Are sexual functioning problems associated with frequent pornography use and/or problematic pornography use? Results from a large community survey including males and females
Bibliographic record
Abstract
There is much debate regarding whether pornography use has positive or negative associations with sexuality-related measures such as sexual functioning problems. The present study aimed to examine differential correlates between quantity (frequency of pornography use–FPU) and severity (problematic pornography use–PPU) of pornography use with respect to sexual functioning problems among both males and females. Multi-group structural equation modeling was conducted to investigate hypothesized associations between PPU, FPU, and sexual functioning problems among males and females (N = 14,581 participants; females = 4,352; 29.8%; Mage=33.6 years, SDage=11.0), controlling for age, sexual orientation, relationship status, and masturbation frequency. The hypothesized model had excellent fit to the data (CFI = 0.962, TLI = 0.961, RMSEA = 0.057 [95% CI = 0.056-0.057]). Similar associations were identified in both genders, with all pathways being statistically significant (p < .001). PPU had positive, moderate associations (βmales=0.37, βfemales=0.38), while FPU had negative, weak associations with sexual functioning problems (βmales=-0.17, βfemales=-0.17). Although FPU and PPU had a positive, moderate association, they should be assessed and discussed separately when examining potential associations with sexuality-related outcomes. Given that PPU was positively and moderately and FPU negatively and weakly associated with problems in sexual functioning, it is important to consider both PPU and FPU in relation to sexual functioning problems.
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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 | 0.004 |
| 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 | 0.001 |
| 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".