Uncovering the Most Robust Predictors of Problematic Pornography Use: A Large-Scale Machine Learning Study Across 16 Countries.
Problematic pornography use (PPU) is the most common manifestation of the newly introduced compulsive sexual behavior disorder diagnosis in the 11th revision of the International Classification of Diseases. Research related to PPU has proliferated in the past two decades, but most prior studies were...
| Publicado en: | Journal of Psychopathology & Clinical Science Vol. 133; no. 6; pp. 489 - 503 |
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| Autores principales: | , , , , , , |
| Formato: | Artículo |
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American Psychological Association
Aug2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=178736263&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 178736263 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 27697541 MXAZ jtl: Journal of Psychopathology & Clinical Science issn: 27697541 maglogo: N pubinfo: dt: Aug2024 vid: 133 iid: 6 pid: 34 pub: American Psychological Association artinfo: ui: 178736263 10.1037/abn0000913 ppf: 489 ppct: 14 formats: tig: atl: Uncovering the Most Robust Predictors of Problematic Pornography Use: A Large-Scale Machine Learning Study Across 16 Countries. aug: au: Bőthe, Beáta Vaillancourt-Morel, Marie-Pier Bergeron, Sophie Hermann, Zsombor Ivaskevics, Krisztián Kraus, Shane W. Grubbs, Joshua B. affil: Département de Psychologie, Université du Québec à Trois-Rivières Centre de Recherche Interdisciplinaire Sur Les Problèmes Conjugaux et Les Agressions Sexuelles (CRIPCAS), Montreal, Québec, Canada Département de Psychologie, Université de Montréal Department of Criminal Psychology, Faculty of Law Enforcement, National University of Public Service Doctoral School of Law Enforcement, Faculty of Law Enforcement, National University of Public Service Department of Psychology, University of Nevada, Las Vegas Department of Psychology, University of New Mexico Center on Alcohol, Substance Use, and Addictions, University of New Mexico su: Artificial intelligence Compulsive behavior Human sexuality Random forest algorithms Machine learning sug: subj: Artificial intelligence Compulsive behavior Human sexuality Random forest algorithms Machine learning keyword: artificial intelligence compulsive sexual behavior machine learning problematic pornography use sex addiction artificial intelligence compulsive sexual behavior machine learning problematic pornography use sex addiction ab: Problematic pornography use (PPU) is the most common manifestation of the newly introduced compulsive sexual behavior disorder diagnosis in the 11th revision of the International Classification of Diseases. Research related to PPU has proliferated in the past two decades, but most prior studies were characterized by several shortcomings (e.g., using homogenous, small samples), resulting in crucial knowledge gaps and a limited understanding concerning empirically based risk factors for PPU. This study aimed to identify the most robust risk factors for PPU using a preregistered study design. Independent laboratories' 74 preexisting self-report data sets (N = 112,397; N = 16) were combined to identify which factors can best predict PPU using an artificial intelligence-based method (i.e., machine learning). We conducted random forest models on each data set to examine how different sociodemographic, psychological, and other characteristics predict PPU, and combined the results of all data sets using random-effects meta-analysis with meta-analytic moderators (e.g., community vs. treatment-seeking samples). Predictors explained 45.84% of the variance in PPU scores. Out of the 700+ potential predictors, 17 variables emerged as significant predictors across data sets, with the top five being (a) pornography use frequency, (b) emotional avoidance pornography use motivation, (c) stress reduction pornography use motivation, (d) moral incongruence toward pornography use, and (e) sexual shame. This study is the largest and most integrative data analytic effort in the field to date. Findings contribute to a better understanding of PPU's etiology and may provide deeper insights for developing more efficient, cost-effective, empirically based directions for future research as well as prevention and intervention programs targeting PPU. General Scientific Summary: This study suggests that the top five predictors of problematic pornography use (PPU) were frequency of use, emotional avoidance pornography use motivation, stress reduction pornography use motivation, moral incongruence, and sexual shame. These findings provide empirically based key insights to develop effective, scientifically driven prevention and intervention programs for PPU that are currently absent from the literature and health care systems. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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