Validation of the Sexual Experience Survey-Short Form Revised Using Lesbian, Bisexual, and Heterosexual Women's Narratives of Sexual Violence.
Sasha N CananKristen N JozkowskiJacquelyn Wiersma-MosleyHeather Blunt-VintiMindy BradleyPublished in: Archives of sexual behavior (2019)
Lesbian and bisexual women have high rates of sexual violence compared to heterosexual women, yet prevalence rates vary widely across studies. The Sexual Experience Survey-Short Form Revised (SES-SFV) is the most commonly used method of measuring sexual assault and rape prevalence, but it has not been validated in this high-risk population of lesbian and bisexual women. The current study assessed a modified form of the SES-SFV utilizing a five-step, mixed-methods approach. Women (N = 1382) who identified as lesbian (31%), bisexual (32%), and heterosexual (31%) completed an online survey disseminated through Qualtrics Online Survey Company to a national audience. All types of non-consensual behaviors (non-penetrative, oral, vaginal, and anal) and nearly all perpetration tactics in the original SES-SFV emerged inductively in our qualitative data. Using quantitative data, lesbian and bisexual victims endorsed each perpetration tactic in the SES-SFV at comparable rates to heterosexual victims. SES-SFV's false-positive categorization was minimal. However, the original SES-SFV did not capture some common experiences that participants described in their open-ended narratives. The SES-SFV satisfactorily assesses sexual assault and rape experiences in lesbian, bisexual, and heterosexual women. Possible additions and deletions to the SES-SFV are presented alongside discussion of managing comprehensiveness and participant fatigue.
Keyphrases
- mental health
- polycystic ovary syndrome
- men who have sex with men
- pregnancy outcomes
- hiv positive
- cervical cancer screening
- intimate partner violence
- hiv testing
- cross sectional
- breast cancer risk
- adipose tissue
- randomized controlled trial
- healthcare
- high resolution
- mass spectrometry
- big data
- pregnant women
- machine learning
- quality improvement
- depressive symptoms
- physical activity
- health information