Analysis of factors influencing campus bullying among junior and senior school students and construction of a nomogram model from Jiangsu Province / 中国学校卫生

ABSTRACT

Objective@#To explore the influencing factors of exposure to campus bullying among junior and senior school students, and to establish a column line diagram model for risk prediction, while providing a theoretical basis for campus bullying prevention and control in secondary schools.@*Methods@#A total of 22 034 junior and senior school students were selected via direct sampling technique from September to November 2021 in 13 cities in Jiangsu Province, China, and questionnaires were administered using the Student Health Behavior Questionnaire. The Chi squared test and multifactor Logistic regression analysis were used to derive the influencing factors of exposure to campus bullying, and a column line graph prediction model was drawn.@*Results@#A total of 540 students reported that they had experienced campus bullying, with a prevalence rate of 2.45%. Being in a non conventional family ( OR =1.30,95% CI =1.02-1.65), overweight/obesity ( OR =1.35,95% CI =1.09-1.67), scolding by parents in the past 30 days ( OR =2.27,95% CI =1.82-2.84), cigarette smoking in the past 30 days ( OR =1.54,95% CI =1.11-2.15), Internet addiction ( OR =2.03，95% CI =1.34-3.08), and depressive symptoms( OR =5.24，95% CI =4.16-6.61), all of which were positively correlated with exposure to campus bullying among junior and senior school students ( P <0.05). Furthermore, the following factors were negatively associated with junior and senior school students protection from campus bullying in female students ( OR = 0.58 , 95% CI =0.46-0.72)，senior school students ( OR =0.68，95% CI =0.54-0.83), eating breakfast sometimes ( OR =0.37，95% CI = 0.22 -0.62), and eating breakfast everyday ( OR =0.28，95% CI =0.17-0.49) ( P <0.05). The column line graph established based on the above influencing factors had an area under the curve of 0.792 (95% CI =0.769-0.815), and the calibration curve showed that the predicted value was basically the same as the measured value.@*Conclusions@#Non conventional families, overweight/obesity, male students, junior school students, scolding by parents, cigarette smoking, Internet addiction, and depressive symptoms are correlated with school bullying among middle school students. The predictive model constructed in the study can provide an effective basis to predict the risk of school bullying and facilitate the implementation of proactive interventions for junior and senior school students.