High-Frequency Pornography Use May Not Always Be Problematic
Bibliographic record
Abstract
BACKGROUND: Previously, variable-centered analytic approaches showed positive, weak-to-moderate associations between frequency of pornography use (FPU) and problematic pornography use (PPU). However, person-centered studies are sparse in the literature, and these could provide insight into whether there are individuals who use pornography frequently and do not experience problems or whether there are individuals with comparable high-frequency use who differ on reported experiencing of negative consequences. AIM: The aims of the present study were (i) to identify profiles of pornography use based on FPU and PPU by applying a person-centered analytic approach and (ii) to examine whether the identified profiles could be distinguished based on theoretically relevant demographic and psychological constructs. METHODS: Latent profile analyses were conducted on 3 nonclinical samples recruited from general websites and a pornography site (study 1: N = 14,006; study 2: N = 483; study 3: N = 672). RESULTS: Results were consistent across all studies. 3 distinct pornography-use profiles emerged: nonproblematic low-frequency pornography use (68-73% of individuals), nonproblematic high-frequency pornography use (19-29% of individuals), and problematic high-frequency use (3-8% of individuals). Nonproblematic and problematic high-frequency-use groups showed differences in several constructs (ie, hypersexuality, depressive symptoms, boredom susceptibility, self-esteem, uncomfortable feelings regarding pornography, and basic psychological needs). CLINICAL TRANSLATION: FPU should not be considered as a sufficient or reliable indicator of PPU because the number of people with nonproblematic high-frequency use was 3-6 times higher than that with problematic high-frequency use. These results suggest that individuals with PPU use pornography frequently; however, FPU may not always be problematic. STRENGTHS & LIMITATIONS: Self-report cross-sectional methods have possible biases that should be considered when interpreting findings (eg, underreporting or overreporting). However, the present research included 3 studies and involved large community samples and visitors of a pornography website. The present study is the first that empirically investigated pornography-use profiles with a wide range of correlates using both severity of PPU and FPU as profile indicators on specific and general samples. CONCLUSION: The present study is a first step in the differentiated examination of pornography-use profiles, taking into consideration both PPU and FPU, and it provides a foundation for further clinical and large-scale studies. Different psychological mechanisms may underlie the development and maintenance of FPU with or without PPU, suggesting different treatment approaches. Therefore, the present results may guide clinical work when considering reasons for seeking treatment for PPU. Bőthe B, Tóth-Király I, Potenza MN, et al. High-Frequency Pornography Use May Not Always Be Problematic. J Sex Med 2020;17:793-811.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".