2 resultados para Unified Delinquency Intervention Services. Illinois.
em Duke University
Resumo:
Empirical studies of education programs and systems, by nature, rely upon use of student outcomes that are measurable. Often, these come in the form of test scores. However, in light of growing evidence about the long-run importance of other student skills and behaviors, the time has come for a broader approach to evaluating education. This dissertation undertakes experimental, quasi-experimental, and descriptive analyses to examine social, behavioral, and health-related mechanisms of the educational process. My overarching research question is simply, which inside- and outside-the-classroom features of schools and educational interventions are most beneficial to students in the long term? Furthermore, how can we apply this evidence toward informing policy that could effectively reduce stark social, educational, and economic inequalities?
The first study of three assesses mechanisms by which the Fast Track project, a randomized intervention in the early 1990s for high-risk children in four communities (Durham, NC; Nashville, TN; rural PA; and Seattle, WA), reduced delinquency, arrests, and health and mental health service utilization in adolescence through young adulthood (ages 12-20). A decomposition of treatment effects indicates that about a third of Fast Track’s impact on later crime outcomes can be accounted for by improvements in social and self-regulation skills during childhood (ages 6-11), such as prosocial behavior, emotion regulation and problem solving. These skills proved less valuable for the prevention of mental and physical health problems.
The second study contributes new evidence on how non-instructional investments – such as increased spending on school social workers, guidance counselors, and health services – affect multiple aspects of student performance and well-being. Merging several administrative data sources spanning the 1996-2013 school years in North Carolina, I use an instrumental variables approach to estimate the extent to which local expenditure shifts affect students’ academic and behavioral outcomes. My findings indicate that exogenous increases in spending on non-instructional services not only reduce student absenteeism and disciplinary problems (important predictors of long-term outcomes) but also significantly raise student achievement, in similar magnitude to corresponding increases in instructional spending. Furthermore, subgroup analyses suggest that investments in student support personnel such as social workers, health services, and guidance counselors, in schools with concentrated low-income student populations could go a long way toward closing socioeconomic achievement gaps.
The third study examines individual pathways that lead to high school graduation or dropout. It employs a variety of machine learning techniques, including decision trees, random forests with bagging and boosting, and support vector machines, to predict student dropout using longitudinal administrative data from North Carolina. I consider a large set of predictor measures from grades three through eight including academic achievement, behavioral indicators, and background characteristics. My findings indicate that the most important predictors include eighth grade absences, math scores, and age-for-grade as well as early reading scores. Support vector classification (with a high cost parameter and low gamma parameter) predicts high school dropout with the highest overall validity in the testing dataset at 90.1 percent followed by decision trees with boosting and interaction terms at 89.5 percent.
Resumo:
Background: Autism Spectrum Disorder (ASD) is a major global health challenge as the majority of individuals with ASD live in low- and middle-income countries (LMICs) and receive little to no services or support from health or social care systems. Despite this global crisis, the development and validation of ASD interventions has almost exclusively occurred in high-income countries, leaving many unanswered questions regarding what contextual factors would need to be considered to ensure the effectiveness of interventions in LMICs. This study sought to conduct explorative research on the contextual adaptation of a caregiver-mediated early ASD intervention for use in a low-resource setting in South Africa.
Methods: Participants included 22 caregivers of children with autism, including mothers (n=16), fathers (n=4), and grandmothers (n=2). Four focus groups discussions were conducted in Cape Town, South Africa with caregivers and lasted between 1.5-3.5 hours in length. Data was recorded, translated, and transcribed by research personnel. Data was then coded for emerging themes and analyzed using the NVivo qualitative data analysis software package.
Results: Nine contextual factors were reported to be important for the adaptation process including culture, language, location of treatment, cost of treatment, type of service provider, familial needs, length of treatment, support, and parenting practices. One contextual factor, evidence-based treatment, was reported to be both important and not important for adaptation by caregivers. The contextual factor of stigma was identified as an emerging theme and a specifically relevant challenge when developing an ASD intervention for use in a South African context.
Conclusions: Eleven contextual factors were discussed in detail by caregivers and examples were given regarding the challenges, sources, and preferences related to the contextual adaptation of a parent-mediated early ASD intervention in South Africa. Caregivers reported a preference for an affordable, in-home, individualized early ASD intervention, where they have an active voice in shaping treatment goals. Distrust of community-based nurses and health workers to deliver an early ASD intervention and challenges associated with ASD-based stigma were two unanticipated findings from this data set. Implications for practice and further research are discussed.