This study used a system dynamics methodology to identify correlation and nonlinear feedback structures among factors affecting adolescent cyberbullying victims (CV) in Korea and to construct and verify a simulation model.

Factors affecting CV were identified by reviewing a theoretical background in existing literature and referencing various statistical data. Related variables were identified through content validity verification by an expert group, after which a causal loop diagram (CLD) was constructed based on the variables. A stock-flow diagram (SFD) using Vensim Professional 7.3 was used to establish a CV model.

Based on the literature review and expert verification, 22 variables associated with CV were identified and the CLD was prepared. Next, a model was developed by converting the CLD to an SFD. The simulation results showed that the variables such as negative emotions, stress levels, high levels of conflict in schools, parental monitoring, and time spent using new media had the strongest effects on CV. The model's validity was verified using equation check, sensitivity analysis for timestep and simulation with 4 CV adolescent.

The system dynamics model constructed in this study can be used to develop intervention strategies in schools that are focused on counseling that can prevent cyberbullying and assist in the victims’ recovery by formulating a feedback structure and capturing the dynamic changes observed in CV. To prevent cyberbullying, it is necessary to develop more effective strategies such as prevention education, counseling and treatment that considers factors pertaining to the individual, family, school, and media.

The purpose of this study was to develop a system dynamics model for adolescent obesity in Korea that could be used for obesity policy analysis.

On the basis of the casual loop diagram, a model was developed by converting to stock and flow diagram. The Vensim DSS 5.0 program was used in the model development. We simulated method of moments to the calibration of this model with data from The Korea Youth Risk Behavior Web-based Survey 2005 to 2013. We ran the scenario simulation.

This model can be used to understand the current adolescent obesity rate, predict the future obesity rate, and be utilized as a tool for controlling the risk factors. The results of the model simulation match well with the data. It was identified that a proper model, able to predict obesity probability, was established.

These results of stock and flow diagram modeling in adolescent obesity can be helpful in development of obesity by policy planners and other stakeholders to better anticipate the multiple effects of interventions in both the short and the long term. In the future we suggest the development of an expanded model based on this adolescent obesity model.

In this study a system dynamics methodology was used to identify correlation and nonlinear feedback structure among factors affecting unplanned extubation (UE) of ICU patients and to construct and verify a simulation model.

Factors affecting UE were identified through a theoretical background established by reviewing literature and preceding studies and referencing various statistical data. Related variables were decided through verification of content validity by an expert group. A causal loop diagram (CLD) was made based on the variables. Stock & Flow modeling using Vensim PLE Plus Version 6.0b was performed to establish a model for UE.

Based on the literature review and expert verification, 18 variables associated with UE were identified and CLD was prepared. From the prepared CLD, a model was developed by converting to the Stock & Flow Diagram. Results of the simulation showed that patient stress, patient in an agitated state, restraint application, patient movability, and individual intensive nursing were variables giving the greatest effect to UE probability. To verify agreement of the UE model with real situations, simulation with 5 cases was performed. Equation check and sensitivity analysis on TIME STEP were executed to validate model integrity.

Results show that identification of a proper model enables prediction of UE probability. This prediction allows for adjustment of related factors, and provides basic data do develop nursing interventions to decrease UE.