Cybersex use and problematic cybersex use among young Swiss men: Associations with sociodemographic, sexual, and psychological factors
Bibliographic record
Abstract
BACKGROUND AND AIMS: Cybersex use (CU) is highly prevalent in Switzerland's population, particularly among young men. CU may have negative consequences if it gets out of control. This study estimated prevalence of CU, frequency of CU (FCU), and problematic CU (PCU) and their correlates. METHODS: = 5,332, mean age = 25.45) completed a questionnaire assessing FCU and PCU, sociodemographics (age, linguistic region, and education), sexuality (being in a relationship, number of sexual partners, and sexual orientation), dysfunctional coping (denial, self-distraction, behavioral disengagement, and self-blame), and personality traits (aggression/hostility, sociability, anxiety/neuroticism, and sensation seeking). Associations were tested using hurdle and negative binomial regression models. RESULTS: At least monthly CU was reported by 78.6% of participants. CU was associated positively with post-secondary schooling (vs. primary schooling), German-speaking (vs. French-speaking), homosexuality, bisexuality (vs. heterosexuality), more than one sexual partner (vs. one), dysfunctional coping (except denial), and all personality traits except sociability, but negatively with being in a relationship (vs. not), age, and sociability. FCU was associated positively with homosexuality, bisexuality, no or more than one sexual partner, dysfunctional coping (except denial), and all personality traits except sociability, but negatively with age, being in a relationship, and sociability. PCU was associated positively with bisexuality, four or more sexual partners, dysfunctional coping, and all personality traits except sociability, but negatively with German-speaking and sociability. DISCUSSION AND CONCLUSIONS: CU should be viewed in light of its associations with sociodemographic, sexual, and psychological factors. Healthcare professionals should consider these aspects to adapt their interventions to patients' needs.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".