Psychotherapy for problematic pornography use: A comprehensive meta-analysis
Bibliographic record
Abstract
Introduction: Problematic pornography use (PPU) affects some individuals, causing distress and impaired functioning, and while psychotherapy is considered a first-line intervention, its efficacy remains understudied and unknown to many therapists. This review aimed to comprehensively synthesize the available evidence on psychotherapy for PPU and related problems (i.e., craving). Methods: For this meta-analytic systematic review, we conducted a systematic literature search, followed by study selection, coding, and data extraction. We then meta-analyzed the resulting studies using a random-effects model with subgroup analyses, meta-regressions, and risk of bias assessments. Results: 20 studies with 2,021 participants met the inclusion criteria. Most studies included cognitive behavioral therapy and acceptance and commitment therapy interventions. Participants receiving psychotherapy improved significantly more than controls on PPU, frequency/duration of pornography use, and sexual compulsivity, with large effect sizes, that were small for craving. Within-subject effects were also large and stable at follow-up. In addition, single-case designs meta-analyses showed clinically significant reductions in PPU, craving, and frequency/duration. We identified moderate effects for related depression symptoms. Most subgroup and meta-regression analyses adjusting for treatment and sample characteristics were not significant. Discussion: These results supports the efficacy of cognitive behavioral therapy and acceptance and commitment therapy in treating PPU and related problems. This has relevant implications for clinical practice (e.g., treating these problems with evidence-based interventions). However, these findings are limited by methodological issues, including the high risk of bias identified. To address these limitations, future research should use more rigorous methods (e.g., randomized controlled trials) and include more diverse groups.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".