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Alexandre Infanti’s presentation
@ 10:30 - 12:00
Exploring DSM-5 Internet Gaming Disorder (IGD) criteria and DSM-5 IGD classification using traditional statistics and supervised machine learning
Online video games count around 827m users worldwide in 2019, it is also a source of revenue that is projected to reach more than 18m in 2020 (Statista, 2020). Even if for most people playing video games is a healthy leisure, a minority of users presents with excessive use associated with negative consequences and functional impairment (Kardefelt-Winther et al., 2017). In 2013, tentative criteria to define Internet Gaming Disorder were introduced in DSM-5 (APA, 2013). Nevertheless, some of these criteria have been criticized as they might be susceptible to pathologize healthy gamers. A recent Delphi study highlighted the most relevant/irrelevant criteria regarding the IGD (Castro-Calvo et al., 2020). In our analyses, we will use data coming from 3 studies in order to observe if supervised machine learning, in complement with traditional statistics, is able to further establish the diagnostic validity of DSM-5 IGD criteria.
Participer à la réunion Zoom: https://unil.zoom.us/j/94836632134