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Nomogram predicting bullying victimization in adolescents.

Jianping Lv1, Hui Ren2, Xinmeng Guo1

  • 1Department of Social Medicine and Health Management, School of Public Health of Jilin University, No. 1163 Xinmin Street, Changchun 130021, China.

Journal of Affective Disorders
|February 17, 2022
PubMed
Summary

This study developed a nomogram to predict adolescent bullying victimization risk using machine learning. The tool effectively identifies at-risk youth for targeted prevention strategies.

Keywords:
AdolescentBullying victimizationNomogramPredictionValidation

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Area of Science:

  • Adolescent Health
  • Public Health
  • Machine Learning in Medicine

Background:

  • Bullying victimization poses significant risks to adolescent well-being.
  • Accurate prediction of risk factors is crucial for effective intervention.

Purpose of the Study:

  • To construct a cross-sectional study predicting bullying victimization risk in adolescents.
  • To identify key predictors and develop a predictive tool.

Main Methods:

  • Recruited 17,365 Chinese adolescents using stratified random cluster sampling.
  • Employed logistic regression, Lasso regression, and machine learning models.
  • Developed and validated a nomogram using internal and external datasets.

Main Results:

  • Identified grade, gender, peer/family violence, BMI, family structure, depressive symptoms, and internet addiction as significant predictors.
  • The nomogram demonstrated good predictive performance with Area Under Curve values of 0.749 (internal) and 0.755 (external).

Conclusions:

  • The developed nomogram is a valuable tool for assessing adolescent bullying victimization risk.
  • Facilitates identification of high-risk groups for targeted preventive measures by healthcare professionals.