possible contributions since this meta-analysis is concerned with articles that
were published after the respective publication of each meta-analysis, however, they were
consulted in terms of a initial review on the body of literature presented above.
In addition to this, this author used these aforementioned terms in search functions for
the online library of the Responsible Gambling Council (www.responsiblegambling.org),
as well as the online gambling library of the International Gambling Research Institute
(www.gamblib.org). Both are comprehensive resources of scholarly articles in the gam-
bling research field, which is still growing and relatively nascent in terms of generating
academic work. Additionally, www.springerlink.com, the online resource for the company
that publishes the seminal journal in the field, the Journal of Gambling Studies, among
many other academic titles, was invaluable during the research process. Similarly,
www.sciencedirect.com was helpful in obtaining additional information from other jour-
nals not covered by the Springer website.
In order to be included in this present analysis, the identified studies had to: (a) have
been published in or after 2005 (and not have been included in the Blinn-Pike meta-
analysis), (b) use the South Oaks Gambling Screen (SOGS) as the main gambling
pathology assessment instrument, (c) use a score of five or greater on the SOGS as the
marker for probable pathological gambling, (d) have the surveyed sample be comprised of
a general group of college/university students exclusively, (e) have taken place in a non-
clinical setting, and (f) report the percentage of the sample in the disordered gambling
range with specified criteria for how the sample was categorized.
Applying these inclusion criteria to the numerous journal articles, papers, presentations,
conference proceedings, reports, and dissertations identified resulted in 23 applicable
studies. Of these, one study had to be omitted because the sole author of the article did not
respond to three separate emails sent by this writer requesting additional information that
was not fully discernible from the available online abstract. The final tally for this synthesis
consisted of 22 discrete studies. The effect size of interest, namely, the percentage of
students scoring 5 or more on the SOGS, where it was not given directly, was computed by
dividing the amount scoring 5 or higher by the total amount of students surveyed. The
variances for the effect sizes of interest were computed using the formula pð1pÞ
n (Borenstein
et al. 2009), where p is the proportion of students scoring 5 or higher on the SOGS, and n is
the total number of student surveyed.

Data Evaluation and Coding Procedures

As this synthesis is a single-group summary attempting to ascertain a certain prevalence

rate, the study outcome was the percentage of those college students who scored a com-
posite tally of 5 or greater on the SOGS, thus indicating a probable pathological gambler.
Coding was done by the primary author and a fellow university researcher over the course
of several weeks. The main items being examined for coding were the percentage of
students scoring a 5 or greater on the SOGS, the percentage of male students, sample size,
age characteristics of the student sample, research funding details, the country in which the
study took place, and whether the students surveyed were enrolled in a public or private
university, or both. Interrater reliability for these coded items were as follows: percentage

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of students scoring a 5 or greater on the SOGS, 82 %; percentage of male students, 91 %;

sample size, 82 %; funding details, 100 %; country in which the study took place, 100 %;
whether the students were from a public or private university, 55 %. Discrepancies were
resolved by a scheduled meeting between the two coders in which both went over the
rationales for their respective decisions, and carefully re-read the texts, until both coders
agreed upon the proper way to code any disputed or unclear item. There were no items
upon which the two coders could not eventually come to an agreement.

Omitted Studies

Note that the interrater agreement listed in the previous paragraph reflects agreement on
independent proportions that were gleaned from the thorough examination of the previously
identified 22 studies. One study from an Australian journal (Blaszczynski eta l. 2008) had to
be excluded because the same authors (in a different order of authorship) produced an article
in an American journal with the exact same information, i.e., sample size, characteristics,
prevalence rates, etc., and would thus be redundant data. In addition to this, another study
had to be eliminated after consultation with the second coder, and after determining that the
sample of students was of individuals who had already admitted to gambling ‘‘at least once a
week,’’ thus contaminating and vastly inflating the overall results.
Still another study was omitted because the group of students surveyed contained a
smaller unreported number of participants who were university staff and faculty. Inter-
estingly, a study of student gambling and casino proximity (Adams et al. 2007) was not
included because the probable pathological gambling rate reported (0.9 %) was so
unusually small that the authors themselves were nonplussed by it, and among the many
limitations listed, specifically warned that ‘‘the data are not useful to estimate prevalence
rates’’ (p. 13). Finally, one study (Goodie 2005) was determined to contain two different
prevalence rates, with two different groups of students, thus resulting in a sole study
containing two independent proportions for the purposes of this review. This ultimately
resulted in 19 data points from 18 qualifying studies for this synthesis, more than either of
the two previous published meta-analyses. See Table 1 for a complete alphabetical listing
of the articles reviewed and utilized in this meta-analysis.

Data Analysis

The analyses were based on current meta-analytical techniques. The original methods were
proposed by Hedges and Olkin (1985) and also described in Cooper et al. (2009). In our
various analyses, we adopted either random-effects or mixed models. Random-effects
weights were computed as wi ¼ 1=ðvi þ S2 Þ, where the between-sample uncertainty (S2)
for k effects was estimated using restricted maximum likelihood estimator. When adopting
mixed-effects models, both predictor variables and additional between-studies uncertainty
in the effect variances are incorporated. All computations were conducted in R (R Core
Team 2012), using the metafor package (Viechtbauer 2010). Categorical and regression
analyses were conducted to examine differences due to gender, age, status as a public or
private school, and year of each study’s publication. For both continuous and categorical
analyses, the results reported were based on mixed-effects models estimating a common
between-study variance for each moderator. Weighted mean effects, standard errors, and
Q statistics are presented within each analysis.

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Table 1 Included primary studies

Study (years) Publication College Country of Number of Percentage
type setting type origin students scoring as PG

Arthur et al. (2008) Journal Public Singapore 148 8.7

Burger et al. (2006) Dissertation Public USA 264 3.0
Butler (2011) Dissertation Private USA 270 3.1
Fortune and Goodie Journal Public USA 72 32.4
(2010)
Goodie 1 (2005) Journal Public USA 200 16.0
Goodie 2 (2005) Journal Public USA 384 23.0
Kido and Shimazaki Journal Public Japan 96 4.2
(2007)
Kuentzel et al. (2008) Journal Public USA 191 6.8
Locke et al. (2013) Journal Public USA 1,979 7.3
Moodie (2008) Journal Both Scotland 1,483 3.9
Oyebisi et al. (2012) Journal Both Nigeria 2,400 14.2
Parker et al. (2005) Journal Private Canada 562 8.7
Petry and Weinstock Journal Both USA 1,356 11.8
(2007)
Platz et al. (2005) Journal Public USA 995 11.05
Tang and Wu (2009) Journal Both China 979 6.4
Weinstock et al. 1 Journal Private USA 159 25.0
(2007)
Weinstock et al. 2 Journal Public USA 1,007 8.9
(2007)
Wickwire et al. 1 Journal Private USA 302 6.0
(2007)
Wickwire et al. 2 Journal Public USA 233 6.6
(2008)

Both both public and private colleges, PG pathological gambler

Dependence

In meta-analysis, independence of the effects is an essential assumption of most standard

analyses (see Becker 2000). In our meta-analysis, one study (Goodie 2005) contributed two
data points. However, given that these two data points are from independent samples (i.e.,
the research reported two separate results with different sets of participants), the
assumption of independence was not violated.

Publication Bias

Publication bias occurs because statistically significant results are more likely to be pub-
lished than non-significant results. Thus, publication bias should be examined in every
meta-analysis. When publication bias (Rothstein et al. 2005) exists, meta-analysis results
may not fully represent the population of interest. Thus, both published and unpublished
studies were included in this review. Publication bias was assessed via a commonly used
graphical representation (i.e., funnel plot) and Egger’s regression test. As can be seen in
Fig. 3, there is a substantial degree of asymmetry in the funnel plot, and only six data

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points actually fit inside the so-called ‘‘funnel.’’ In addition, the results of the Egger’s
regression test indicated that there is statistically significant asymmetry (z = 4.15,
p \ .05). These two assessments of publication bias indicate that the results of this meta-
analysis should be interpreted with some caution. However, there are two good reasons to
suspect that perhaps publication bias is not a major issue or concern for this particular
meta-analysis. First, after performing the ‘‘trim and fill’’ procedure (Duval and Tweedie
2000), several data points appear below 0.00 on the ‘‘observed outcome’’ (i.e., percentage)
scale, which, in a prevalence study incorporating percentages/proportions, is obviously not
possible (see Fig. 4). Second, studies such as the ones analyzed in this report, which are
simply percentages of college students scoring a particular number on a given assessment
instrument, seemingly do not have any vested interest in whether these proportions are
comparatively ‘‘low’’ or ‘‘high,’’ and are merely reporting the overall results, thus making
any claims of publication bias in this present study tenuous, at best. None of the articles
utilized were evaluating a treatment effect or protocol, so it would seem none would have
any vested interest in inflating (or deflating) the proportions.

Results

A forest plot of the studies included in the synthesis as well as a frequency histogram of the
19 effect sizes are presented in Figs. 1 and 2 above. The estimated percentages of probable
pathological gamblers among college students ranged from 3.00 to 32.00 % with a median
of 8.70 %, and the distribution was slightly positively skewed. The overall weighted-
average estimated percentage of probable pathological gamblers among college students
under the random-effects model was 10.23 % (SE = 1.56, p \ .05), with a 95 % CI from
7.17 to 13.29 %. The homogeneity test (QT(18) = 330.30, p \ .05, I2 = 97.54 %) indi-
cated that the effects did not all arise from the same population. This very substantial
heterogeneity is consistent with syntheses of single-group summaries (Borenstein et al.
2009). Next, moderator analyses to examine potential sources of variability were con-
ducted. For categorical analyses, the weighted mean effects, standard errors, and Q sta-
tistics are presented in Table 2 for the three data sets.

Moderators

While the primary purpose of this research paper was to conduct a meta-analysis of the
studies reporting the prevalence proportions of probable pathological gambling among
college and university students, several moderators of interest were examined to determine
their possible impact on these rates, and any implications that may arise (Fig. 3).

Country

This synthesis included 13 studies done in the United States and Canada, as well as five in
other countries (China, Japan, Nigeria, Scotland, and Singapore). To that end, country of
origin was analyzed as a moderator and as such we obtained a probable pathological
gambling prevalence that could be generalized to the population of North American col-
lege and university students. A categorical analysis was conducted to compare the effect
values from the studies that reported on students in the United States and Canada
(14 effects from the 13 studies) to five effects that did not. An initial fixed-effects test
indicates between-groups differences. However, the difference between the two sets of

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Fig. 1 Forest plot of studies included in the synthesis. Note. Study numbers correspond with their
alphabetical order as listed in Table 1

Fig. 2 Distribution of prevalence values

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Table 2 Results for moderator analyses

Categories Prevalence

k Mean and (SE) QResidual

Overall 19 10.23* (1.56) 330.30*

Categorical moderators
Country (QB (1) = 1.12)
US and Canada 14 11.26* (1.85) 179.97*
Other 5 7.50* (3.03) 143.20*
School type (QB (2) = 0.19)
Public 11 10.87* (2.23) 116.97*
Private 4 10.06* (3.67) 42.96*
Both 4 9.07* (3.56) 165.10*
Continuous moderators
Gender (QB (1) = 0.84)
Slope 19 -0.01 (0.01) 323.32*
Age (QB (1) = 1.71)
Slope 14 -1.72 (1.31) 203.72*
Year (QB (1) = 0.19)
Slope 19 -0.31 (0.72) 328.96*

The degree of freedom for QResidual are k – 1; k = number of effects

* p \ .05

Fig. 3 Funnel plot of studies included in the synthesis. Note. Though it may appear that only 18 data points
are represented in this plot, the first blot furthest to the left is actually two convergent data points, and this
can be confirmed by enlarging the figure

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effects disappeared when the additional unexplained variation was incorporated into the
analysis (QB(1) = 1.12, p = .30). Significant variation was found among both set of
effects. Specifically, for studies reporting on students in the United States and Canada
(QR(13) = 179.97, p \ .05) and for studies reporting on students on other countries
(QR(4) = 143.20, p \ .05). Nonetheless, under mixed-effect model, the average weighted
mean for the United States and Canada was 11.26 % (SE = 1.85) and for others was
7.50 % (SE = 3.03), both were different from zero. This indicates that on average the
percentage of studies reporting on students in the United States and Canada are a point
higher overall than the overall percentage calculated and reported above. Incidentally, the
five non-North American studies were lower than the overall and U.S./Canadian rates
(Fig. 4).

School Type

This study was also designed to examine whether the students’ enrollment in either a
public or private university had any substantial effect on the prevalence rates reported. The
studies were grouped as ‘‘public’’ (k = 11), private’’ (k = 4), or ‘‘both’’ (k = 4) for the
studies had a mix of both public and private students in the sample. The between-groups
test was not statistically significant with QB(2) = 0.19, p = .91, indicating that this
moderator did not explain any differences among the effects under the mixed-model. The
three average weighted effects differed from zero, with mean effect sizes of 10.87 %
(SE = 2.23), 10.07 % (SE = 3.67), and 9.07 (SE = 3.56), for public, private, and both
types of schools respectively. The three QR were significant, indicating that effects within
group results varied more than expected by chance.

Fig. 4 Funnel plot of studies using the ‘‘trim and fill’’ procedure

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Gender

The influence of gender on the results cannot be underestimated, given that males have a
higher prevalence of gambling in general (Oster and Knapp 1998) and of pathological
gambling (Lesieur et al. 1991). To that end, the studies were examined to see how the
percentage of male students in the samples influenced the prevalence rates. Those effects
(k = 5) that did not report a gender breakdown were not included. Quite coincidentally,
these five omitted studies were the international studies, and thus the following statistics
also represent the U.S./Canadian studies only. However, using ‘‘percentage of male stu-
dents’’ as a moderator variable in a weighted regression model, the initial variability that
this model explained disappeared when the additional unexplained variation was incor-
porated into the mixed-model analysis (QM(1) = 0.84, p = .36). Thus, percentage of male
students did not relate significantly to the size of the effects under the mixed-effects model
(b = -0.0001, SE = 0.0001).

Age

Average age of student sample was used as a moderator variable in a regression model for
those effects (k = 13) which provided such information. The initial explanatory power of
the model under fixed-effect tests disappeared when the additional unexplained variation
was incorporated into the mixed-model analysis (QM(1) = 1.72, p = .19). This result
suggests that the student’s age does not impact the prevalence in a statistically significant
fashion. More specifically, as the age increases by 1 year, the rate of probable pathological
gambling goes down by 1.72 % (SE = 1.31, z = -1.31, p = .19). Thus, it appears then
that the age of the student does not have any influence one way or another in terms of the
prevalence of probable pathological gambling.

Year of Publication

The final predictor that we examined was the year of publication of the study. The initial
variability that this model explained disappeared when the additional unexplained variation
was incorporated into the mixed-model analysis (QM(1) = 0.19, p = .67). Thus, year of
publication did not relate significantly to the size of the effects under the mixed-effects
model (b = -0.31, SE = 0.72).

Discussion

This is the third reported attempt to utilize a meta-analytical approach to synthesize the
prevalence rates of pathological gambling among college and university students, and
the first to include international studies from outside the United States and Canada. The
procedure allowed for the synthesis of findings across multiple sources of estimates. The
first meta-analysis was reported by Shaffer et al. (1999) using 14 studies with college
students, and the second synthesis was reported by Blinn-Pike and colleagues in 2007, and
utilized 15 studies. This present study exceeds both, synthesizing 19 independent estimates
from 18 studies, and encompassing over 13,000 college students.

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Methodology

The prevalence estimate for college students reported here (10.23 %) was higher than that
reported by Blinn-Pike and colleagues (7.89 %) in 2007 and more than double the rate
reported by Shaffer et al. in 1999 (5.05 %). In fact, the percentage of college students with
probable pathological gambling reported here is indeed higher as hypothesized at the onset
of this synthesis, but considerably larger than we had anticipated. The 10.23 % estimated
in this paper is very close to the 95 % confidence interval’s upper bound of the Blinn-Pike
estimate. Even after controlling for the influence of international reports, the prevalence
rate actually went up more than one percent (11.26 %) when attempting to generalize to
the population of North American college and university students.
Second, the methodologies differed in the present study from the first two published
meta-analyses in several significant and important ways. The more recent meta-analysis
(Blinn-Pike et al. 2007), which was more similar in methodologies to this present study,
did not include dissertations, unpublished reports, or international studies among its studies
analyzed. However, unlike the first meta-analysis published in this milieu (Shaffer et al.
1999), this present study and the Blinn-Pike study used a random effects model to calculate
the overall prevalence rate. Shaffer and colleagues utilized the Kruskal–Wallis test, which
implicitly assumes a fixed effects model (Blinn-Pike et al. 2007), and is arguably inap-
propriate for this type of single-group meta-analytical synthesis.
While meta-analysis is the correct statistical tool for synthesizing these estimates in the
state of the research regarding college student gambling, it is necessarily limited by the
amount and content of the literature involved. The large degree of heterogeneity reported
here resulted in a relatively wide confidence interval for the prevalence of pathological
gambling in college students [7.17 %, 13.29 %], but it is interesting to note that the
prevalence estimate reported by Blinn-Pike et al. (2007) is close to the lower bound of this
present study’s confidence interval. Additionally, this research’s estimated lower bound, in
turn, is well above the synthesized estimate reported by Shaffer and colleagues in the first
meta-analysis conducted in this milieu in 1999.
Finally, in terms of the hypotheses presented earlier in this paper, it appears that, out of
the five hypotheses stated, only one was actually shown to be accepted, namely, that the
rate of probable pathological gambling in college students has increased appreciably since
2005. The research presented herein does not support the influence of age of the under-
graduate student, nor the type of institution (public or private) which the student attends,
and not the year in which the study was published on those proportions.

Limitations

Although this meta-analysis contains more studies encompassing a larger number of stu-
dents surveyed than either of the two previous meta-analyses, it is, however, not without its
limitations. As indicated earlier in this paper, while the SOGS has demonstrated good
validity and reliability among university students (Beaudoin and Cox 1999; Lesieur et al.
1991), it does tend to more often identify false positives (Stinchfield 2002). However, the
SOGS’ versatility and use in the vast majority of research articles makes this instrument
hard to dismiss. While there are other relevant and psychometrically acceptable instru-
ments available to assess probable pathological gambling (e.g., the Inventory of Gambling
Motives, Attitudes, and Behaviors), none are used as commonly as the SOGS is in its
various forms in the United States and abroad, both clinically and in research. The five

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identified international studies did report evidence of sufficient validity and reliability
estimates for the cases in which translated versions of the SOGS were implemented,
however, future meta-analyses may want to include other instruments for assessing gam-
bling pathology in the college student population.
Also, the amount of studies utilized for this synthesis was necessarily limited by the
somewhat narrow range of years (2005–2013), but this was a function of the proposed study
itself, and its stated purpose to provide a contrast and comparison to the other previous two
meta-analyses in a chronological context. The amount of international studies was relatively
small (n = 5) as well because of this, and also because of the specific usage of the SOGS as the
assessment instrument of choice, as outlined above. In addition to this, the studies that were
identified as candidates for this present synthesis were not consistently reliable in terms of
reporting certain demographic information of interest which made coding for such items as the
male/female SOGS score breakdown impossible (in fact, none of the studies reported this).

Implications and Recommendations

If, as it appears, the rate of pathological (e.g., disordered) gambling among college students
is dramatically on the rise, then certain issues need necessarily be addressed in the near
future. The vast proliferation and glamorization of gambling in terms of increased gaming
venues, media attention, Internet gambling sites, and states’ continued emphases on lot-
teries and scratch-off cards as a major stream of revenue, etc., all seemingly contribute in
part to this startling prevalence rate increase. In addition, if these trends continue upward as
is projected, it is only logical to assume that this increased rate in college students would
result in a concomitantly increased rate in the adult population as these college students
age. This, in fact, has already been evaluated and reported (Shaffer et al. 1999). Thus, the
implications of this rate increase in a societal context cannot be underestimated. As seen at
the start of this report, the monetary costs alone are staggering.
To that end, those in most direct contact with these college students, namely, colleges and
universities, need to immediately take it upon themselves to develop and implement strategic
gambling education and harm reduction with their students. This could easily be incorporated
into the programs that students regularly receive with similar educational programs involving
sex, drug, and alcohol education, as well as violence prevention. Even something as seem-
ingly simple as educational brochures and materials distributed at new student orientation, as
well as mental health and medical centers on campus would be a good place to start. This
would be especially noteworthy and beneficial at university campuses which may be in close
proximity to large, Las Vegas-style casinos, as well as other gambling outlets (the so-called
‘‘racinos’’—racetracks where slot machines and video lottery terminals have been added)
where the minimum age to gamble is often only 18, the same age as most incoming freshmen
college students. Also, universities should seriously consider not sanctioning on-campus
gambling activities including poker tournaments and casino nights.
Additionally, college counseling centers, if they are not already, should also include
screening (with such instruments as the SOGS) for probable pathological gambling with
those students coming in or referred for services, along with the other litany of disorders
and problems generally screened for. Lesieur et al. (1991) suggested that all students
coming into a counseling center for mental health issues be screened for problem gam-
bling. Counseling personnel should at least be able to refer students to community
resources to help those individuals identified as probable pathological gamblers such as
Gambler Anonymous, gambling help-lines, and professional counseling services.

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Appendix 2 contains a useful list of online college gambling resources. While not a focus
of this particular prevalence study, certain sub-groups of students, it has been seen in the
literature, such as student-athletes (as expounded upon in this study’s introduction), stu-
dents of color, those in fraternities and sororities, as well as gay/lesbian/bisexual/trans-
gender students are at an increased risk for gambling problems above and beyond those
prevalence estimates reported here, and this obviously needs to be researched further, and
also be taken into account when counseling centers come into contact with such students
(NCAA 2003; Rockey Jr. et al. 2005; Stinchfield et al. 2006; McComb and Hanson 2009).
Finally, it is sincerely hoped that this meta-analysis and the notable increase in prev-
alence estimates synthesized within the past 9 years will serve as a ‘‘wake-up’’ call to both
researchers and university administration alike. The results presented should encourage not
only more needed research in terms of the college and university student body as a distinct
population unto itself, but also prompt colleges to develop and implement programs to
address, educate, prevent, identify, and treat those students who are most at risk for
pathological gambling problems. When one stops to consider that essentially one in 10
college students has a severe gambling problem, it is a surprising and sobering thought
indeed. As has been demonstrated, in less than a decade, the prevalence of this serious
psychological and social problem have gone up dramatically, with no apparent signs of
slowing down in the foreseeable future.

Appendix 1

DSM-IV-TR Diagnostic Criteria for Pathological Gambling (312.31)

Diagnostic Criteria:

Persistent and recurrent maladaptive gambling behavior as indicated by at least five of the
following:
1. is preoccupied with gambling (e.g., preoccupied with reliving past gambling
experiences, handicapping or planning the next venture, or thinking of ways to get
money with which to gamble)
2. needs to gamble with increasing amounts of money in order to achieve the desired
excitement
3. has repeated unsuccessful efforts to control, cut back, or stop gambling
4. is restless or irritable when attempting to cut down or stop gambling
5. gambles as a way of escaping from problems or of relieving a dysphoric mood (e.g.,
feelings of helplessness, guilt, anxiety, depression)
6. after losing money gambling, often returns another day in order to get even
(‘‘chasing’’ one’s losses)
7. lies to family members, therapist, or others to conceal the extent of involvement with
gambling
8. has committed illegal acts, such as forgery, fraud theft, or embezzlement, in order to
finance gambling
9. has jeopardized or lost a significant relationship, job, or educational career
opportunity because of gambling
10. relies on others to provide money to relieve a desperate financial situation caused by
gambling

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Differential diagnosis
Distinct from
• Social and professional gambling
• Gambling in the context of a manic episode
• Problems with gambling in antisocial personality disorder

ICD-10 Diagnostic Criteria for Pathological Gambling (F63.0)

The disorder consists of frequent, repeated episodes of gambling that dominate the patient’s
life to the detriment of social, occupational, material, and family values and commitments.

Diagnostic Criteria:

a. Repeated (two or more) episodes of gambling over a period of at least 1 year.

b. These episodes do not have a profitable outcome for the person, but are continued
despite personal distress and interference with personal functioning in daily living.
c. The person describes an intense urge to gamble which is difficult to control, and
reports that he or she is unable to stop gambling by an effort of will.
d. The person is preoccupied with thoughts or mental images of the act of gambling or
the circumstances surrounding the act.

Exclusions:

• Excessive gambling by manic patients (F30)

• Gambling and betting not otherwise specified (Z72.6)
• Gambling in dissocial personality disorder (F60.2)

Appendix 2

College Gambling Resources

1. BetCheck: www.responsiblegambling.org/betcheck/
(a) Online tool that allows gamblers to assess their risk by answering their questions

2. Self Help Handbook for Problem Gambling: www.problemgamblingvictoria.ca/handbook/

handbook_toc.shtm
(a) This book is for adult gamblers who would like to address their problem
gambling individually.

3. Gambling Decisions: www.gamingresearch.blogspot.com/2006/12/gambling-decisions

program-news.html
(a) Six-week program to help individuals abstain or control gambling

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4. YMCA Youth Gambling Project: www.peterboroughymca.org/programs/ygp.htm

(a) Gambling program for youth 8–24
5. Association of Problem Gambling Services Administrators: www.camh.net/egambling/
pdf/jgi_15_christensen.pdf

(a) A group that initiates collaboration among states for problem gambling services

6. National Council on Problem Gambling: www.ncpgambling.org

(a) National organization that provides information on problem and pathological
gambling including clinician gambling certification

7. Responsible Gaming Council: www.responsiblegambling.org/en/index.cfm

(a) Website of a non-profit organization that address problem gambling preventions

8. Gambler’s Anonymous: www.gamblersanonymous.org

(a) Organizational website that provides lists of meetings and resources for those
seeking assistance for pathological gambling.

9. Don’t Bet on It: www.dontbetonit.org

(a) Interactive resource for college athletes, developed by NCAA

10. Campus Blues: www.campusblues.com/gambling.asp

(a) This site provides information on gambling on American campuses

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