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Avoiding gambling harm: An evidence-based set of safe gambling practices for consumers

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  • Nerilee Hing
  • Matthew Browne
  • Alex M T Russell
  • Matthew Rockloff
  • Vijay Rawat
  • Fiona Nicoll
  • Garry Smith

Abstract

Prior studies have identified self-regulatory strategies that are infrequently used by problem-gamblers, but which might be protective if used. However, guidelines with evidence-based safe gambling practices (SGPs) that prevent gambling-related harm are lacking. This study aimed to: 1) identify a parsimonious set of evidence-based SGPs that best predict non-harmful gambling amongst gamblers who are otherwise most susceptible to experiencing gambling harm; 2) examine how widely are they used; and 3) assess whether their use differs by gambler characteristics. A sample of 1,174 regular gamblers in Alberta Canada completed an online survey measuring uptake of 43 potential SGPs, gambling harms and numerous risk factors for harmful gambling. Elastic net regression identified a sub-sample of 577 gamblers most susceptible to gambling harm and therefore most likely to benefit from the uptake of SGPs. A second elastic net predicted gambling harm scores in the sub-sample, using the SGPs as candidate predictors. Nine SGPs best predicted non-harmful gambling amongst this sub-sample. The behaviour most strongly associated with increased harm was using credit to gamble. The behaviour most strongly associated with reduced harm was ‘If I’m not having fun gambling, I stop’. These SGPs form the basis of evidence-based safe gambling guidelines which can be: 1) promoted to consumers, 2) form the basis of self-assessment tests, 3) used to measure safe gambling at a population level, and 4) inform supportive changes to policy and practice. The guidelines advise gamblers to: stop if they are not having fun, keep a household budget, keep a dedicated gambling budget, have a fixed amount they can spend, engage in other leisure activities, avoid gambling when upset or depressed, not use credit for gambling, avoid gambling to make money, and not think that strategies can help you win. These guidelines are a promising initiative to help reduce gambling-related harm.

Suggested Citation

  • Nerilee Hing & Matthew Browne & Alex M T Russell & Matthew Rockloff & Vijay Rawat & Fiona Nicoll & Garry Smith, 2019. "Avoiding gambling harm: An evidence-based set of safe gambling practices for consumers," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-18, October.
  • Handle: RePEc:plo:pone00:0224083
    DOI: 10.1371/journal.pone.0224083
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    References listed on IDEAS

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    1. Ben D. MacArthur & Richard O. C. Oreffo, 2005. "Bridging the gap," Nature, Nature, vol. 433(7021), pages 19-19, January.
    2. Simone N. Rodda & Nerilee Hing & David C. Hodgins & Alison Cheetham & Marissa Dickins & Dan I. Lubman, 2018. "Behaviour change strategies for problem gambling: an analysis of online posts," International Gambling Studies, Taylor & Francis Journals, vol. 18(3), pages 420-438, September.
    3. Matthew Browne & Nancy Greer & Vijay Rawat & Matthew Rockloff, 2017. "A population-level metric for gambling-related harm," International Gambling Studies, Taylor & Francis Journals, vol. 17(2), pages 163-175, May.
    4. Simone N. Rodda & Kathleen L. Bagot & Victoria Manning & Dan I. Lubman, 2019. "‘Only take the money you want to lose’ strategies for sticking to limits in electronic gaming machine venues," International Gambling Studies, Taylor & Francis Journals, vol. 19(3), pages 489-507, September.
    5. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    6. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    Cited by:

    1. Matthew Browne & Vijay Rawat & Catherine Tulloch & Cailem Murray-Boyle & Matthew Rockloff, 2021. "The Evolution of Gambling-Related Harm Measurement: Lessons from the Last Decade," IJERPH, MDPI, vol. 18(9), pages 1-14, April.
    2. Marie-Claire Flores-Pajot & Sara Atif & Magali Dufour & Natacha Brunelle & Shawn R. Currie & David C. Hodgins & Louise Nadeau & Matthew M. Young, 2021. "Gambling Self-Control Strategies: A Qualitative Analysis," IJERPH, MDPI, vol. 18(2), pages 1-15, January.

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