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"I Pulled Them Apart and Told Them to Stop": A Mixed-Methods Examination of Bystander Behavior, Preparedness, and Emotional Reaction.

Deborah M CasperTricia H WitteEmily GibsonEmily McCulley
Published in: Journal of interpersonal violence (2022)
The goals of this mixed-methods study were to examine self-reported behavior of bystanders who intervened in specific situations of potential sexual violence and physical dating violence, to explore their sense of preparedness to intervene, and to assess bystanders' emotional reactions to their self-reported action or inaction when witnessing potential sexual and dating violence. The participants ( n = 553, 65.2% female, 76% freshmen, M age = 18.7 years), responded to a sequence of questions pertaining to witnessing and intervening in specific potentially dangerous situations, emotional reactions to their action or inaction, and preparedness. Bystander participants also provided narrative responses describing their behavior. We used joint display analyses to integrate, analyze, and interpret the qualitative and quantitative data. Of the 553 participants, 38% witnessed "a man talking to a woman and she looked uncomfortable," 27% witnessed "someone taking an intoxicated person up to their room," and 39% witnessed "someone grabbing or pushing their boyfriend or girlfriend"; of those who witnessed, the percentage of those who intervened was 42%, 25%, and 19%, respectively. Bystander behavior involved one of 5Ds: distract, direct, delegate, distance, diffuse, or a combination. Although most bystanders did not get involved, most (94.6%) reported that they felt prepared to intervene. For those who intervened, most reported feeling positive about their action; however, most who did not intervene reported feeling negative about their inaction. Implications for college bystanders and bystander education programs are discussed.
Keyphrases
  • mental health
  • public health
  • healthcare
  • physical activity
  • electronic health record
  • high resolution
  • human health
  • intimate partner violence
  • machine learning
  • climate change
  • mass spectrometry
  • electron transfer