Abstract – Julie Pattinson - Researcher Education Programme

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Gambling-related harm in UK elderly populations: An evaluation of physical health
and psychological well-being as predictive factors for harm
The ultimate aim of this research programme is to develop knowledge and understanding of
gambling-related harm within UK adults over the age of 65 years. It is evident that there is a
paucity of empirical research from which to develop theory regarding development and
maintenance of gambling-related harm within this demographic. It is expected that the
research outcomes from this thesis will provide a foundation for development of a long term
research programme into prevention, screening and intervention for gambling-related harm
for elderly adults in the UK. Objectives include (1) Observe common gambling behavioural
patterns within elderly population in the UK, with specific reference to frequency, duration
and preferences (2) Identify common gambling cognitive patterns with specific reference to
motivation and reward for gambling involvement for this population (3) Comparative
analysis of demographic and physical health differences between problem gamblers, nonproblem gamblers and non-gamblers within elderly population (4) Comparative analysis of
psychological health differences between problem gamblers, non-problem gamblers and nongamblers within elderly populations, with specific reference to affective disorders (5) Assess
predictive value of physical health and psychological well-being factors for gambling-related
harm in specific subgroups in elderly populations.
The research design is comprised of four separate studies. The aim of study is one is to
develop a substantive framework to account for gambling behaviour and cognition in elderly
populations within the United Kingdom. An inductive approach will be employed to meet the
required objective, namely systematic Grounded Theory (Strauss & Corbin, 1986). Study two
will observe statistical relationships between gambling-related harm and physical health
variables in elderly populations. Study two will employ a cross-sectional survey design to
examine risk factors for gambling-related harm, with specific emphasis on physiological
health factors. As well as demographic and behavioural questions, important physical health
variables to emerge in Study 1 will be included, and specific attention will be drawn to the
construct of Physical Frailty. Four sets of variables to be measured include gambling
behaviour, level of frailty, sex differences and demographics. Instruments include The
Canadian Study of Health and Ageing (CSHA) Clinical Frailty Scale (Rockwood, Xiaowei,
Macknight, Bergman, Hogan, McDowell & Arnold, 2005), The Problem Gambling Severity
Index (PGSI) (Ferris & Wynne, 2001) and to measure cognitive impairment, The Clock
Drawing Test (Shua-Haim, Koppuzha & Gross, 1996) is a valid screening test for dementia
and cognitive dysfunction. The survey will cover multiple geographical locations collating
information from a diverse cross-sectional sample of approximately 1000. Study three will
observe statistical relationships between gambling-related harm and psychological disorder in
elderly populations. Study three will employ a similar design as Study two, but instead of
physical health, psychological well-being will be observed in context with gambling-related
harm across general elderly populations. The survey will focus primarily on depression and
anxiety disorders, however other relevant psychological disorders that appear to be important
based on the findings of Study one will also be included. Instruments for study three include
The 15 Item - Geriatric Depression Scale (GDS) (Sheikh &Yesavage, 1986) The Geriatric
Anxiety Inventory (GAI) (Pachana, Byrne, Siddle, Koloski, Harley & Arnold, 2007), The
Problem Gambling Severity Index (PGSI) (Ferris & Wynne, 2001) and The Clock Drawing
Test (Shua-Haim et al , 1996). Study four aims to develop a prediction model for gamblingrelated harm in UK elderly populations. Study four will initially employ a correlational
design using Multiple Regression to initiate development of a prediction model for gamblingrelated harm within this demographic.
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