Pornography and problematic pornography use: occurrence, patterns, and associated factors in a national gender-based controlled cross-sectional study
Bibliographic record
Abstract
BACKGROUND: A study on pornography is vital due to internet accessibility, widespread pornography usage, and a lack of data, especially in non-western countries. AIM: This study estimates the occurrence of pornography use (PU), compares demographic, sexual, and psychological factors between users (PUs) and non-users, and identifies associated factors of PU based on gender. It examines problematic pornography use (PPU), comparing usage patterns between PPUs and non-PPUs, and identifies associated factors of PPU. METHODS: In 2021, an online cross-sectional nationwide survey was conducted among 1249 Iranians (865 women, 384 men) in all provinces except one, using a convenience sampling method through social media platforms. OUTCOMES: Participants were categorized into PUs and non-users based on their pornography use over the past year. PUs were further divided into PPUs and non-PPUs, using the Problematic Pornography Consumption Scale cutoff (>20). The researcher-made questions assessed patterns of pornography use, demographic characteristics, and sexual information; sexual health variables and psychological factors were evaluated by standard scales. RESULTS: PU was reported by 30.1% of participants (n = 376), including 27.5% of women and 35.9% of men. Logistic regression identified being male, shorter marriage duration, earlier age at first sex, lower religiosity, poorer sexual communication, masturbation, substance abuse, and depression as associated factors for PU. Among PUs, 13% (n = 49) were PPUs, including 10% of women and 17.1% of men. Linear regression identified the following risk factors for PPU: being male, longer marriage duration, masturbation, sexual distress, and pornography use. Conversely, having more children was a protective factor. Compared to non-PPUs, PPUs reported higher pornography consumption, the primary motivation being masturbation, greater usage among close friends, prioritizing pornography over sex with their spouse, negative effects on their sex life, and increased use during the COVID-19 pandemic. CLINICAL IMPLICATIONS: Healthcare providers should address modifiable factors related to PU/PPU through early sex education and support. Objective measurements of PPU should be prioritized over subjective perceptions, as many infrequent users feel moral incongruence. STRENGTHS AND LIMITATIONS: The study's applicability may be limited by imbalanced gender participation, recruitment of married individuals, and a small number of PPUs. However, strengths include standardized assessment tools, gender-based data collection, and anonymous sampling to enhance response accuracy in conservative contexts. CONCLUSION: Accurate pornography occurrence measurement requires clear definitions, consideration of dropout rates, and consistent time units. Strong correlations with PPU included frequent masturbation, fewer children, lower education for women, poor sexual communication, and frequent PU for men.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".