Promoting Resilience in the Context of
Risk: Applications of Resilience Theory to
Gambling in Two Samples of Youth
ABBY L. GOLDSTEIN, PH.D.
OISE, UNIVERSITY OF TORONTO
Adolescent Gambling
 Rates of gambling among youth rival those of alcohol
use
 US survey of 14-21 year olds (Welte, Barnes, Tidwell,
& Hoffman, 2008)


68% gambled in past year
11% more than twice per week
Adolescent Gambling
 Higher prevalence of pathological gambling among
adolescents than adults
 Early initiation of gambling associated with
problems in young adulthood, increased likelihood of
mental health concerns (Burge, Pietrzak, Molina, &
Petry, 2004)
Adolescent Gambling
 Significant research on risk correlates of gambling
 Alcohol use
 Tobacco use
 Other drug use
 Delinquency
 Peer violence
 Dating violence
Resilience Theory
 Framework for understanding how adolescents
adapt well, even with exposure to multiple risk
factors
 Accumulation of risk  increased likelihood of
unhealthy behaviours
 Promotive factors  reduce likelihood of negative
outcomes despite exposure to risk
Resilience Theory
 Promotive factors exert their effects in one of two
ways (Fergus & Zimmerman, 2005)
1)
2)

Compensatory – exert a direct effect in the context of risk
Interactive – moderate or weaken the impact of risk factors
Few studies have examined how risk and
promotive factors contribute to gambling in
adolescents (see Lussier, Derevensky, Gupta, Bergevin, &
Ellenbogen, 2007 for an exception)
Application of Resilience Theory to Gambling –
Youth in an Inner City ED
 Study explored the application of resilience theory to
gambling in a sample of adolescents presenting to an
inner city ED
 ED important context for screening and intervention
 Use of Latent Class Analysis (LCA) to identify
subgroups of gamblers
Study of Youth in ED
 Part of larger RCT of an alcohol and violence
intervention in the ED in Flint, MI
 Baseline sample consisted of 726 adolescents and
34.3% had gambled in the past year (N = 249)
 Among those who gambled


30.1% were female
59.4% were African American, 30.9% Caucasian
Measures
 Gambling items adapted from the OSDUS (Adlaf, Paglia-
Boak, Beitchman, & Wolfe, 2006)


Frequency of gambling in past 12 months
Largest amount gambled in past 12 months
 Subset of items from the South Oaks Gambling Screen
Revised for Adolescents (SOGS-RA; Winters, Stinchfield,
& Fulkerson, 1993)





How often gone back to win $ lost?
Gambled more than planned?
Felt bad about gambling?
Argued with family/friends?
Borrowed money and not paid it back?
Measures
Risk Factors
•
•
•
•
•
•
•
Alcohol use - Alcohol Use Disorders Identification Test
(AUDIT; Saunders et al., 1993)
Drug Use – Add Health items (Harris et al., 2003)
Peer violence – Add Health and CTS2 items (Sieving et al.,
2001; Strauss et al., 1996)
Dating violence – CADRI items (Wolfe et al., 2001)
Community violence (Richters & Martinez, 1993)
Delinquency (Zimmerman et al., 2000)
Peer influence (negative) (Ostaszewski & Zimmerman, 2006)
Measures
Promotive Factors
 Adult mentors (Zimmerman et al., 2002)
 School, religious, community involvement (Doljanac &
Zimmerman, 1998)
 Parental monitoring (Arthur et al., 2002)
 Peer influence (positive) (Ostaszewski & Zimmerman, 2006)
Measures
 Index scores
 Risk and promotive factor index scores
 All items standardized
 Upper 15.9% of the distribution high levels of risk or promotive
factor (score of 2), middle 68.2% average levels (score of 1),
and lower 15.9% low or no promotion or risk (score of 0)
 Combine from all measures
Goldstein, A. L., Walton, M. A., Cunningham, R., Chermack, S., & Blow, F. (in press). A latent class analysis of adolescent
gambling: Application of resilience theory. International Journal of Mental Health and Addiction.
Bivariate associations between gambling groups,
demographics and risk factors
Variable
Low Cons
(N=155)
High Cons Total
(N=94)
(N=249)
61.3%
43.9%
84.0%
63.8%
69.9%
51.4%
66.5%
57.8%
66.0%
60.2%
66.3%
58.7%
3.3 (2.7)
2.4 (2.4)
0.8 (2.3)
3.7 (2.9)
3.6 (2.8)
3.6 (2.4)
1.4 (3.2)
4.6 (3.4)
3.4 (2.7)
2.9 (2.5)
1.1 (2.7)
4.1 (3.1)
Peer violence (M, SD)***
9.4 (8.6)
16.1(11.6)
12.0 (10.3)
Dating violence (M, SD)**
2.4 (3.4)
4.2 (4.6)
3.1 (4.0)
Community violence (M, SD)***
Friends’ negative influence (M, SD)***
Delinquency (M, SD) ***
4.1 (2.9)
9.5 (5.9)
3.7 (4.7)
6.7 (2.9)
13.1(6.2)
8.2 (8.2)
5.1 (3.1)
10.8 (6.3)
5.4 (6.6)
Demographic Variables
Gender (Male) (%)***
Race (African-American) (%)**
Age group (17 and 18) (%)
Public Assistance (Yes) (%)
Risk Factors
Smoke cigarettes (M, SD)
Use marijuana (M, SD)***
Use illicit drugs (M, SD)
AUDIT-C score (M, SD)*
Goldstein, A. L., Walton, M. A., Cunningham, R., Chermack, S., & Blow, F. (in press). A latent class analysis of adolescent
gambling: Application of resilience theory. International Journal of Mental Health and Addiction.
Bivariate associations between gambling groups,
promotive factors, and index scores
Variable
Low
Cons
(N=155)
High Cons Total
(N=94)
(N=249)
School involvement (M, SD)
2.3 (2.3)
1.9 (2.2)
2.1(2.3)
Community involvement (M, SD)
0.8 (1.6)
0.8 (1.6)
0.8 (1.6)
Religious involvement (M, SD)
2.2 (2.1)
2.0 (2.1)
2.1 (2.1)
Adult mentor (%)
64.5%
59.6%
62.7%
Parental monitoring (M, SD) ***
23.0 (5.5) 20.8 (5.2)
22.1 (6.5)
Friends’ positive influence (M, SD)
6.1 (3.3)
5.7 (2.9)
6.0 (3.2)
Risk Factor Index (M, SD)***
9.8 (3.0)
12.0 (3.1)
10.6 (3.2)
Promotive Factor Index (M, SD)*
5.6 (1.2)
5.3 (1.3)
5.5 (1.2)
Promotive Factors
Goldstein, A. L., Walton, M. A., Cunningham, R., Chermack, S., & Blow, F. (in press). A latent class analysis of adolescent
gambling: Application of resilience theory. International Journal of Mental Health and Addiction.
Application of Resilience Theory to Predicting
Classification in High Consequence Group
Variable
Model 1
Risk Only
Risk Factor
Index
1.30***
Promotive
Factor Index
Risk x
Promotive
Factor
Model 2
Compensatory
1.18-1.44
Model 3
Risk-Protective
1.33***
1.19-1.50 1.37***
1.22-1.55
0.98
0.77-1.26 0.97
0.75-1.24
1.10*
1.01-1.21
Goldstein, A. L., Walton, M. A., Cunningham, R., Chermack, S., & Blow, F. (in press). A latent class analysis of adolescent
gambling: Application of resilience theory. International Journal of Mental Health and Addiction.
Predicted value for gambling consequence group as a
function of high vs. low promotive factor
Goldstein, A. L., Walton, M. A., Cunningham, R., Chermack, S., & Blow, F. (in press). A latent class analysis of adolescent
gambling: Application of resilience theory. International Journal of Mental Health and Addiction.
Conclusions
 Promotive factors attenuate risk for gambling
 The driving promotive factor is parental monitoring
 Consistent with literature on substance use in
adolescence
 Important role of parents, over and above other
factors
Child Maltreatment & Gambling
 Child maltreatment identified as a significant risk
factor for the development of gambling problems
 Theoretical models highlight gambling as a way of
coping with early trauma (Blaszczynski & Nower,
2002; Jacobs, 1986; Lesieur & Blume, 1991)
Child Maltreatment & Gambling
 Pathological gamblers have higher rates of CM than
general population and increased severity of CM
associated with lower age of gambling onset (Petry &
Steinberg, 2005)
 In a community sample, individuals with gambling
problems have higher rates of CM than those without
(Hodgins et al., 2010)
 Similar findings emerged for a sample of adolescents
and young adults (Felsher et al., 2010)
Parental Monitoring in a Child Welfare Sample?
 Examine the role of parental/caregiver monitoring in
promoting resilience in a sample of emerging adults
transitioning out of child welfare
 Do promotive factors compensate or moderate the
relationship between CM and gambling?
Study of Emerging Adults in CW
 Recruited emerging adults on “cheque day”
 97 emerging adults participated (76.0% female)
 Majority was currently attending school (56.7%) and
36.1% were employed
 Had been involved with child welfare for an average
of 9 years (SD = 4.13)
Measures
 Child maltreatment – Childhood Trauma
Questionnaire – Short Form (Bernstein et al., 2003)

Number of types of moderate to severe maltreatment
 Connor-Davidson Resilience Scale (CD-RISC; Connor &
Davidson, 2003)
 Measures salient features of resilience (patience, self-efficacy,
tolerance of negative affect, optimism)
 Measure of internal resilience
 Caregiver monitoring (Barnes et al., 1999)
Results
 Maltreatment scores ranged from 0 to 5

33.6% experienced 1-2 types

28.6% experienced 3-4 types

15.3% experienced all 5 types
 Overall, 29.6% of the sample reported lifetime
gambling
 21.4% reported spending between $1 to $9 on
gambling and only 7.1% had spend more than $50
at one time
 12.2% of participants had experienced problems
related to their gambling
Bivariate relationships between background variables,
maltreatment and promotive factors
1
2
3
4
5
6
7
8
1. Age
2. Gender
.17
3. Years in CAS
.06
-.03
4. Number CAS
caseworkers
-.21
-.08
.18
5. CDRISC
-.03
-.14
-.10
-.05
6. Caregiver
Monitoring
-.14
.14
.13
-.01
.36**
7. Types of
Maltreatment
.27**
.19
-.02
.11
-.17
.30**
8. Gambling
Frequency
-.02
-.27** .05
.11
-.08
-.07
9. Gambling
Consequences
-.10
.11
.21
-.11
-.44** .11
-.15
.07
.32*
Conclusions
 Preliminary findings – child maltreatment did not
increase risk for gambling frequency or
consequences
 However, caregiver monitoring was significantly
associated with fewer gambling consequences
 Further evidence that parental monitoring plays a
significant and important role in reducing problem
gambling behaviours in youth and young adults
Thank You!
 Funding for research



Social Sciences and Humanities Research Council
National Institute on Alcohol Abuse and Alcoholism (M. Walton, PI)
Ministry of Research and Innovation – Early Researcher Award
 Collaborators








Christine Wekerle, Ph.D. (McMaster University)
Deborah Goodman, Ph.D. (Children’s Aid Society of Toronto)
Bruce Leslie, M.S.W (Toronto Catholic Children’s Aid Society)
Maureen Walton, Ph.D. (University of Michigan)
Rebecca Cunningham, M.D. (University of Michigan)
Marc Zimmerman, Ph.D. (University of Michigan)
Stephen Chermack, PhD. (University of Michigan)
Fred Blow, Ph.D. (University of Michigan)