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While the financial and psychological burden on problem gamblers can be severe, at least some of the ill effects are also passed on to family or other close social ties. The present study estimated the number of affected-others for the typical problem gambler. Australian members of an online panel with gambling problems (N = 3076) and panel members who indicated that they had been affected by someone else’s gambling (N = 2129) were asked to estimate the number of other people who were negatively affected by their gambling. Using robust statistics to analyse this data, the study found lower estimates made by problem gamblers (four affected people) compared to estimates made by affected others (six affected people, including the respondent). It was concluded that a point-estimate of six people affected is a more accurate figure since it does not suffer from self-presentation effects of problem gamblers. Low-risk and moderate-risk gamblers, unsurprisingly, affected far fewer other people (one and three, respectively). Both gamblers and affected-others most often identified close family members, including spouses and children, as the people impacted by others’ gambling problems. These results provide an approximate measure of the number of people affected, per problem gambler, to facilitate accurate accounting of the harms accruing from gambling problems.
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International Gambling Studies
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A typical problem gambler affects six others
Belinda C. Goodwin, Matthew Browne, Matthew Rockloff & Judy Rose
To cite this article: Belinda C. Goodwin, Matthew Browne, Matthew Rockloff & Judy Rose (2017)
A typical problem gambler affects six others, International Gambling Studies, 17:2, 276-289, DOI:
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Published online: 07 Jun 2017.
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VOL. 17, NO. 2, 276289
A typical problem gambler aects six others
Belinda C. Goodwina, MatthewBrownea, MatthewRockloa and Judy Roseb
aSchool of Human, Health, and Social Sciences, Central Queensland University (CQU), Branyan, Australia;
bSchool of Social Science, University of Queensland, Brisbane, Australia
While the nancial and psychological burden on problem gamblers
can be severe, at least some of the ill eects are also passed on to family
or other close social ties. The present study estimated the number of
aected-others for the typical problem gambler. Australian members
of an online panel with gambling problems (N = 3076) and panel
members who indicated that they had been aected by someone
else’s gambling (N = 2129) were asked to estimate the number of
other people who were negatively aected by their gambling. Using
robust statistics to analyse this data, the study found lower estimates
made by problem gamblers (four aected people) compared to
estimates made by aected others (six aected people, including the
respondent). It was concluded that a point-estimate of six people
aected is a more accurate gure since it does not suer from self-
presentation eects of problem gamblers. Low-risk and moderate-risk
gamblers, unsurprisingly, aected far fewer other people (one and
three, respectively). Both gamblers and aected-others most often
identied close family members, including spouses and children, as
the people impacted by others’ gambling problems. These results
provide an approximate measure of the number of people aected,
per problem gambler, to facilitate accurate accounting of the harms
accruing from gambling problems.
In evaluating the harm associated with problematic gambling behaviour, it is important
to consider how ‘aected others’ – including spouses, children, friends and associates of
the gambler – might be negatively impacted (Korn, Gibbins, & Azmier, 2003). Research
in various international settings has revealed the extent to which close friends and family
can be impacted by others’ gambling (Dowling, Rodda, Lubman, & Jackson, 2014; Ferland
et al., 2008; Hing, Tiyce, Holdsworth, & Nuske, 2013; Holdsworth, Nuske, Tiyce, & Hing,
2013; McComb, Lee, & Sprenkle, 2009; Orford, Templeton, Velleman, & Copello, 2005;
Salonen, Castrén, Alho, & Lahti, 2014; Wenzel, Øren, & Bakken, 2008). For example, a recent
cross-sectional Norwegian study found that concerned signicant others (CSOs) reported
elevated conict, nancial detriment and impaired mental and physical health as a result of
a partner’s gambling (Wenzel et al., 2008). Longitudinal evidence from a Swedish population
Gambling harms; gamblers;
affected others; PGSI;
problem gambling; family
Received 26 August 2016
Accepted9 May 2017
© 2017 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Belinda C. Goodwin
suggests that CSOs of gamblers experience poor mental health, risky alcohol use, nancial
hardship and strained relationships, although causality could not be established. Similar
ndings are true for Australian and Canadian samples, where problem gamblers’ spouses
typically report decrements to nancial security, social activity, emotional and physical
health, and family interaction (Ferland et al., 2008; Hing et al., 2013; Holdsworth et al., 2013).
ese issues are accompanied by increased problems at work, personal debt and marital
problems, as well as drug and alcohol use (Ferland et al., 2008; Hing et al., 2013; Holdsworth
et al., 2013). Negative physical and mental health outcomes are also reported for children of
gamblers, who may experience neglect due to diminished parental care or lack of resources
(Darbyshire, Oster, & Carrig, 2001; Shaw, Forbush, Schlinder, Rosenman, & Black, 2007).
Furthermore, nancial insecurity associated with ongoing gambling by parents can aect
more than one generation (Darbyshire et al., 2001), and can also aect extended relatives
and friends, and extend to the wider community (Clarke, Abbott, DeSouza, & Bellringer,
2007; Hing et al., 2013; Kalischuk, Nowatzki, Cardwell, Klein, & Solowoniuk, 2006).
ese ndings, largely derived from qualitative research, describe the experience of
gambling-related harm from the perspective of aected others, and highlight the way in
which harm is not limited only to those in close proximity to the gambler, but also aects
extended familial, social and community networks. Given the impact of problem gam-
bling on others, it is not surprising that eorts to quantify the social cost of gambling have
attempted to include this aspect of harm (Centre for Social & Health Outcomes Research
& Evaluation, 2008; Productivity Commission, 2010). For these calculations to be correct,
however, it is critically important to employ a reasonable estimate of the mean number of
aected others for every problem gambler. It is also helpful to know what demographic
groups are most aected by the gambling of others.
An Australian Productivity Commission report (1999) suggested that each gambler in
Australia aects between 5 and 10 other individuals. is gure has been cited oen, both
in the literature (Banks, 2007; Hinchlie, 2008; Leung, Wong, Lau, & Yeung, 2010) and in
non-academic communications (e.g. Responsible Gambling Fund Trustees, 2007); however,
no empirical evidence has been oered in support. A similarly non-precise estimate of ‘at
least 10 people’ has been attributed to Ladouceur (1993, as cited in Ferland et al., 2008).
Several population-based studies have estimated the incidence of others aected by gam-
bling. For example, Scandinavian studies, using a range of dierent measures, suggested
that between 2% and 19% of the population are gambling-related CSOs (Wenzel et al., 2008;
Salonen et al., 2014; Svensson, Romild, & Shepherdson, 2013). Abbott, Bellringer, Garrett,
and Mundy-McPherson (2014) estimated that 8% of New Zealanders were aected by the
gambling of someone close to them. Given that the prevalence of problem gambling in New
Zealand is estimated to be less than 1% (Devlin & Walton, 2012), their gure might be taken
to imply approximately eight CSOs per problem gambler, but this would ignore those who
are aected by individuals in Problem Gambling Severity Index (PGSI; Ferris & Wynne,
2001) categories other than ‘high risk/problem-gambling’. e varying estimates between
countries may be due to dierences in each survey’s wording and the terminologies used,
rather than an indication of international heterogeneity. For example, the term ‘concerned
signicant other’ infers a close family member (most likely a partner) who is showing con-
cern whereas the term ‘aected other’ does not imply relationship status or level of concern.
Study aims
Determining an estimate of the number of people aected by a typical problem gambler is
of signicant practical importance, particularly in understanding the aggregate harm caused
by problem gambling. Previous research has provided estimates of the proportion of people
aected by gambling (most commonly CSOs); however, to date no studies have provided a
precise estimate of the amount of people aected per gambler. is article presents ndings
from direct questioning of gamblers and aected others in terms of how many people are
inuenced by problem gambling harms, with the aim of providing a precise estimate of
the number and type of people aected per each low-, moderate- and high-risk gambler
according to the respective PGSI categories.
Limited research has shown that relationship status and type of gambling activity might be
associated with more negative impact from another’s gambling (Dowling, Rodda, Lubman,
& Jackson, 2014), but the likelihood of being an aected other based on these characteristics
has not been directly assessed. e current study also aims to identify the most aected
people (e.g. spouses, children, co-workers, etc.) and the types of gambling activities that
are impacting on the greatest number of aected others. is is important knowledge for
targeting intervention eorts for aected others towards the most vulnerable groups and
most risky products.
Participants and procedure
We analysed data gathered as part of a large-scale survey of gambling-related harm to
gamblers and aected others (Browne et al., 2016). Research participants were invited
via email by a commercial online panel provider in Australia. Participants were compen-
sated for their time by points that could be accumulated and exchanged with the agency
for cash. e sample comprised 5205 participants (45.3% male) ranging from 18 to 89
years of age (M = 46.96, SD = 15.18) who reported either (a) that their own gambling
had caused them some degree of problems at some point in their lives (n = 3076), or (b)
having had a close relationship with a person whose gambling had caused them prob-
lems at some point in their lives (n = 2129). Participants were recruited in two phases. In
the rst phase, participants were rst asked ‘Has there been a time when your gambling
has caused problems in your life, no matter how minor?’ ose who answered ‘yes’ were
directed to a survey for gamblers. If they answered ‘no, participants were asked ‘Have you
had a close relationship with a person whose gambling has caused problems in your life, no
matter how minor?’ In the second phase of recruitment the question order was reversed
and participants were preferentially directed to the aected others survey (see Appendix A
for a visual representation of the process). All participants were Australian residents, with
the majority (80.5%) born in Australia. Age, gender, income, education level and country
of birth are described separately for each group in Appendix B. Ethical approval for the
research was granted by the university’s internal review board and participants provided
informed consent before participating.
At the outset of the survey, participants were directed to consider a 12-month period in
their life when gambling had been causing them the most problems. Questions were then
phrased retrospectively with respect to this period. irty-three percent of participants
reported this period to be in the most recent 12 months. As part of the larger research pro-
ject, participants also completed a checklist of gambling-related harms that had occurred
to them during this time (see Appendix C). Age, gender and relationship with gambler/
aected other was recorded for all participants as well as the preferred form of gambling
of the gambler.
Amount of aected others
Gamblers were asked to consider the checklist of gambling-related harms (Appendix C)
and report the number of people whom they believed had been aected by their gambling.
Aected others were asked to report the number of people whom they believed had been
aected by one gambler.1 Item wording was as follows: ‘Considering all the issues raised
earlier, how many other people would you estimate were aected by your gambling during
this period of time?’ (gambler) and ‘Considering all the issues raised earlier, how many other
people would you estimate were aected by this persons gambling during this period of time?
(aected other).
Problem gambling status
Gamblers responded to nine items on the Problem Gambling Severity Index (PGSI)
designed to measure problem gambling in the general population (Ferris & Wynne, 2001).
All items began with ‘At this time’ (this replaced the typical text ‘In the last 12 months’
for some items) to reect retrospective responding. For aected others, the PGSI was also
modied for second-person responding; that is, to describe the problem gambling status
of a gambler, rather than themselves (e.g. ‘At this time, did you feel that the person bet more
than they could really aord to lose?’). A detailed evaluation of the psychometric validity of
these modications has been reported elsewhere (Browne et al., 2016). In brief, as described
in Browne et al. (2016), the PGSI was shown to have measurement invariance for recent
retrospective reporting, and for reporting by self and aected others. One exception was
that self-reporters tended to provide lower mean scores than aected others – presumably
due to a tendency to minimize the negative impact of their behaviour. Cronbachs alpha
values were α==.90 and α= =.78 for the rst and second-hand reported PGSI measures
respectively. Based on summed PGSI score, participants and gamblers nominated by aected
others were categorized as ‘low-risk’ (PGSI = 1–2), ‘moderate-risk’ (PGSI = 3–7) or ‘prob-
lem-gambler’ (PGSI>7) according to Ferris & Wynne (2001).2
Descriptive analyses were conducted to examine the characteristics of those aected by
others’ gambling. In designing the main analysis, we assumed that variability in the number
of aected others reported could be aected by several factors:
(1) PGSI category of the gambler. More severe gambling problems should be related
to an increased number of aected others.
(2) First-hand versus second-hand reporting. We expected that self-reporters may be
more likely to minimize the impact of their gambling on others due to self-serving/
presentation bias (Greenberg, Pyszczynski, & Solomon, 1981/1982). Additionally,
aected others are a censored sample, in that at least one person (the respondent)
must have been aected in order for them to be eligible to complete the survey.
(3) Natural variation within PGSI category, including familial structure, and size of
social network.
(4) Individual dierences in response frame. Respondents would be expected to vary
with regard to an implicit threshold of what it means to be ‘aected.
Because the response, a count of aected others, is bounded at zero, these sources of
variation have the potential to create an upward bias in the calculation of a simple mean.
erefore, our estimates and uncertainty estimates are based on bootstrapped trimmed
means using a stringent threshold for excluding extreme cases, with 25% of extreme values
excluded. Analyses were conducted using standard function in the R statistical program-
ming environment (R Core Team, 2014).
e (25% trimmed) mean number of aected others was compared between PGSI status
for both the self-report and reports of the aected others. As shown in Figure 1, problem
gamblers reported the highest number of total aected others (self-report M =3.65; reported
by others M = 5.88), followed by moderate risk (self-report M = 0.73; reported by others M =
3.20) and low risk gamblers (self-report M = 0.03; reported by others M = 1.51). us, there
Figure 1. 25% trimmed means and bootstrapped 95% confidence intervals by gambler status and
reporting group.
was a consistent discrepancy between reporting by aected others and gamblers, whereby
aected others estimated a greater number of people aected by the gamblers’ behaviour.
Table 1 details the relationship status and preferred product of gamblers who aected
others. e table also shows the percentage of gamblers who reported aecting at least one
spouse (or partner), close friend, parent, sibling, child, other family member or colleague/
co-worker. As shown in Table 2, almost half of the aected others’ sample were aected by
someone who primarily played electronic gaming machines (EGMs) (47.5%), followed by
race bettors (23.5%). Aected others were most oen spouses (38.0%), children (19.2%)
and close friends (14.8%). In support of these direct results from aected others, over half
(51.7%) of gamblers who reported aecting at least one other person indicated they had
aected a spouse or a partner. Children (19.2%), close friends (18.9%) and parents (19.2%)
were also commonly reported to have been aected by the gamblers in their self-reports.
We analysed the potential impact of demographic and gambling status variables on the
number of aected others reported. Given that the response is a non-negative integer count,
and subject to overdispersion, we applied negative-binomial count multiple regression with
a log link. While this model specication is well suited to handle to a reasonable degree
of overdispersion, very large outliers may still create problems with estimation. erefore
we tested the model using outlier rejection thresholds, with counts of greater than 10, 20
and 30 being excluded. Estimated beta coecients appeared stable across these dierent
thresholds. Table 3 presents beta coecients and standard errors for the model for cases
reporting 30 or fewer aected others (N = 4520). e number of aected others reported
Table 1.Relationship statuses reported by gamblers affected others.
^Gamblers could select more than one relationship that is affected.
N (%)
Gambler^(n = 2076) Aected other(n = 2069)
The person is my …
Spouse, de facto or romantic partner 1589 (51.7) 809 (38.0)
Son/daughter 590 (19.2) 408 (19.2)
Close friend 580 (18.9) 315 (14.8)
Other family 387 (12.6) 264 (12.4)
Sibling 289 (9.4) 142 (6.7)
Colleague/co-worker 289 (9.4) 73 (3.4)
Parent 573 (18.6) 58 (2.7)
Other N/A 60 (2.8)
Table 2.Preferred product of gamblers reported by gamblers and affected others.
^Only gamblers who reported affecting at least one other person included.
N (%)
Gambler^(n = 2120) Aected other(n = 2129)
The gambler’s preferred product is …
Electronic gaming machines (EGM) 1094 (51.6) 1014 (47.6)
Race betting 325 (15.3) 501 (23.5)
Casino table games 141 (6.7) 146 (6.9)
Sports betting 236 (11.1) 134 (6.3)
Poker 125 (5.9) 123 (5.8)
Lottery 172 (8.1) 63 (3.0)
Keno 27 (1.3) 19 (0.9)
Don’t know N/A 129 (6.1)
signicantly increased by problem gambler category, and when the nominating party was
an aected other (rather than a gambler). Controlling for gambling risk-status, sports and
racing gamblers aected signicantly more people than EGM players, while keno and lotto
players aected fewer people than EGM players. When controlling for gambling character-
istics, gamblers who were older and female tended to aect fewer others. However, when
the reporting was done by an aected other, those respondents who were older and female
tended to report more people were aected.
e current study aimed to estimate the typical number of people aected by a problem
gambler, and to identify the most aected people and the types of gambling activities that
have the most impact. Although gures on the typical number are oen quoted in the lit-
erature, to our knowledge this is the rst study to directly investigate this point estimate. A
key feature of the study is that we surveyed both gamblers and aected others.
Best estimates for the number of aected others
We found that a typical problem gambler reported aecting about four others, whereas
those who were aected by a problem gambler on average estimated this gure to be six –
including themselves. As mentioned above, this discrepancy is probably primarily due to
(a) under-reporting by gamblers due to the self-presentation bias that is more common in
self-report data compared to data reported by others (Nederhof, 1985), and (b) censoring of
the sample of aected others. Again, censoring bias occurs because the survey response of
Table 3.Negative binomial regression predicting number of affected others reported.
*p<0.05; **p<0.01; ***p<0.005.
Dependent variable:
Count # Aected
Age gambler −0.010*** (0.001)
Affected other reporting 0.222 (0.129)
Female gambler −0.117*** (0.038)
PGSI low risk −0.565*** (0.125)
PGSI moderate risk 0.384*** (0.084)
PGSI problem gambler 1.035*** (0.079)
Preferred gambling activity (EGM vs.)
Sports 0.105* (0.052)
Race 0.114*** (0.036)
Poker 0.081 (0.063)
Casino −0.050 (0.056)
Keno −0.269* (0.138)
Lotto −0.176*** (0.059)
Other −0.004 (0.077)
Age (Affected other) 0.006*** (0.002)
Female (Affected other) 0.116* (0.056)
Intercept 0.704*** (0.119)
Observations 4520
Log likelihood −10,114.200
theta 2.219*** (0.086)
Akaike Inf. Crit. 20,260.410
the ‘aected other’ necessarily includes the respondent themselves as ‘one’ of those aected.
With respect to censoring, this bias is likely to be signicant (e.g. between 0.5 and 1.0) in
the case of the low-risk category, where it is plausible that a large proportion of associated
gamblers truly do not aect even one person. However, for problem gamblers, it is likely
that only a negligible proportion of gamblers truly do not aect anybody, and therefore the
censorship bias is likely to be slight. With respect to under-reporting by gamblers, there are
multiple psychological explanations for the minimization of self-reported impact on others,
such as common tendency to present oneself in a positive light (Greenberg et al., 1981/1982),
attribute negative outcomes to external forces (Rotter, 1966), or positive memory biases
(Walker, Skowronski, & ompson, 2003). erefore, our interpretation is that the gure of
six aected others per problem gambler is the most valid since it is least aected by under-re-
porting. is is within, but at the lower end of, the range of gures commonly quoted
(Ferland et al., 2008; Productivity Commission, 1999). Taking into account censoring, and
therefore rounding-down our point-estimates, we conclude that a typical moderate-risk
gambler aects about three people, and a low-risk gambler aects one person. at is,
both of these latter gures are on the low side of our estimated ranges to account for the
attenuating eects of censoring. Our separate estimates for number of aected others per
gambler at each level of PGSI risk is useful as it can be weighted according to the specic
population prevalence statistics for low-, moderate- and high-risk (problem) gamblers to
produce accurate estimates of total aected others in the population, and potentially be
applied to international settings.
Who is most likely to be harmed by another person’s gambling?
In terms of demographic characteristics of aected others, current ndings were similar
to those from previous research. For example, Dowling et al. (2014) found that over 60%
of aected others seeking counselling were the spouse or partner of a problem gambler,
and almost 20% were the children of gamblers. is suggests people who live in the closest
proximity and are dependent nancially and emotionally on a problem gambler are most
likely to be aected by their behaviour.
What gambling games are most likely to harm aected others?
Dowling et al. (2014) also reported that over 40% of the concerned signicant others in
their sample were related to gamblers who were primarily EGM players. In the current
study almost half of the aected others’ sample fell into this category. is likely reects the
high proportion of gamblers who play EGMs. EGMs feature a combination of risky struc-
tural characteristics such as rapid playing speeds and payout intervals, multiplier potential,
reinforcing payout schedules, and attractive audiovisual eects. ese features are more
amenable to risky and problematic play than many other gambling products (Blaszczynski,
Walker, & Sharpe, 2001; Jackson, omas, omason, Holt, & McCormack, 2000; Smith
& Wynne, 2004); therefore we might also expect that EGM players are more likely than
other gamblers to export gambling-associated harms to others (Breen & Zimmerman, 2002;
Doughney, 2002).
Sports and race betting were associated with greater (gambler-reported) estimates for
aected others harmed, whereas lotto and keno were conversely associated with lower
estimates (see Table 3). Both sports and race-betting are dominated by male gamblers,
whereas lotto and keno attract proportionately more female gamblers. Given the relative
rarity of female sports and race bettors, it is dicult to determine whether gender or type
of game dominates in our analysis. Future research may illuminate whether the type of
preferred games or alternatively gambler-demographic factors are more inuential in deter-
mining the dispersion and severity of harm that impacts aected others. Nevertheless, the
nding that certain games, such as EGMs, sports and race-betting, are associated with a
greater number of others being harmed is important in estimating population-level harm.
ese games not only cause harm to problem gamblers, but also export harm to a greater
number of aected others, magnifying their eects on the whole community. Moreover,
although we expect most western countries are likely to have broadly similar numbers of
aected others to those estimated in this article, jurisdictions outside Australia may have
fewer or more aected others for every gambler based on dierent mixes of preferred
gambling products.
Any consideration of a numeric gure depends heavily on the threshold one uses to dene
being ‘aected’. Our operational denition of ‘aected’ is the occurrence of at least one of
the items on the gambling harms checklist presented in Appendix C which represents a
variety of nancial, relationship, work- or study-related, emotional and health-related harms
experienced by gamblers (Browne et al., 2016). e downside of this approach is that we are
unable to estimate the degree to which individuals, other than the participants themselves,
are aected by the gambler.
In addition to the potential bias caused by gamblers under-reporting harms, it must
also be acknowledged that aected others’ reports may also be subject to similar issues. It
is dicult, however, to predict whether aected others are susceptible to under-reporting
harms due to lack of knowledge of the full extent of the gambles impact on others, or to
overestimating the proportion of harm due to negative emotions regarding the person with
the behaviour. Given that judging the quantity and extent of harm is intrinsically subjective,
there is not an obvious solution to this issue. Nevertheless, the dual perspectives presented
in the present study go some way to addressing uncertainty due to reporting bias.
Finally, recruitment was done through an online panel, which is not representative of
the general population of gamblers, and in which problem gamblers are over-represented.
However, our results are provided with respect to, or control for, gambler risk category.
Although an eort was made to allocate aected others and gamblers to their respective
surveys in an unbiased manner (see Appendix A), the smaller amount of participants taking
part in the second phase of recruitment meant that gamblers who also identied as aected
others may have been slightly under-represented overall.
Accurate understanding of how many aected others are impacted by gambling problems,
as well as a better understanding of who is aected, is helpful in eorts to reduce com-
munity harm. e costs of problem gambling are not limited to the immediate eects on
the nancial and emotional well-being of the problem gambler, but also extend to people
intimately connected to the gambler through family and other social ties. ese connec-
tions must be considered to understand the wider costs of problem gambling, and provide
a foundational knowledge for the investment in appropriate interventions. is research
provides an important rst step in accounting for who and how many are aected by gam-
bling problems. Interventions in the UK and US have successfully assisted aected others
in dealing with the consequences of others’ problem gambling (GamCare, 2003; Winters,
Benston, & Stincheld, 1996). In the future, it may be possible to tailor such campaigns
to provide the most relevant assistance and advice to the people most at risk and to those
posing the most risk to others.
Conict of interest
Funding sources
is work was supported by the Victorian Responsible Gambling Foundation (VRGF)
under Grant No. VRGF 1–13.
Competing interests
e authors have no competing interests to declare. e VRGF had no involvement in the
research design, methodology, conduct, analysis or write-up.
Constraints on publishing
Some results and methodology from this manuscript form part of the research report written
for the VRGF which underwent their review. No changes were made as a result of this review.
1. In calculating total aected others, the respondent was included by adding 1 to each response.
2. Non-gamblers (PGSI = 0) were categorized as low risk as all study targets had experienced
and/or caused some form of gambling harm and very few participants recorded a PGSI score
of zero (n = 140).
Notes on contributors
Belinda C. Goodwin is a PhD candidate, casual lecturer and research worker for the Experimental
Gambling Research Laboratory (EGRL) at Central Queensland University (CQU). She has a strong
background in gambling and addictive behaviour research.
Matthew Browne is a senior lecturer and a senior researcher in the EGRL. He is an expert statistician
and has been chief or co-investigator on several large-scale, prominent gambling-related research
projects since 2009.
Matthew Rocklo leads the EGRL and serves as the head of the Population Research Laboratory
(PRL) at CQU. He has a long history of successfully managing large-scale, prominent gambling-re-
lated research projects.
Judy Rose has completed a Bachelor of Arts, a Masters in applied linguistics and a PhD in social
science. She is a key member of the ERGL and has extensive research experience.
Matthew Browne
Matthew Rocklo
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Appendix A: Recruitment process
Appendix Figure 1. Two-phase participant recruitment process.
Phase 1: Phase 2:
N = 5597
Has there been a time
when your gambling
has caused problems
in your life, no matter
how minor?
Yes No
survey for
N = 2458
Have you had a close
relationship with a
person whose
gambling has caused
problems in your life,
no matter how
Yes No
survey for
N = 1678
N = 1461
N = 1294
Have you had a close
relationship with a person
whose gambling has caused
problems in your life, no
matter how minor?
Yes No
survey for
N= 451
Has there been a time
when your gambling
has caused problems
in your life, no matter
how minor?
Yes No
survey for
Appendix B: Analysis of group dierences
Appendix B Table 1 details the gender, age, country of birth, education and income dierences
between the participants in the gamblers group versus those in the aected others group. A small
signicant eect was found for gender (r = .25) with more males appearing in the gamblers group
(55.7%) than in the aected others group (30.2%). Some very weak associations were also found for
tertiary education and income level, with slightly higher income earners in the gamblers group (r =
-.05) and slightly more tertiary educated participants in the aected others group (r=-.04).
Appendix B Table 1. Breakdown of gender, age, country of birth, education and income according
to group.
Group A Group B Pearson’s r
(Gamblers) (Aected others)
Male 55.7% 30.2% .25*
Age 45.05 (14.98) 44.83 (15.48) −.01
Australian born 79.9% 81.3% −.02
Tertiary educated 46.7% 51.0% .04*
Personal income level^ 6.30 (2.87) 5.98 (2.97) −.05*
* = p < .05. Categorical variables displayed as proportion of group. Means and SDs displayed for continuous variables. ^
Income level was recorded in even brackets Level 6 (the mean for both Group A & B) represents the ‘$31,200–$41,599 per
year’ bracket.
Appendix C: 2015 Victorian harms checklist
Participants were asked ‘Have you experienced any of these issues as a result of your gambling in the
last 12 months?’ and were presented with a checklist of harms that covered the following six domains.
For full list of items, see Browne et al. (2016).
e.g. Reduction of my savings, Less spending on recreational expenses such as eating out, going to the
movies or other entertainment.
e.g. Spent less time with people I care about, Neglected my relationship responsibilities.
e.g. Felt distressed about my gambling, Felt insecure or vulnerable.
e.g. Reduced physical activity due to my gambling, Neglected my hygiene and self-care.
e.g. Was late for work or study, Used my work or study time to gamble.
e.g. Le children unsupervised, Felt compelled or forced to commit a crime or steal to fund gambling
or pay debts.
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Excessive gambling negatively impacts the lives of gamblers’ spouses. This study investigated stressful life events, perceived stress, coping resources and health risks of 10 spouses recruited from two treatment facilities. In semi-structured interviews, the Social Readjustment Rating Scale identified life events encountered in the past year. The 12-item General Health Questionnaire assessed psychiatric impairment of the participants who also answered questions on physical symptoms and emotional problems. Open-ended questions gathered information on coping resources. The interviews were audio-taped and transcribed for thematic analysis. Replicating previous research results, the spouses disclosed marital, social, financial and vocational stresses related to gamblers’ disordered gambling. The majority utilized both formal and informal help, internal and external coping resources to deal with the adverse circumstances. Many reported significant symptoms of physical, emotional and mental health problems. Perceived stress, but not the number of negative life events, correlated significantly with physical and psychiatric symptoms. Only positive beliefs as a type of coping resources were inversely correlated with pathology. The study findings have implications for interventions and future research.
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Problem gambling not only impacts those directly involved, but also the concerned significant others (CSOs) of problem gamblers. The aims of this study were to investigate the proportion of male and female CSOs at the population level; to investigate who the CSOs were concerned about; and to investigate sociodemographic factors, gender differences, gambling behaviour, and health and well-being among CSOs and non-CSOs. The data (n = 4484) were based on a cross-sectional population study. Structured telephone interviews were conducted in 2011-2012. The data were weighted based on age, gender and residency. The respondents were defined as CSOs if they reported that at least one of their significant others (father, mother, sister/brother, grandparent, spouse, own child/children, close friend) had had gambling problems. Statistical significance was determined by chi-squared and Fisher's exact tests, and logistic regression analysis. Altogether, 19.3% of the respondents were identified as CSOs. Most commonly, the problem gambler was a close friend (12.4%) of the CSO. The percentage of close friends having a gambling problem was larger among male CSOs (14.4%) compared with female CSOs (10.3%; p <= 0.001), while the percentage of partners with gambling problem was larger among females (2.6%) than among males (0.8%; p <= 0.001). In the best fitting model, the odds ratio (95% CI) of being a male CSO was 2.03 (1.24-3.31) for past-year gambling problems, 1.46 (1.08-1.97) for loneliness and 1.78 (1.38-2.29) for risky alcohol consumption. The odds ratio (95% CI) of being a female CSO was 1.51 (1.09-2.08) for past-year gambling involvement, 3.05 (1.18-7.90) for past-year gambling problems, 2.21 (1.24-3.93) for mental health problems, 1.39 (1.03-1.89) for loneliness and 1.97 (1.43-2.71) for daily smoking. CSOs of problem gamblers often experience cumulating problems such as their own risky gambling behaviour, health problems and other addictive disorders. The clearest gender difference was seen in smoking by CSO. In order to develop efficient and targeted support and services for CSOs, it is necessary to understand the correlates related to different subgroups of CSOs.
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Research into the impact of problem gambling on close social networks is scarce with the majority of studies only including help-seeking populations. To date only one study has examined concerned significant others (CSOs) from an epidemiological perspective and it did not consider gender. The aim of this study is to examine the health, social support, and financial situations of CSOs in a Swedish representative sample and to examine gender differences. A population study was conducted in Sweden in 2008/09 (n = 15,000, response rate 63%). Respondents were defined as CSOs if they reported that someone close to them currently or previously had problems with gambling. The group of CSOs was further examined in a 1-year follow up (weighted response rate 74% from the 8,165 respondents in the original sample). Comparisons were also made between those defined as CSOs only at baseline (47.7%, n = 554) and those defined as CSOs at both time points. In total, 18.2% of the population were considered CSOs, with no difference between women and men. Male and female CSOs experienced, to a large extent, similar problems including poor mental health, risky alcohol consumption, economic hardship, and arguments with those closest to them. Female CSOs reported less social support than other women and male CSOs had more legal problems and were more afraid of losing their jobs than other men. One year on, several problems remained even if some improvements were found. Both male and female CSOs reported more negative life events in the 1 year follow-up. Although some relationships are unknown, including between the CSOs and the individuals with gambling problems and the causal relationships between being a CSO and the range of associated problems, the results of this study indicate that gambling problems not only affect the gambling individual and their immediate close family but also the wider social network. A large proportion of the population can be defined as a CSO, half of whom are men. While male and female CSOs share many common problems, there are gender differences which need to be considered in prevention and treatment.
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Partners can be especially vulnerable to the negative effects of gambling problems, but little research has sought to understand partners’ experiences from their own unique perspectives. This qualitative interpretive study explored the impacts of gambling problems on partners. In-depth interviews were conducted with 18 partners and ex-partners of people with gambling problems to understand their experiences of gambling problems from their perspectives. The findings showed that partners experienced a wide range of negative effects, especially on their financial security, their emotional, mental and physical health, and on their relationships. The financial impacts of gambling problems on partners were substantial and far-reaching. Some partners were forced to take up extra employment to cover household expenses and pay off gambling-related debts. Others lost their savings, homes, belongings and established ways of life. While these impacts were extensive, partners also experienced a range of emotional impacts that were equally devastating. Their gambling partner’s lies, dishonesty and concealment of problems and gambling behaviour created considerable distress, loss of trust and a sense of betrayal. These experiences undermined these partners’ sense of self-identity, and created additional conflicts within their relationships. Along with accumulating mental and physical health impacts, these challenges lead to separation and/or divorce for many participants. These findings point to the need for greater understanding of partners’ experiences and public health initiatives that protect partners and their families from the harmful effects of gambling problems.
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Problem gambling can significantly affect the lives of concerned significant others (CSOs) of problem gamblers, especially family members, but little is known about their help-seeking activities and experiences. This paper explores help-seeking by CSOs of problem gamblers and their related motivators and barriers. A telephone interview was administered to 48 CSOs who called an Australian gambling helpline seeking assistance for themselves and/or a person with gambling problems. Key motivators for seeking help (through helplines, non-professional sources, and self-help measures) were concerns the gambling might become a major problem, negative emotions, problems maintaining normal daily activities, concerns for dependents’ welfare, and health concerns. Barriers included wanting to solve the problem on their own, and shame. Findings highlight the need to better equip CSOs to assist both the person with gambling problems towards treatment and recovery and to protect their own physical, emotional, social and financial wellbeing.
The 'concerned significant others' (CSOs) of people with problem gambling frequently seek professional support. However, there is surprisingly little research investigating the characteristics or help-seeking behaviour of these CSOs, particularly for web-based counselling. The aims of this study were to describe the characteristics of CSOs accessing the web-based counselling service (real time chat) offered by the Australian national gambling web-based counselling site, explore the most commonly reported CSO impacts using a new brief scale (the Problem Gambling Significant Other Impact Scale: PG-SOIS), and identify the factors associated with different types of CSO impact. The sample comprised all 366 CSOs accessing the service over a 21month period. The findings revealed that the CSOs were most often the intimate partners of problem gamblers and that they were most often females aged under 30years. All CSOs displayed a similar profile of impact, with emotional distress (97.5%) and impacts on the relationship (95.9%) reported to be the most commonly endorsed impacts, followed by impacts on social life (92.1%) and finances (91.3%). Impacts on employment (83.6%) and physical health (77.3%) were the least commonly endorsed. There were few significant differences in impacts between family members (children, partners, parents, and siblings), but friends consistently reported the lowest impact scores. Only prior counselling experience and Asian cultural background were consistently associated with higher CSO impacts. The findings can serve to inform the development of web-based interventions specifically designed for the CSOs of problem gamblers.
The Productivity Commission report, Australia’s Gambling Industries (1999), found that levels of problem gambling are of major concern. According to the report, around 330,000 (2.3 per cent) adult Australians have significant gambling problems, and for every one of these an additional 5 to 10 people are adversely affected in a direct way by their gambling. A vital question one is to ask is to what degree does a gambling venue owe a duty of care to its patrons? While cases such as Reynolds v. Katoomba RSL [2001] NSWCA 234; (2001) 53 NSWLR 43 have recognised that no such duty is owed, this article will discuss the extent to which Australian courts are beginning to recognise that a duty of care is owed by gaming premises.
Two New Zealand surveys were examined to assess the robustness and reliability of the Problem Gambling Severity Index (PGSI). The PGSI cohered to a single factor in both data sets and had high internal reliability. These features held when separately considering men, women, Māori, Pacific and Asian people. Positive associations were evident between the PGSI and gambling behaviour, accessing gambling intervention services, arguing about gambling, the burden of debt due to gambling, and the co-morbidity of smoking. A meta-analysis of the two surveys establishes a prevalence of .53%. When considering 36 overseas studies this figure is adjusted to .50%. These estimates are around 20% higher than that established by the largest NZ study and 25% lower than the latest study. The use of meta-analysis is recommended to obtain a timely and accurate estimate of the prevalence of problem gambling, especially when repeating a large sample survey is prohibitively expensive.