Properties of the Problematic Pornography Consumption Scale (PPCS-18) in community and subclinical samples in China and Hungary
Bibliographic record
Abstract
Several scales assessing problematic pornography use (PPU) are available. However, in most previous studies, primarily nonclinical and Western samples were used to validate these scales. Thus, further research is needed to validate scales to assess problematic pornography use across diverse samples, including subclinical populations. The aim of the present study was to examine and compare the psychometric properties of the PPCS-18 in Hungarian and Chinese community samples and in subclinical men. A sample of Chinese community men (N1 = 695), a sample of subclinical men who were screened for PPU using the Brief Pornography Screen (N2 = 4651), and a sample of Hungarian community men (N3 = 9395) were recruited to investigate the reliability and validity of the PPCS-18. Item-total score correlation, confirmatory factor analyses, reliability, and measurement invariance tests showed that the PPCS-18 yielded strong psychometric properties among Hungarian and Chinese community men and indicated potential utility in the subclinical men. The network analytic approach also corroborates that the six factors of the PPCS-18 can reflect the characteristic of the participants from different cultural contexts, and participants from community and subclinical populations. In sum, the PPCS-18 demonstrated high generalizability across cultures and community and subclinical men.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".