Uncovering the most robust predictors of problematic pornography use: A large-scale machine learning study across 16 countries.

Details

Ressource 1Request a copy Under indefinite embargo.
UNIL restricted access
State: Public
Version: author
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Serval ID
serval:BIB_3D1E48F7BFAE
Type
Article: article from journal or magazin.
Collection
Publications
Institution
UNIL/CHUV
Title
Uncovering the most robust predictors of problematic pornography use: A large-scale machine learning study across 16 countries.
Journal
Journal of Psychopathology and Clinical Science
Author(s)
Bőthe Beáta, Vaillancourt-Morel Marie-Pier, Bergeron Sophie, Hermann Zsombor, Ivaskevics Krisztián, Kraus Shane W., Grubbs Joshua B.
ISSN
2769-755X
2769-7541
Publication state
Published
Issued date
17/06/2024
Peer-reviewed
Oui
Language
english
OAI-PMH
oai:serval.unil.ch:BIB_3D1E48F7BFAE
DOI
10.1037/abn0000913
Pubmed
38884980
Create date
18/07/2024 18:40
Last modification date
19/07/2024 7:07
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