The Association between the Quantity and Severity of Pornography Use: A Meta-analysis
Bibliographic record
Abstract
Although the quantity of pornography use (QPU, i.e., frequency/time spent on pornography use) has been positively associated with the severity of pornography use (i.e., problematic pornography use, PPU), the magnitudes of relationships have varied across studies. This meta-analysis aimed to assess the overall relationships and identify potential moderating variables to explain the variation in these associations between QPU and PPU. We performed a literature search for all published and unpublished studies from 1995 to 2020 in major online scientific databases up until December 2020. Sixty-one studies were identified with 82 independent samples involving 74,880 participants. Results indicated that there was a positive, moderate relationship between QPU and PPU (r = 0.34, p < .001). The strength of relationship significantly varied across measures of PPU based on different theoretical frameworks, indicators of QPU, and sexual cultural contexts (conservative vs. permissive sexual values). Frequency was a more robust quantitative indicator of PPU than time spent on pornography use. In conservative countries, QPU showed more robust association with self-perceived PPU. Future studies are encouraged to select the measurement of PPU according to research aims and use multi-item measures with demonstrated content validity to assess pornography use. Cross-cultural (conservative/permissive) comparisons also warrant further research.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.020 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".