Literature DB >> 31965383

The Gambling Consumption Mediation Model (GCMM): A Multiple Mediation Approach to Estimate the Association of Particular Game Types with Problem Gambling.

Tim Brosowski1, Daniel Thor Olason2, Tobias Turowski3, Tobias Hayer3.   

Abstract

Bivariate associations of problem gambling with participation in particular game types often decrease when adjusting for demographics or consumption behavior (e.g., number of game types played). A summary of 14 peer-reviewed studies showed inconsistencies as well as conceptual and methodological challenges. The aim of this study was to expand previous research by a combination of (1) sophisticated feature-engineering, which disaggregates gambling intensity into facets within and beyond a game type of interest, and (2) the application of mediation models. Data comprised last year gamblers of three merged cross sectional Icelandic gambling surveys of 2007, 2011, and 2017 (N = 4422). For each of 15 game types (12-month time frame), a parallel multiple mediation model was applied to disaggregate bivariate associations of last year game type participation and problem gambling (Problem Gambling Severity Index) by six mediating mechanisms: (1) demographic problem gambling propensity, (2) number of game types played, (3) gambling frequency within the type, (4) maximum gambling frequency across all types beyond, (5) usual spending within the type, (6) maximum usual spending across all types beyond. Games showed two distinct profiles via which mediator they mostly impacted problem gambling: Electronic gaming machines offline, scratch cards offline, live betting online, and poker offline as well as online impacted problem gambling mostly via gambling frequency within, whereas all other types mostly impacted via the number of game types played. The applied mediation models answer the question by which mechanism game types impact problem gambling in a more exhaustive way than previous research.

Entities:  

Keywords:  Breadth; Depth; Intensity; Mediation; Problem gambling

Mesh:

Year:  2021        PMID: 31965383      PMCID: PMC7882568          DOI: 10.1007/s10899-020-09928-3

Source DB:  PubMed          Journal:  J Gambl Stud        ISSN: 1050-5350


  41 in total

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6.  Understanding cultural omnivores: social and political attitudes.

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7.  Decoding Problem Gamblers' Signals: A Decision Model for Casino Enterprises.

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Journal:  J Gambl Stud       Date:  2015-12

8.  Disordered gambling, type of gambling and gambling involvement in the British Gambling Prevalence Survey 2007.

Authors:  Debi A LaPlante; Sarah E Nelson; Richard A LaBrie; Howard J Shaffer
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9.  Type of gambling as an independent risk factor for suicidal events in pathological gamblers.

Authors:  Anja Bischof; Christian Meyer; Gallus Bischof; Ulrich John; Friedrich Martin Wurst; Natasha Thon; Michael Lucht; Hans-Joergen Grabe; Hans-Juergen Rumpf
Journal:  Psychol Addict Behav       Date:  2016-01-21

10.  The relationship between player losses and gambling-related harm: evidence from nationally representative cross-sectional surveys in four countries.

Authors:  Francis Markham; Martin Young; Bruce Doran
Journal:  Addiction       Date:  2015-11-16       Impact factor: 6.526

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  3 in total

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Authors:  Jacob J Coutts; Andrew F Hayes
Journal:  Behav Res Methods       Date:  2022-10-17

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3.  Alter Game: A Study Protocol on a Virtual "Serious Game" for Relapse Prevention in Patients With Gambling Disorder.

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  3 in total

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