The Associations Between Social, Behavioral, and Health Risk Factors on a High School Student’s Decision to Pursue Post High School Plans

Background: Due to the relationship between SES and health, pursuing post high-school plans can lead to better future health outcomes for the student. The current paper assesses how behavioral and health risk factors, and family and social support, effect a student’s decision to pursue post high school plans. Methods: Data from the Youth Behavioral Component of the 2007 Connecticut School Health Survey were analyzed. Composite measures of exposure to/participation in violent behavior, mental and physical health, family/social support and substance abuse were created. The effects of these domains on the decision to pursue post high-school plans were assessed using logistic regression. Data were stratified by socioeconomic status. Results: Low SES students were more likely than high SES students to be doubtful for post high-school plans. Cocaine abuse emerged as the risk factor that put low SES students at the highest odds of not pursuing post high-school plans, followed by involvement in violent/aggressive behavior, and receiving less family/social support than their peers. Similar findings regarding violence and family/social support were found in the high SES group. Findings regarding substance abuse in the high SES group were not statistically significant. Discussion: Prevention programs regarding violence and substance abuse may have the added benefit of increasing the likelihood that high school students will make post high school plans. Preventing cocaine use among low SES students may be of particular importance. Violence prevention measures should be tailored to the target group. Adequate family/social support emerged as an encouraging factor for post high school plans.

Computer Simulation of Crowd Dynamics and Destructive Crowd Behavior

The social processes that lead to destructive behavior in celebratory crowds can be studied through an agent-based computer simulation. Riots are an increasingly common outcome of sports celebrations, and pose the potential for harm to participants, bystanders, property, and the reputation of the groups with whom participants are associated. Rioting cannot necessarily be attributed to the negative emotions of individuals, such as anger, rage, frustration and despair. For instance, the celebratory behavior (e.g., chanting, cheering, singing) during UConn’s “Spring Weekend” and after the 2004 NCAA Championships resulted in several small fires and overturned cars. Further, not every individual in the area of a riot engages in violence, and those who do, do not do so continuously. Instead, small groups carry out the majority of violent acts in relatively short-lived episodes. Agent-based computer simulations are an ideal method for modeling complex group-level social phenomena, such as celebratory gatherings and riots, which emerge from the interaction of relatively “simple” individuals. By making simple assumptions about individuals’ decision-making and behaviors and allowing actors to affect one another, behavioral patterns emerge that cannot be predicted by the characteristics of individuals. The computer simulation developed here models celebratory riot behavior by repeatedly evaluating a single algorithm for each individual, the inputs of which are affected by the characteristics of nearby actors. Specifically, the simulation assumes that (a) actors possess 1 of 5 distinct social identities (group memberships), (b) actors will congregate with actors who possess the same identity, (c) the degree of social cohesion generated in the social context determines the stability of relationships within groups, and (d) actors’ level of aggression is affected by the aggression of other group members. Not only does this simulation provide a systematic investigation of the effects of the initial distribution of aggression, social identification, and cohesiveness on riot outcomes, but also an analytic tool others may use to investigate, visualize and predict how various individual characteristics affect emergent crowd behavior.