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PubMed Publications| Keyword search (4,164 papers available) |  |
"French M" Authored Publications:
| Title | Authors | PubMed ID | |
|---|---|---|---|
| 1 | A portrait of online gambling: a look at a transformation amid a pandemic | Kairouz S; Savard AC; Murch WS; Dixon MR; Martin NB; Brodeur M; Dauphinais S; Ferland F; Hamel D; Dufour M; French M; Monson E; Van Mourik V; Morvannou A; | 40770758 CONCORDIA |
| 2 | Distinguishing Persistent Versus Episodic Clusters of At-Risk Respondents on the Problem Gambling Severity Index | Murch WS; Scheurich R; Monson E; French M; Kairouz S; | 40338426 PSYCHOLOGY |
| 3 | "It would Never have Happened Without the Pandemic": Understanding the Lived Experience of Individuals who Increased Their Online Gambling Participation | Savard AC; Kairouz S; Nadeau-Tremblay J; Brodeur M; Ferland F; French M; Morvannou A; Blanchette-Martin N; Dufour M; VanMourik V; Monson E; | 39115755 SOCANTH |
| 4 | The HIV self-testing debate: where do we stand? | Gagnon M; French M; Hébert Y; | 29347929 SOCANTH |
| 5 | Criminal Code reform of HIV non-disclosure is urgently needed: Social science perspectives on the harms of HIV criminalization in Canada | Hastings C; French M; McClelland A; Mykhalovskiy E; Adam B; Bisaillon L; Bogosavljevic K; Gagnon M; Greene S; Guta A; Hindmarch S; Kaida A; Kilty J; Massaquoi N; Namaste V; O' Byrne P; Orsini M; Patterson S; Sanders C; Symington A; Wilson C; | 38087186 PSYCHOLOGY |
| 6 | Using machine learning to retrospectively predict self-reported gambling problems in Quebec | Murch WS; Kairouz S; Dauphinais S; Picard E; Costes JM; French M; | 36880253 SOCANTH |
| 7 | COVID-19, public health, and the politics of prevention. | Mykhalovskiy E; French M; | 33156541 SOCANTH |
| 8 | Consent and criminalisation concerns over phylogenetic analysis of surveillance data. | Chung C, Khanna N, Cardell B, Spieldenner A, Strub S, McClelland A, French M, Gagnon M, Guta A | 31272660 SOCANTH |
| Title: | Using machine learning to retrospectively predict self-reported gambling problems in Quebec | ||||
| Authors: | Murch WS, Kairouz S, Dauphinais S, Picard E, Costes JM, French M | ||||
| Link: | https://pubmed.ncbi.nlm.nih.gov/36880253/ | ||||
| DOI: | 10.1111/add.16179 | ||||
| Publication: | Addiction (Abingdon, England) | ||||
| Keywords: | Behaviour tracking; behavioural addiction; machine learning; online gambling; problem gambling; random Forest; | ||||
| PMID: | 36880253 | Category: | Date Added: | 2023-03-07 | |
| Dept Affiliation: |
SOCANTH
1 Department of Sociology and Anthropology, Concordia University, Montreal, Quebec, Canada. |
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Description: |
Background and aims: Participating in online gambling is associated with an increased risk for experiencing gambling-related harms, driving calls for more effective, personalized harm prevention initiatives. Such initiatives depend on the development of models capable of detecting at-risk online gamblers. We aimed to determine whether machine learning algorithms can use site data to detect retrospectively at-risk online gamblers indicated by the Problem Gambling Severity Index (PGSI). Design: Exploratory comparison of six prominent supervised machine learning methods (decision trees, random forests, K-nearest neighbours, logistic regressions, artificial neural networks and support vector machines) to predict problem gambling risk levels reported on the PGSI. Setting: Lotoquebec.com (formerly espacejeux.com), an online gambling platform operated by Loto-Québec (a provincial Crown Corporation) in Quebec, Canada. Participants: N = 9145 adults (18+) who completed the survey measure and placed at least one bet using real money on the site. Measurements: Participants completed the PGSI, a self-report questionnaire with validated cut-offs denoting a moderate-to-high-risk (PGSI 5+) or high-risk (PGSI 8+) for experiencing past-year gambling-related problems. Participants agreed to release additional data about the preceding 12 months from their user accounts. Predictor variables (144) were derived from users' transactions, apparent betting behaviours, listed demographics and use of responsible gambling tools on the platform. Findings: Our best classification models (random forests) for the PGSI 5+ and 8+ outcome variables accounted for 84.33% (95% CI = 82.24-86.41) and 82.52% (95% CI = 79.96-85.08) of the total area under their receiver operating characteristic curves, respectively. The most important factors in these models included the frequency and variability of participants' betting behaviour and repeat engagement on the site. Conclusions: Machine learning algorithms appear to be able to classify at-risk online gamblers using data generated from their use of online gambling platforms. They may enable personalized harm prevention initiatives, but are constrained by trade-offs between their sensitivity and precision. |
