Nguyen Tran H, Shah Gulzar H, Kaur Ravneet, Muzamil Maham, Ikhile Osaremhen, Ayangunna Elizabeth
Department of Health Management, Economics & Policy, School of Public Health, Augusta University, Augusta, GA 30912, USA.
Department of Health Policy and Community Health, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA.
Children (Basel). 2024 Jun 28;11(7):788. doi: 10.3390/children11070788.
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.
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).
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, = 0.003) and electronically (AOR = 1.30, = 0.004). Adolescent students showing marijuana use had higher odds of being bullied in school (AOR = 2.15, < 0.001) and electronically (AOR = 1.81, < 0.001). While spending a long time on digital devices raises the risk of being electronically bullied (AOR = 1.25, = 0.014), being physically active is not associated with being bullied. Neither of the two lifestyle factors was associated with in-school bullying.
Interventions addressing violence among adolescents can benefit from empirical evidence of risk factors for bullying victimization in high school.
欺凌是一个全球性的公共卫生问题，对行为健康有严重的不利影响。了解欺凌受害的预测因素对于有效应对该问题的公共政策举措至关重要。除了传统的面对面欺凌外，由于虚拟社区中社交互动和身份形成的增加，电子欺凌也变得更加普遍。本研究旨在确定校内欺凌和电子欺凌的预测因素。
我们采用多变量逻辑回归分析了美国17232名高中生的全国代表性样本，即2021年青少年风险行为监测系统的全国部分。该调查在2021年9月至12月的新冠疫情期间进行。所考察的因素包括社会人口学特征（年龄、性别、种族）、外貌（肥胖）、积极的生活方式（进行体育活动、长时间玩电子游戏）以及冒险行为（使用大麻）。
我们的数据表明，社会人口学特征是在校内和电子方面遭受欺凌的有力预测因素。肥胖更有可能导致在校内（优势比=1.32，P=0.003）和电子方面（优势比=1.30，P=0.004）遭受欺凌。使用大麻的青少年学生在校内（优势比=2.15，P<0.001）和电子方面（优势比=1.81，P<0.001）遭受欺凌的几率更高。虽然长时间使用电子设备会增加遭受电子欺凌的风险（优势比=1.25，P=0.014），但进行体育活动与遭受欺凌无关。这两个生活方式因素均与校内欺凌无关。
针对青少年暴力的干预措施可受益于高中欺凌受害风险因素的实证证据。
