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Trends in bullying victimization by gender among U.S. high school students.

Nancy M H PontesCynthia G AyresCarla LewandowskiManuel C F Pontes
Published in: Research in nursing & health (2018)
This research used four consecutive waves of data from the National Youth Risk Behavior Survey (YRBS) conducted by the Centers for Disease Control (CDC), to estimate linear time trends by gender in the prevalence of school and electronic bullying victimization among U.S. high school students (N = 61,042). Dependent variables were student self-reported school bullying victimization and electronic bullying victimization during the previous 12 months. Independent variables used to estimate multiple logistic regression models by gender were survey year, race/ethnicity, and grade level. Results showed the prevalence of school bullying increased significantly among females from 2009 (21.2%) to 2015 (24.8%), linear trend OR = 1.08 [1.04, 1.12]; and decreased significantly among males from 2009 (18.7%) to 2015 (15.8%), linear trend OR = 0.93 [0.89, 0.98]. Prevalence of electronic bullying was unchanged between 2011 to 2015 among both male and female students. Asian race, relative to White race, was associated with significantly lower rates of both school and electronic bullying victimization among females, but not males. The incidence of school and electronic bullying victimization was significantly lower among Black and Hispanic students, but not among multiple-race students, regardless of student gender. Healthy People 2020 set a goal to reduce school bullying victimization 10% by 2019. As of 2015, school bullying victimization decreased significantly among males (16% decrease); it significantly increased among females (17% increase). Future research should explore underlying factors related to these divergent trends, and develop effective strategies to reverse the alarming rise in female school bullying victimization.
Keyphrases
  • high school
  • risk factors
  • mental health
  • physical activity
  • cross sectional
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  • deep learning
  • cell cycle
  • data analysis