Validation of the Problematic Pornography Consumption Scale: Short Form (PPCS-6) in a Spanish Clinical Population with Gambling Disorder
Bibliographic record
Abstract
The prevalence of problematic pornography use (PPU) and its potential negative effects have raised concerns, necessitating the availability of accurate assessment tools. This study aimed to validate the Problematic Pornography Consumption Scale (PPCS-6) in a Spanish sample with gambling disorder. The sample consisted of 359 adults (92.2% men, M = 39.5 years, SD = 13.6) seeking treatment for gambling disorder. Other than the PPCS-6, various psychometrically sound instruments were used to assess variables related to PPU, gambling behavior, psychopathology, emotional dysregulation, impulsivity, and personality features. Confirmatory factor analysis and correlation coefficients were used for data analysis to examine the factor structure and assess convergent-discriminative validity of the PPCS-6. The psychometric properties of the PPCS-6 were supported in the present treatment-seeking population, showing a one-dimensional solution with good fit and internal consistency. Higher PPCS-6 scores were associated with more severe psychopathology, higher impulsivity, more emotion regulation difficulties, and lower self-directedness. Additionally, positive correlations were observed between PPCS-6 scores and specific motivations for using pornography. This study validates the Spanish version of the PPCS-6 as a reliable screening tool for assessing PPU in clinical populations, specifically in individuals with gambling disorder.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".