New project will use machine learning to help improve engagement in gambling addiction treatment
A new project led by Professor Matt Field at the University of Sheffield aims to significantly improve how people engage with treatment for gambling addiction, thanks to funding from the Academic Forum for the Study of Gambling.
The research team will use sophisticated machine learning – where computers analyse large amounts of data to find patterns – to understand why many individuals drop out of gambling treatment services.
Using data from project partners the NHS Northern Gambling Service, researchers will use sophisticated data analysis techniques to study predictors of treatment retention/dropout and to better understand why a significant 40% of those referred to the service do not attend an initial appointment.
High dropout rates are a recognised challenge in addiction treatment services worldwide, and understanding the specific drivers within the UK's problem gambling support system remains limited. There can be many drivers behind dropout, although current understanding of the precise reasons and predictors of dropout among people in the UK seeking help for problem gambling is relatively weak.
The Academic Forum for the Study of Gambling (AFSG) has award exploratory grants to three projects which aim to provide insights to improve the identification and treatment of gambling harms in the UK. Matt's project 'Understanding and improving engagement and retention in NHS gambling treatment services' will begin in July 2025 and run for a year. It is hoped the findings will be a springboard for future research aimed at developing and testing interventions to improve engagement and retention in gambling treatment.
Speaking about the project, Matt said:
"My colleagues and I are excited to begin this programme of work, which will use advanced statistical techniques that have been used in other areas of mental health and clinical psychology in recent years. We are delighted to partner with the NHS Northern Gambling Clinics, and our findings will shed light on the factors that determine dropout from treatment, in many cases even before the initial clinical assessment. Our approach has the potential to identify client characteristics and other contextual factors that can be the target of focussed interventions to improve engagement and retention in treatment, and thereby improve the outcomes of treatment".
A version of this story was first published on the University of Sheffield website.
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