Identification and comprehensive characterization of moral disapproval and behavioral dysregulation-based pornography-use profiles across 42 countries
Bibliographic record
Abstract
Background and aims: The Moral Incongruence Model of Pornography Use proposes that pornography-use-related problems may be present due to problematic pornography use (PPU) and/or moral disapproval (MD) of pornography use. Despite some supporting empirical evidence, no study has tested the presence of different pornography-use profiles based on individuals' behavioral dysregulation (i.e., PPU) and moral values concerning pornography use. The generalizability of previous findings to diverse populations has also been limited given the scarcity of studies conducted outside of Western countries. Methods: Using data from the International Sex Survey (42 countries, N = 66,994; Mage = 32.16 years, SD = 12.27), we conducted latent profile analysis to identify pornography-use profiles based on individuals' frequency of use, MD, and PPU. The profiles were compared along a wide range of pornography-use-related, sexuality-related, and psychological correlates. Results: Six pornography-use profiles were identified, including two increased risk groups (i.e., Increased risk of PPU without MD and Increased risk of PPU with some MD). Several factors differentiated between the increased risk vs. no/low risk profiles (e.g., relatedness satisfaction) as well as between the two increased risk profiles (e.g., religiosity). Apart from behavioral dysregulation, moral values concerning pornography use played an important role in distinguishing pornography-use profiles and demonstrated the importance of inquiring about MD when working with individuals with pornography-use-related problems. Conclusion: Findings also support recent calls for better-integrated sex therapy and sexual medicine perspectives into pornography-use-related problems research and care.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".