Abstract
Recent research has shown that harm is not just a feature of problem gambling, but can also be observed in other lower risk categories. Some debates exist, however, as to the distribution of harm across these categories and how harm should be best measured. This study was designed to examine how estimates of self-reported harm are affected by the methodology used. A particular focus was on how harm estimates for low and higher risk gambling (as classified by the PGSI) varied when respondents were able to make more graded attributions of their harm to gambling. An online panel sample of 554 gamblers responded to a brief survey that included the PGSI, measures of gambling harm drawn from Browne et al. (Assessing gambling-related harm in Victoria: a public health perspective, Victorian Responsible Gambling Foundation, Melbourne, 2016) as well as questions about demographics and gambling habits. The recruitment was designed to obtain good representation of each PGSI group, with 23% found to be problem gamblers; 36% moderate risk and 21% low risk gamblers. In support of Browne et al. (2016), the findings showed that higher proportions of harm in low risk gamblers is likely to be identified when one uses binary or ‘any harm’ scoring, but that this effect mostly disappears when more graded scoring or attribution of harm measures are used. Higher risk PGSI groups consistently reported more harms and more serious harms than lower risk groups. It was concluded that the measurement of gambling harm and its estimated distribution over PGSI categories is quite sensitive to how it is measured.
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Notes
In a sample of 500, the number of cases with gambling harm for LR, MR and PG can be < 10 with soft scoring (i.e., any harm) and only 2 or 3 for moderate harm scoring. Even a sample of 1500, as used by Browne and Rockloff (2018) would not avoid this problem.
In the UK, the prevalence of problem gambling based on the PGSI similar to Australia, but the % of MR (1.1%) and LR (2.4%) is generally lower (Gambling Commission 2019).
In fairness to Browne et al. (2016), their sample had a larger proportion of Australian gamblers which made the use of Australian prevalence data more relevant to their sample. However, their recruitment method was not straight-forward and involved several criteria which would not make it representative of the general population.
References
Abbott, M. W., Binde, P., Hodgins, D., et al. (2013). Conceptual framework of harmful gambling: An international collaboration. Ontario: OPGRC.
Ben-Tovim, D., Esterman, A., Tolchard, B., & Battersby, M. (2001). The Victorian gambling screen. Report prepared for the Gambling Research Panel, Melbourne, Victoria.
Blaszczynski, A., Anjoul, F., Shannon, K., Keen, B., Pickering, D., & Wieczorek, M. (2015). Gambling harm minimisation report. Sydney: Office of Liquor, Gambling and Racing, NSW.
Browne, M., Goodwin, B. C., & Rockloff, M. (2018). Validation of the Short Gambling Harm Screen (SGHS): A tool for assessment of harms from gambling. Journal of Gambling Studies, 34, 499–512.
Browne, M., Langham, E., Rawat, V., Greer, N., Li, E., Rose, J., et al. (2016). Assessing gambling-related harm in Victoria: A public health perspective. Melbourne: Victorian Responsible Gambling Foundation.
Browne, M., Rawat, V., Greer, N., Langham, E., Rockloff, M., & Hanley, C. (2017). What is the harm? Applying a public health methodology to measure the impact of gambling problems and harm on quality of life. Journal of Gambling Issues, 36, 28–50.
Browne, M., & Rockloff, M. (2017). The dangers of conflating gambling-related harm with disordered gambling: Commentary on: Prevention paradox logic and problem gambling (Delfabbro & King, 2017). Journal of Behavioral Addictions, 6, 317–320.
Browne, M., & Rockloff, M. (2018). Prevalence of gambling-related harm provides evidence for the prevention paradox. Journal of Behavioral Addictions. https://doi.org/10.1556/2006.7.2018.41. (Advance online publication).
Canale, N., Vieno, A., & Griffiths, M. D. (2016). The extent and distribution of gambling-related harms and the prevention paradox in a British population survey. Journal of Behavioral Addictions, 5, 204–212.
Commission, Gambling. (2019). Gambling participation in 2018: Behaviour, awareness and attitudes. London: Gambling Commission.
Delfabbro, P. H., & King, D. (2017). Prevention Paradox logic and problem gambling: Does low risk gambling impose a greater burden of harm than high risk gambling? Journal of Behavioral Addictions, 6, 163–167.
Delfabbro, P. H., & King, D. (2018). Challenges in the conceptualisation and measurement of gambling-related harm. Journal of Gambling Studies (in press).
Department of Treasury and Finance. (2017). Fourth social and economic impact study of gambling in Tasmania. Hobart: Tasmanian Government.
Ferris, J., & Wynne, H. (2001). The Canadian Problem Gambling Index: Final report. Ottawa, Canada.
Haeusler, J. (2019). Responsible gambling: An international industry stakeholder’s perspective. In H. Shaffer, A. Blazczynski, R. Ladouceur, P. Collins, & D. Fong (Eds.), Responsible gambling: Primary stakeholder perspectives (pp. 181–207). New York: Oxford University Press.
Korn, D., & Shaffer, H. (1999). Gambling and the health of the public: Adopting a public health perspective. Journal of Gambling Studies, 15, 289–365.
Langham, E., Thorne, H., Browne, M., Donaldson, P., Rose, J., & Rockloff, M. (2016). Understanding gambling related harm: A proposed definition, conceptual framework, and taxonomy of harms. BMC Public Health, 16, 1–23.
Lesieur, H., & Blume, S. (1987). The South Oaks Gambling Screen (the SOGS): A new instrument for the identification of problem gamblers. American Journal of Psychiatry, 144, 1184–1188.
Li, E., Browne, M., Rawat, V., Langham, E., & Rockloff, M. (2017). Breaking bad: Comparing gambling harms among gamblers and affected others. Journal of Gambling Studies, 33, 223–248.
Markham, F., Young, M., Doran, B., et al. (2017). A meta-regression analysis of 41 Australian problem gambling prevalence estimates and their relationship to total spending on electronic gaming machines. BMC Public Health, 17, 11.
Productivity Commission. (1999). Australia’s gambling industries. Canberra: Productivity Commission.
Raisamo, S. U., Makela, P., Salonen, A., & Lintonen, T. (2014). The extent and distribution of gambling harm in Finland as assessed by the Problem Gambling Severity Index. European Journal of Public Health, 25, 716–722.
Rawat, V., Browne, M., Bellringer, M., Greer, N., Kolandai-Matchett, K., Rockloff, M., et al. (2018). A tale of two countries: Comparing disability weights for gambling problems in New Zealand and Australia. Quality of Life Research, 27, 2361–2371.
Shaffer, H. J., & Korn, D. A. (2002). Gambling and related mental disorders: A public health analysis. Annual Review of Public Health, 23, 171–212.
Volberg, R., & Williams, R. J. (2012). Best practices in the population assessment of problem gambling. Report prepared for the Ontario Problem Gambling Research Centre. Guelph, Ontario, Canada.
Wardle, H., Reith, G., Best, D., McDaid, D., & Platt, S. (2018). Measuring gambling-related harms: A framework for action. London: GambleAware.
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Delfabbro, P., Georgiou, N. & King, D.L. Measuring Gambling Harm: The Influence of Response Scaling on Estimates and the Distribution of Harm Across PGSI Categories. J Gambl Stud 37, 583–598 (2021). https://doi.org/10.1007/s10899-020-09954-1
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DOI: https://doi.org/10.1007/s10899-020-09954-1
Keywords
- Gambling-harm
- Response-scaling
- Problem gambling
- Low risk gambling