The Risk of Bullying and Probability of Help-Seeking Behaviors in School Children: A Bayesian Network Analysis

Reporting Standards for Bayesian Network Modelling

Reproducibility is a key measure of the veracity of a modelling result or finding. In other research areas, notably in medicine, reproducibility is supported by mandating the inclusion of an agreed set of details into every research publication, facilitating systematic reviews, transparency and reproducibility. Governments and international organisations are increasingly turning to modelling approaches in the development and decision-making for policy and have begun asking questions about accountability in model-based decision making. The ethical issues of relying on modelling that is biased, poorly constructed, constrained by heroic assumptions and not reproducible are multiplied when such models are used to underpin decisions impacting human and planetary well-being. Bayesian Network modelling is used in policy development and decision support across a wide range of domains. In light of the recent trend for governments and other organisations to demand accountability and transparency, we have compiled and tested a reporting checklist for Bayesian Network modelling which will bring the desirable level of transparency and reproducibility to enable models to support decision making and allow the robust comparison and combination of models. The use of this checklist would support the ethical use of Bayesian network modelling for impactful decision making and research.

Predictive factors regarding bullying behavior in Romanian schools

Introduction

The present study investigates the phenomenon of bullying in schools in the city of Constanta, Romania.

Method

From the age point of view, we have had n = 210 12-year-old subjects and 193 13-year-old subjects, and from the biological gender point of view, there were 234 girls and 169 boys. The study's main objective was to investigate aggressive behavior in adolescents in a school context. The study is cross-sectional and aims to analyze behaviors and interpersonal relationships having as dependent variable "Conflicts in the school environment" and two independent variables, respectively "Aggressive Manifestations" and "Aggressive Behaviors," used in proving the first hypothesis, dependent variable "Verbal attacks as an aggressor" and the predictive variables "Social exclusion" and "Conflicts within the school environment" used to demonstrate the second hypothesis and the dependent variable "Bullying behavior" and the predictor variables "Acceptance of unethical behaviors," "Violation of privacy as an aggressor" and "Dissemination of information without authorization" used to demonstrate the third hypotheses.

Result

The results indicate significant correlations between aggressive behaviors and conflicts in the school environment (r = 0.596, p < 0.001), suggesting that interventions must be integrated and address the underlying causes of aggressive behaviors and associated manifestations. The association between "Conflicts in the school environment" and "Aggressive behaviors" revealed a significant correlation (r = 0.387, p < 0.001) and a moderate correlation between "Perception of conflicts" and "Aggressive manifestations" (r = 0.423, p < 0.001).

Conclusion

The conclusions emphasize the importance of understanding the complexity of aggressive behavior dynamics and predictive factors for developing effective strategies for prevention and intervention in the educational environment. As a limitation of the study, it is advisable to follow the group of subjects from a longitudinal point of view to identify changes in the behavioral manifestations of these adolescents, in a school context.

Family Functioning and Internalizing and Externalizing Problems in Gifted Children

INTRODUCTION

Although gifted children can express high cognitive skills, they can also show socioemotional difficulties. Drawing from Olson's circumplex model, the present paper assessed the role of family functioning in internalizing and externalizing problems in gifted children.

MATERIALS AND METHODS

362 mothers and their 362 gifted children were included. The unbalanced subscales of the FACES IV-disengagement, enmeshment, rigidity, and chaos-and the CBCL were administered to mothers. The children completed the WISC-IV.

RESULTS

The model predicting internalizing problems was significant and explained 5.6% of the variance. Only rigidity had an independent and positive effect on internalizing problems in gifted children over and above sociodemographic variables and QI, whereas disengagement, enmeshment, and chaos were not associated with internalizing problems. The model predicting externalizing problems was significant and explained 10% of the variance. Again, rigidity was the only variable that had an independent and positive effect on externalizing problems in gifted children over and above sociodemographic variables and QI, whereas disengagement, enmeshment, and chaos were not associated with externalizing problems in this population.

DISCUSSION

Rigid families with a low ability to change in conditions that require readjustment appear to increase the risk of both internalizing and externalizing problems in gifted children. Although further studies are needed to support these preliminary findings, parental support interventions aimed at increasing flexibility appear to be useful.

Chains of tragedy: The impact of bullying victimization on mental health through mediating role of aggressive behavior and perceived social support

OBJECTIVE

Bullying is a worldwide concern for its devastating consequences. The current study focused on bullying victims, examining the effects of being bullied on mental health and the chain of mediating mechanisms among adolescents. Specifically, this study attempts to explain the relationship between bullying victimization and mental health from the perspective of maladaptive behavior and perceived social support.

METHODS

A total of 3,635 adolescents responded to questions on bullying victimization, aggressive behavior, perceived social support, and mental health measurements including anxiety, depression, and subjective well being scale combined.

RESULTS

(1) Bullying victimization was significantly correlated with aggressive behavior, perceived social support, and mental health, including anxiety, depression, and subjective well being. (2) Bullying victimization not only negatively predicts mental health levels but also has an indirect impact on mental health through three pathways: a separate mediating effect on aggressive behavior, a separate mediating effect on perceived social support, and a chain mediating effect on both.

CONCLUSION

The present results demonstrate that maladaptive behavior by bullying victims can lead to changes in their perceived social support and mental health problems. Violence begets violence and provides no constructive solutions, instead, produces a tragic chain of victimization. Further implications are discussed accordingly.

Application of a Bayesian Network Learning Model to Predict Longitudinal Trajectories of Executive Function Difficulties in Elementary School Students

Executive function is the mental ability to modulate behavior or thinking to accomplish a task. This is developmentally important for children's academic achievements and ability to adjust to school. We classified executive function difficulties (EFDs) in longitudinal trajectories in Korean children from 7 to 10 years old. We found predictors of EFDs using latent class growth analysis and Bayesian network learning methods with Panel Study data. Three types of latent class models of executive function difficulties were identified: low, intermediate, and high EFDs. The modeling performance of the high EFD group was excellent (AUC = .91), and the predictors were the child's gender, temperamental emotionality, happiness, DSM (Diagnostic and Statistical Manual of Mental Disorders) anxiety problems, and the mother's depression as well as coparenting conflict recognized by the mother. The results show that using latent class growth analysis and Bayesian network learning are helpful in classifying the longitudinal EFD patterns in elementary school students. Furthermore, school-age EFD is affected by emotional problems in parents and children that continue from early life. These findings can support children's development and prevent risk by preclassifying children who may experience persistent EFD and tracing causes.