Problematic pornography use across countries, genders, and sexual orientations: Insights from the International Sex Survey and comparison of different assessment tools
Bibliographic record
Abstract
BACKGROUND AND AIMS: Problematic pornography use (PPU) is a common manifestation of the newly introduced Compulsive Sexual Behavior Disorder diagnosis in the 11th edition of the International Statistical Classification of Diseases and Related Health Problems. Although cultural, gender- and sexual orientation-related differences in sexual behaviors are well documented, there is a relative absence of data on PPU outside Western countries and among women as well as gender- and sexually-diverse individuals. We addressed these gaps by (a) validating the long and short versions of the Problematic Pornography Consumption Scale (PPCS and PPCS-6, respectively) and the Brief Pornography Screen (BPS) and (b) measuring PPU risk across diverse populations. METHODS: ) = 32.4 years, standard deviation = 12.5], a study across 42 countries from five continents, we evaluated the psychometric properties (i.e. factor structure, measurement invariance, and reliability) of the PPCS, PPCS-6, and BPS and examined their associations with relevant correlates (e.g. treatment-seeking). We also compared PPU risk among diverse groups (e.g. three genders). RESULTS: The PPCS, PPCS-6, and BPS demonstrated excellent psychometric properties [for example, comparative fit index = 0.985, Tucker-Lewis Index = 0.981, root mean square error of approximation = 0.060 (90% confidence interval = 0.059-0.060)] in the confirmatory factor analysis, with all PPCS' inter-factor correlations positive and strong (rs = 0.72-0.96). A total of 3.2% of participants were at risk of experiencing PPU (PPU+) based on the PPCS, with significant country- and gender-based differences (e.g. men reported the highest levels of PPU). No sexual orientation-based differences were observed. Only 4-10% of individuals in the PPU+ group had ever sought treatment for PPU, while an additional 21-37% wanted to, but did not do so for specific reasons (e.g. unaffordability). CONCLUSIONS: This study validated three measures to assess the severity of problematic pornography use across languages, countries, genders, and sexual orientations in 26 languages: the Problematic Pornography Consumption Scale (PPCS, and PPCS-6, respectively), and the Brief Pornography Screen (BPS). The problematic pornography use risk is estimated to be 3.2-16.6% of the population of 42 countries, and varies among different groups (e.g. genders) and based on the measure used.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".