Predicting problem gambling among online sports and race bettors: Assessing the value of machine learning using behavioural and self-reported data.
- DOI
- 10.1556/2006.2025.00525
- Published
- 2026 Jun 2
- Container
- Journal of behavioral addictions
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1556/2006.2025.00525,
title = {Predicting problem gambling among online sports and race bettors: Assessing the value of machine learning using behavioural and self-reported data.},
author = {Heirene RM and Zhang E and Vanichkina D and de Leau CT and Huynh ELY and Gainsbury SM},
year = {2026},
journal = {Journal of behavioral addictions},
doi = {10.1556/2006.2025.00525},
url = {https://doi.org/10.1556/2006.2025.00525}
}RIS
TY - JOUR TI - Predicting problem gambling among online sports and race bettors: Assessing the value of machine learning using behavioural and self-reported data. AU - Heirene RM AU - Zhang E AU - Vanichkina D AU - de Leau CT AU - Huynh ELY AU - Gainsbury SM PY - 2026 JO - Journal of behavioral addictions DO - 10.1556/2006.2025.00525 UR - https://doi.org/10.1556/2006.2025.00525 ER -
APA
RM, H., E, Z., D, V., CT, D. L., ELY, H., & SM, G. (2026). Predicting problem gambling among online sports and race bettors: Assessing the value of machine learning using behavioural and self-reported data.. Journal of behavioral addictions. https://doi.org/10.1556/2006.2025.00525
Source records
- pubmed · retrieved 2026-09-24T23:44:54.361Z