Gender-Related Differences in Associations Between Sexual Abuse and Hypersexuality
Bibliographic record
Abstract
BACKGROUND: Individuals with histories of sexual abuse may be more likely to experience sexual-related problems including hypersexuality, but gender-related differences remain unclear. AIM: This online study examined sexual abuse history and hypersexuality by gender among 16,823 Hungarian adults, adjusting for age, sexual orientation, relationship status, education, employment status, and residence. METHODS: An online questionnaire on one of the largest Hungarian news portals advertised this study examining sexual activities in January 2017. 3 categorizations of age-related sexual abuse were examined: child sexual abuse (CSA) occurring at age 13 and earlier (compared to no abuse), adolescent/adult sexual abuse (AASA; compared to no abuse), and CSA and AASA (CSA/AASA; compared to one age-related category of abuse or the other). OUTCOMES: The outcome variable, hypersexuality, was examined as a continuous variable due to the low prevalence of clinical hypersexuality in this sample. 3 multivariate linear regression analyses adjusting for covariates aimed to predict hypersexuality from each category of abuse, along with gender and its interaction with each category. RESULTS: In all models, younger age, non-heterosexual sexual orientation, male gender, single relationship status, less than full-time work, and living in a capital city were associated with hypersexuality, and education was not a significant predictor. CSA, AASA, and CSA/AASA predicted hypersexuality in both men and women. There was a significant interaction between CSA/AASA and gender, such that the relationship between CSA/AASA and hypersexuality was stronger in men than in women. CLINICAL TRANSLATION: Sexual abuse at each developmental time-point may influence hypersexuality among men and women, although the cumulative impact of CSA and AASA on hypersexuality may be particularly relevant among men. STRENGTHS & LIMITATIONS: This is one of the largest studies to examine gender-related differences in the relationship between sexual abuse and hypersexuality. Nevertheless, our study is cross-sectional, and longitudinal work is needed to determine how sexual abuse affects children, adolescents, and adults throughout their lives. CONCLUSION: Developmental impacts of sexual abuse may be considered in a gender-informed fashion in order to develop and optimize effective prevention and treatment strategies for hypersexuality. Slavin MN, Blycker GR, Potenza MN, et al. Gender-Related Differences in Associations Between Sexual Abuse and Hypersexuality. J Sex Med 2020;17:2029-2038.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".