Semen Arousal: Its Prevalence, Relationship to HIV Risk Practices, and Predictors among Men Using the Internet to Find Male Partners for Unprotected Sex
Bibliographic record
Abstract
PURPOSE: This paper examines the extent to which men who use the Internet to find other men for unprotected sex are aroused by semen. It also looks at the relationship between semen arousal and involvement in HIV risk practices, and the factors associated with higher levels of semen arousal. METHODS: 332 men who used any of 16 websites targeting unprotected sex completed 90-minute telephone interviews. Both quantitative and qualitative data were collected. A random sampling strategy was used. Semen arousal was assessed by four questions asking men how much they were turned on by the way that semen smelled, tasted, looked, and felt. RESULTS: 65.1 % of the men found at least one sensory aspect of semen to be "fairly" or "very" arousing, compared to 10.2% being "not very" or "not at all" aroused by all four sensory aspects of semen. Multivariate analysis revealed that semen arousal was related to greater involvement in HIV risk practices, even when the impact of other salient factors such as demographic characteristics, HIV serostatus, and psychological functioning was taken into account. Five factors were found to underlie greater levels of semen arousal: not being African American, self-identification as a sexual "bottom," being better educated, being HIV-positive, and being more depressed. CONCLUSIONS: Being aroused by the sensory aspects of giving or receiving semen is commonplace amongst men in this high-risk population. Semen arousal was related closely to involvement in risk practices, indicating a need for HIV intervention programs to address this phenomenon in this population.
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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 | 0.003 |
| 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 | 0.001 |
| 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".