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Background: Bullying is a global public health problem with severe adverse effects on behavioral health. Understanding the predictors of victimization by bullying is essential for public policy initiatives to respond to the problem effectively. In addition to traditional in-person bullying, electronic bullying has become more prevalent due to increasing social interaction and identity formation in virtual communities. This study aims to determine the predictors of in-school and electronic bullying. Methods: We employed multivariable logistic regression to analyze a nationally representative sample of 17,232 high school students in the United States, the 2021 Youth Risk Behavior Surveillance System national component. The survey was conducted during the COVID-19 pandemic, from September through December 2021. The factors examined included sociodemographic characteristics (age, gender, race), appearance (obesity), physically active lifestyles (being physically active, spending a long time on digital games), and risk-taking behavior (using marijuana). Results: Our results indicated that sociodemographic characteristics were strong predictors of being bullied in school and electronically. Being obese is more likely to result in bullying in school (AOR = 1.32, p = 0.003) and electronically (AOR = 1.30, p = 0.004). Adolescent students showing marijuana use had higher odds of being bullied in school (AOR = 2.15, p < 0.001) and electronically (AOR = 1.81, p < 0.001). While spending a long time on digital devices raises the risk of being electronically bullied (AOR = 1.25, p = 0.014), being physically active is not associated with being bullied. Neither of the two lifestyle factors was associated with in-school bullying. Conclusions: Interventions addressing violence among adolescents can benefit from empirical evidence of risk factors for bullying victimization in high school.
Background: Bullying is a global public health problem with severe adverse effects on behavioral health. Understanding the predictors of victimization by bullying is essential for public policy initiatives to respond to the problem effectively. In addition to traditional in-person bullying, electronic bullying has become more prevalent due to increasing social interaction and identity formation in virtual communities. This study aims to determine the predictors of in-school and electronic bullying. Methods: We employed multivariable logistic regression to analyze a nationally representative sample of 17,232 high school students in the United States, the 2021 Youth Risk Behavior Surveillance System national component. The survey was conducted during the COVID-19 pandemic, from September through December 2021. The factors examined included sociodemographic characteristics (age, gender, race), appearance (obesity), physically active lifestyles (being physically active, spending a long time on digital games), and risk-taking behavior (using marijuana). Results: Our results indicated that sociodemographic characteristics were strong predictors of being bullied in school and electronically. Being obese is more likely to result in bullying in school (AOR = 1.32, p = 0.003) and electronically (AOR = 1.30, p = 0.004). Adolescent students showing marijuana use had higher odds of being bullied in school (AOR = 2.15, p < 0.001) and electronically (AOR = 1.81, p < 0.001). While spending a long time on digital devices raises the risk of being electronically bullied (AOR = 1.25, p = 0.014), being physically active is not associated with being bullied. Neither of the two lifestyle factors was associated with in-school bullying. Conclusions: Interventions addressing violence among adolescents can benefit from empirical evidence of risk factors for bullying victimization in high school.
Amongst school-aged children and youth, bullying is a significant problem warranting further investigation. The current study sought to investigate the influence of the COVID-19 pandemic waves and school closures on the bullying experiences of 22,012 children aged 4–18-years-old who were referred and assessed at mental health agencies in Ontario, Canada. Individual, familial, and mental health variables related to bullying experiences were also investigated. Data were collected from January 2017 to February 2022. The pre-pandemic period of study included January to June 2017, September 2018/2019 to June 2019/2020. The pandemic period was divided into categories of remote learning (17 March 2020 to 30 June 2020, 8 January 2021 to 16 February 2021, 12 April 2021 to 30 June 2021) and in-person learning (remaining pandemic dates). The summer holidays pre-pandemic were in July–August 2017, 2018, 2019 and during the pandemic they were in July–August 2020 and 2021. Logistic regressions were conducted to analyze data. Findings related to COVID-19 showed bullying rates to be lower during the pandemic when compared to pre-pandemic levels (bullied others during pandemic in school: OR = 0.44, CI = 0.34–0.57; victim of bullying during pandemic in school: OR = 0.41, CI = 0.33–0.5). Furthermore, bullying rates were lower during the pandemic periods when schools were closed for in-person learning (bullied others during pandemic remote: OR = 0.62, CI = 0.45–0.85; victim of bullying during pandemic remote: OR = 0.24, CI = 0.17–0.34). Children who lived in lower income areas, experienced home life challenges, exhibited mental health difficulties, or had behavioural concerns were more likely to be involved in bullying experiences. Finally, classroom type and school program impacted the child’s likelihood of bullying others or being bullied. These findings further our understanding of the impact of school closures on children’s mental health and behaviour during the pandemic. Public health and policy implications such as bullying prevention, supervision, and conflict management are discussed.
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