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Sexual Agreements and Intimate Partner Violence Among Male Couples in the U.S.: An Analysis of Dyadic Data.

Akshay SharmaErin KahleStephen SullivanRob Stephenson
Published in: Archives of sexual behavior (2020)
Prior research with male couples has focused on how sexual agreements can influence relationship dynamics, sexual risk taking, and health promoting behaviors. Little is known about the association between sexual agreements and the experience or perpetration of intimate partner violence (IPV) in this population. Our study sought to evaluate these associations using dyadic data from a sample of 386 male couples residing in the U.S. Both partners independently reported on their relationship characteristics, sexual agreements, and specific acts reflecting physical, emotional, controlling, and monitoring IPV in separate surveys. Participants were more likely to have experienced IPV in the past year if they were in a relationship for ≥ 3 years versus < 3 years (aOR = 1.62, 95% CI = 1.03-2.53). Among 278 couples who had formulated sexual agreements, men who concurred with their partners on being in an "open" relationship were less likely to have experienced IPV versus those in a "closed" relationship (aOR = 0.47, 95% CI = 0.25-0.89). However, participants were more likely to have experienced IPV if their partners believed they had previously broken their sexual agreement (aOR = 2.79, 95% CI = 1.03-7.52). The verbal explicitness and duration of sexual agreements were not associated with either experiencing or perpetrating IPV in the past year. However, increasing levels of depressive symptomatology were associated with a greater likelihood of both experiencing and perpetrating IPV. Our findings highlight the need to prioritize dyadic interventions for male couples that focus on skills building around enhancing mutual communication and negotiating sexual agreements to reduce IPV.
Keyphrases
  • intimate partner violence
  • mental health
  • public health
  • physical activity
  • big data
  • electronic health record
  • artificial intelligence
  • deep learning
  • middle aged