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Digital Sexual Violence and Suicide Risk in a National Sample of Sexual Minority Adolescents.

Ankur SrivastavaJoshua A RusowSheree M SchragerRob B StephensonJeremy T Goldbach
Published in: Journal of interpersonal violence (2022)
This paper aimed to examine the association between digital sexual violence (threat to post or nonconsensual posting of sexually explicit media) and suicidal (ideation, planning, and attempt) and non-suicidal self-harm behavior. The data for the current analysis come from an online sample of sexual minority adolescents (aged 14-17) recruited from across the United States ( n  = 970). Multivariate logistic regressions were used to examine the association between digital sexual violence with suicide (ideation, planning, and attempt) and self-harm. In the sample, 9.1% of participants reported being threatened to have their sexually explicit media posted without their consent, while 6.5% reported their sexually explicit media had been posted without their consent. Threat to post sexually explicit media without consent was associated with higher odds of reporting suicidal ideation (odds ratio [OR] = 1.88), suicide plan (OR = 2.12), suicide attempt (OR = 3.56), and self-harm (OR = 1.96). While nonconsensual posting of sexually explicit media was associated with higher odds of reporting suicidal ideation (OR = 1.82) and suicide attempt (OR = 2.20). All models controlled for age, assigned sex at birth, sexual identity, and race and ethnicity. These findings underscore important considerations and future research directions. Given the associations between digital sexual violence and suicide risk among sexual minority adolescents, suicide prevention efforts with adolescents must be responsive to the needs of sexual minority adolescents and the changing landscape of sexual violence in digital spaces. Future research should examine the trajectories of digital sexual violence among adolescents and comparative analyses by demographic subgroups to better understand changes in these processes over time.
Keyphrases
  • mental health
  • young adults
  • physical activity
  • depressive symptoms
  • machine learning
  • pregnant women
  • adverse drug
  • artificial intelligence
  • drug induced