Associations Between Pornography Use Frequency, Pornography Use Motivations, and Sexual Wellbeing in Couples
Bibliographic record
Abstract
Pornography use is prevalent, even among partnered individuals. Although pornography use motivations represent key predictors of sexual behaviors, prior studies only assessed the associations between pornography use frequency and sexual wellbeing, with mixed results. This cross-sectional dyadic study examined the associations between partners’ individual and partnered pornography use frequency, motivations, and sexual wellbeing. Self-report data from 265 couples (Mage_men = 31.49 years, SD = 8.26; Mage_women = 29.36 years, SD = 6.74) were analyzed using an actor-partner interdependence model. Men’s greater emotional avoidance motivation was related to their own lower sexual function (β = −.24, p = .004) and greater sexual distress (β = .19, p = .012), while their higher sexual curiosity motivation was related to higher partnered sexual frequency (β = .15, p = .031), their own greater sexual satisfaction (β = .13, p = .022), sexual function (β = .16, p = .009), and lower sexual distress (β = −.13, p = .043). Women’s higher partnered pornography use frequency was associated with their own greater sexual function (β = .15, p = .034) and lower sexual distress (β = −.14, p = .012). Additionally, women’s higher individual pornography use frequency (β = .33, p < .001) and lower sexual pleasure motivation (β = −.35, p = .002) were associated with higher partnered sexual frequency. No partner effects were observed. Findings highlight that women’s pornography use frequency and each partner’s motivations might play crucial roles in couples’ sexual wellbeing.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".