Effect of pornography use on the sexual satisfaction: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective Dissatisfaction with an inividual’s sex life is underlying factor that can lead to pornography addiction. The current research aims to understand the relationship between pornography use and sexual satisfaction.Methods The pooled correlation coefficient with 95% confidence interval was calculated using random effects. The meta-regression method was used to examine factors affecting heterogeneity between studies and Egger’s test was used to evaluate of publication bias.Results 41 studies with a total sample size of 70541 participants were included in the meta-analysis. The pooled estimate for the correlation coefficient in total, in male and in female −0.06 (95% CI: −0.09 to −0.02), −0.07 (95% CI: −0.16 to 0.02) and −0.04 (95% CI: −0.08 to −0.01). The pooled estimate of correlation coefficient was −0.04 (95% CI: −0.07 to −0.02) in cross-sectional, −0.12 (95% CI: −0.19 to −0.05) in cohort, 0.00 (95% CI: −0.15 to 0.15) in studies that used self-report questionnaire and −0.06 (95% CI: −0.08 to −0.03) in studies that used online survey. Based on the results of the meta-regression, the publication year was found to have a significant effect on heterogeneity among studies (B = 0.013, p = 0.018). However, study design, age, data collection method, quality score and sample size did not have a significant effect.Conclusions There was a significant negative correlation between pornography and sexual satisfaction and the disaggregation of results by gender also indicated this negative correlation among women. However, the relationship between pornography and sexual satisfaction was not significant in men.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".