Predicting dropout of male perpetrators from the Cognitive Self Change

This study aims to use pre-treatment assessment scores to predict the dropout of 103 incarcerated male violent perpetrators undertaking a long term aggression programme, namely the Cognitive Self Change Programme (CSCP), in six English prisons. A hierarchy of best predictors of attrition in this sample is developed. Results found eight out of the 46 assessment variables analysed had a significant association with treatment dropout. Further to this Discriminant Function analysis predicted group membership with 80% accuracy, successfully distinguishing perpetrators who dropped out of the programme from those who completed it. The findings support the use of identifying risk factors pre-treatment to predict dropout and offer a practical way to identify group members likely to drop out of the CSCP in addition to identifying markers for programme improvement. The need for further research to increase our understanding of the underlying causal explanations that link specific assessment items to treatment dropout is discussed

Building a Grad Nation: Progress and Challenge in Ending the High School Dropout Epidemic

This fourth annual update on America's high school dropout crisis shows that for the first time the nation is on track to meet the goal of a 90 percent high school graduation rate by the Class of 2020 -- if the pace of improvement from 2006 to 2010 is sustained over the next 10 years. The greatest gains have occurred for the students of color and low-income students most affected by the dropout crisis. Many schools, districts and states are making significant gains in boosting high school graduation rates and putting more students on a path to college and a successful career. This progress is often the result of having better data, an understanding of why and where students drop out, a heightened awareness of the consequences to individuals and the economy, a greater understanding of effective reforms and interventions, and real-world examples of progress and collaboration. These factors have contributed to a wider understanding that the dropout crisis is solvable.While progress is encouraging, a deeper look at the data reveals that gains in graduation rates and declines in dropout factory high schools occurred unevenly across states and subgroups of students (e.g. economically disadvantaged, African American, Hispanic, students with disabilities, and students with limited English proficiency). As a result, large "graduation gaps" remain in many states among students of different races, ethnicities, family incomes, disabilities and limited English proficiencies. To repeat the growth in graduation rates in the next ten years experienced in the second half of the last decade, and to ensure progress for all students, the nation must turn its attention to closing the graduation gap by accelerating progress for student subgroups most affected by the dropout crisis.This report outlines the progress made and the challenges that remain. Part 1: The Data analyzes the latest graduation rates and "dropout factory" trends at the state and national levels. Part 2: Progress and Challenge provides an update on the nation's shared efforts to implement the Civic Marshall Plan to reach the goal of at least a 90 percent high school graduation rate for the Class of 2020 and all classes that follow. Part 3: Paths Forward offers recommendations on how to accelerate our work and achieve our goals, with all students prepared for college and career. The report also offers "snapshots" within schools, communities, and organizations from Orlando to Oakland that are making substantial gains in boosting high school graduation rates

Stressors and turning points in high school and dropout : a stress process, life course framework

High school dropout is commonly seen as the result of a long-term process of failure and disengagement. As useful as it is, this view has obscured the heterogeneity of pathways leading to dropout. Research suggests, for instance, that some students leave school not as a result of protracted difficulties but in response to situations that emerge late in their schooling careers, such as health problems or severe peer victimization. Conversely, others with a history of early difficulties persevere when their circumstances improve during high school. Thus, an adequate understanding of why and when students drop out requires a consideration of both long-term vulnerabilities and proximal disruptive events and contingencies. The goal of this review is to integrate long-term and immediate determinants of dropout by proposing a stress process, life course model of dropout. This model is also helpful for understanding how the determinants of dropout vary across socioeconomic conditions and geographical and historical contexts

Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: a methodological proof-of-concept study

There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients’ dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.This work was supported by the German Research Foundation National Institute (DFG, Grant nos. LU 660/8-1 and LU 660/10-1 to W. Lutz). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding author had access to all data in the study and had final responsibility for the decision to submit for publication. Dr. Hofmann receives financial support from the Alexander von Humboldt Foundation (as part of the Humboldt Prize), NIH/NCCIH (R01AT007257), NIH/NIMH (R01MH099021, U01MH108168), and the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative. (LU 660/8-1 - German Research Foundation National Institute (DFG); LU 660/10-1 - German Research Foundation National Institute (DFG); Alexander von Humboldt Foundation; R01AT007257 - NIH/NCCIH; R01MH099021 - NIH/NIMH; U01MH108168 - NIH/NIMH; James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative)Accepted manuscrip