Profiles of Cyberpornography Use and Sexual Well-Being in Adults
Bibliographic record
Abstract
INTRODUCTION: Although findings concerning sexual outcomes associated with cyberpornography use are mixed, viewing explicit sexual content online is becoming a common activity for an increasing number of individuals. AIM: To investigate heterogeneity in cyberpornography-related sexual outcomes by examining a theoretically and clinically based model suggesting that individuals who spend time viewing online pornography form three distinct profiles (recreational, at-risk, and compulsive) and to examine whether these profiles were associated with sexual well-being, sex, and interpersonal context of pornography use. METHODS: The present cluster-analytic study was conducted using a convenience sample of 830 adults who completed online self-reported measurements of cyberpornography use and sexual well-being, which included sexual satisfaction, compulsivity, avoidance, and dysfunction. MAIN OUTCOMES MEASURES: Dimensions of cyberpornography use were assessed using the Cyber Pornography Use Inventory. Sexual well-being measurements included the Global Measure of Sexual Satisfaction, the Sexual Compulsivity Scale, the Sexual Avoidance Subscale, and the Arizona Sexual Experiences Scale. RESULTS: Cluster analyses indicated three distinct profiles: recreational (75.5%), highly distressed non-compulsive (12.7%), and compulsive (11.8%). Recreational users reported higher sexual satisfaction and lower sexual compulsivity, avoidance, and dysfunction, whereas users with a compulsive profile presented lower sexual satisfaction and dysfunction and higher sexual compulsivity and avoidance. Highly distressed less active users were sexually less satisfied and reported less sexual compulsivity and more sexual dysfunction and avoidance. A larger proportion of women and of dyadic users was found among recreational users, whereas solitary users were more likely to be in the highly distressed less active profile and men were more likely to be in the compulsive profile. CONCLUSION: This pattern of results confirms the existence of recreational and compulsive profiles but also demonstrates the existence of an important subgroup of not particularly active, yet highly distressed consumers. Cyberpornography users represent a heterogeneous population, in which each subgroup is associated with specific sexual outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".