Symptoms of problematic pornography use among help-seeking male adolescents: Latent profile and network analysis
Bibliographic record
Abstract
Background and aims: Little data exist on exploring the subgroups and characteristics of problematic pornography use (PPU) in help-seeking adolescents. The aims of the study were to classify the subgroups among help-seeking male adolescents, explore their similarities and differences, and uncover their core symptoms. Methods: A total of 3,468 Chinese male adolescents (Mage = 16.64 years, SD = 1.24) who were distressed about their pornography use were recruited. The Problematic Pornography Consumption Scale, the Brief Pornography Screen Scale, and Moral Disapproval of Pornography Use were used to classify them. The General Health Questionnaire, the Pornography Craving Questionnaire, and the Sexual Compulsivity Scale were used to investigate participants' negative consequence related to their pornography use; and the Online Sexual Activity Questionnaire (OSAs) and time spent on pornography use every week were considered as quantitative indicators. Results: Help-seeking male adolescents could be divided into 3 profiles, namely, self-perceived problematic (SP, n = 755), impaired control (IC, n = 1,656), and problematic use groups (PPU, n = 1,057). Frequency of OSAs was important for the identification of SP individuals, while negative consequences were more effective in identifying individuals with objective dysregulated behavior. Salience and mood modification were shared by all groups; however, in addition to this, the SP and PPU groups also showed withdrawal symptoms. Discussion and conclusion: This study's results provide support for the presence of different profiles of help-seeking individuals and information on potential intervention targets among adolescents which is lacking in the literature.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".