gambling in alberta - Research Services - University of Lethbridge

and health practices’, and b) the 2008 and 2009 surveys where problem gambling questions
were only administered to people who reported a minimal amount of gambling involvement
(e.g., 1/month on some form). In general, research indicates that these latter procedures
produce more accurate rates of problem gambling (Williams & Volberg, 2009, 2010) (thus,
surveys in other years are producing rates that are slightly higher than ‘true’ rates).
Three different problem gambling assessment instruments have been used in the Alberta
population surveys: the South Oaks Gambling Scale (using a past year time frame) (Lesieur &
Blume, 1987), the Canadian Problem Gambling Index (Ferris & Wynne, 2001), and the Problem
and Pathological Gambling Measure (PPGM) (see Appendix E) (Williams & Volberg, 2010).
The PPGM is a relatively new instrument that has been tested and refined over the past 8 years
by Dr. Robert Williams (unpublished data). It has 4 categories of Recreational Gambler, At Risk
Gambler, Problem Gambler, and Pathological Gambler (with the latter two groups being
collectively identified as ‘problem gamblers’). In a large scale validation study involving a
sample of 7,273 individuals from 105 countries (including 977 clinically assessed problem
gamblers), the PPGM evidenced good internal consistency (Cronbach’s alpha = .81), as well as
good concurrent validity by virtue of its significant correlation with scores on other problem
gambling instruments (Pearson correlation of .75 with the SOGS, .70 with the CPGI, and .82
with the NODS) (Williams & Volberg, 2010). Most importantly, however, the
problem/nonproblem categorizations of the PPGM have considerably higher correspondence to
clinically assessed problem/nonproblem categorizations than either the CPGI, SOGS, or NODS
(all 4 instruments were administered to these 7,273 individuals). 72 The sensitivity of the PPGM
is 99.7%, specificity is 98.9%, positive predictive power is 93.5%, negative predictive power is
99.9%, overall diagnostic efficiency is 99.0%, and the ratio of instrument identified problem
gamblers relative to clinically assessed problem gamblers is 1.07 (Williams & Volberg, 2010). 73
This strong association with clinically assessed problem gambling is largely due to the PPGM’s
comprehensive assessment of all potential harms of gambling, the fact that designation as a
problem gambler requires evidence of gambling-related harm plus evidence of impaired control
(to better correspond to the most commonly accepted definition of problem gambling, Neal,
Delfabbro, & O’Neil, 2005), and assessment procedures that significantly reduce the presence
of both false positives and false negatives (see scoring details of the PPGM in Appendix E for
further details). Also, unlike other instruments, the classification accuracy of the PPGM is
unaffected by the age, gender, and ethnic origins of the sample (Williams & Volberg, 2010).
72 Clinicians were asked to ascertain the presence or absence of features that would classify the person as a
problem or nonproblem gambler as defined by the definition put forward by Neal, Delfabbro, & O’Neil (2005).
73 By comparison, the CPGI 3+ has 91.2% sensitivity, 85.5% specificity, 49.4% positive predictive power, 98.4%
negative predictive power, 86.3% diagnostic efficiency, and a ratio of instrument assessed PG prevalence over
clinically assessed PG prevalence of 1.9. The traditional CPGI 8+ threshold for problem gambling has a 44.4%
sensitivity, 99.2% specificity, 89.9% positive predictive power, 92.0% negative predictive power, 91.9% diagnostic
efficiency, and a ratio of instrument assessed PG prevalence over clinically assessed PG prevalence of 0.49.
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