Pornography and problematic pornography use: occurrence, patterns, and associated factors in a national gender-based controlled cross-sectional study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».