Secondary school inclusion rates: The relationship between the training and beliefs of school site principals and the implementation of inclusion

Programs require strong support and guidance from those in leadership positions to ensure proper implementation (Fullen, 2001). Consequently, school site principals must rely on the training they have received to support them in making appropriate decisions. It is the school site principal’s leadership that is pivotal in the success of students with disabilities (DiPaola & Walther-Thomas, 2003; Monteith, 2000). In fact, the principal has a moral obligation to provide an environment that supports social justice in schools (Grogan & Andrews, 2002). The inclusion of students with disabilities does just that—it ensures that these students are not segregated to a “separate but equal” education. This study utilized a participant survey to collect data on principals’ beliefs and training in special education. This information was compared to the percentage of time students with disabilities spent with their non-disabled peers in the principals’ respective schools. An analysis was conducted to identify if a linear relationship exists between the selected variables and the inclusion percentages. Open-ended questions were included in the original survey which allowed for a thematic analysis of the responses. These responses were utilized to allow participants to further express their thoughts on the identified variables. Results indicated that there were no statistically significant relationships identified between the beliefs and training of secondary school site principals and the percentage of time that their students in special education spend with their non-disabled peers. Although the original research questions were not supported, further post hoc analysis indicated that the results obtained did support that the principals believed inclusion had a social benefit to students. Additional investigation into the academic benefits of inclusion is still needed. In addition, principals who indicated that they had some type of training in special education indicated a higher percentage that the individual student should be the focal point when making placement decisions. These results support the need for further research in the area of principal preparation programs and their relationships to the daily practice of school site principals.

Network construction and graph theoretical analysis of functional language networks in pediatric epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.^

A State and Territorial Survey Regarding Utilization of Environmental Health Shelter Assessments during Disasters, and a Secondary Analysis of Available Shelter Assessment Data

Disasters are complex events characterized by damage to key infrastructure and population displacements into disaster shelters. Assessing the living environment in shelters during disasters is a crucial health security concern. Until now, jurisdictional knowledge and preparedness on those assessment methods, or deficiencies found in shelters is limited. A cross-sectional survey (STUSA survey) ascertained knowledge and preparedness for those assessments in all 50 states, DC, and 5 US territories. Descriptive analysis of overall knowledge and preparedness was performed. Fisher’s exact statistics analyzed differences between two groups: jurisdiction type and population size. Two logistic regression models analyzed earthquakes and hurricane risks as predictors of knowledge and preparedness. A convenience sample of state shelter assessments records (n=116) was analyzed to describe environmental health deficiencies found during selected events. Overall, 55 (98%) of jurisdictions responded (states and territories) and appeared to be knowledgeable of these assessments (states 92%, territories 100%, p = 1.000), and engaged in disaster planning with shelter partners (states 96%, territories 83%, p = 0.564). Few had shelter assessment procedures (states 53%, territories 50%, p = 1.000); or training in disaster shelter assessments (states 41%, 60% territories, p = 0.638). Knowledge or preparedness was not predicted by disaster risks, population size, and jurisdiction type in neither model. Knowledge: hurricane (Adjusted OR 0.69, 95% C.I. 0.06-7.88); earthquake (OR 0.82, 95% C.I. 0.17-4.06); and both risks (OR 1.44, 95% C.I. 0.24-8.63); preparedness model: hurricane (OR 1.91, 95% C.I. 0.06-20.69); earthquake (OR 0.47, 95% C.I. 0.7-3.17); and both risks (OR 0.50, 95% C.I. 0.06-3.94). Environmental health deficiencies documented in shelter assessments occurred mostly in: sanitation (30%); facility (17%); food (15%); and sleeping areas (12%); and during ice storms and tornadoes. More research is needed in the area of environmental health assessments of disaster shelters, particularly, in those areas that may provide better insight into the living environment of all shelter occupants and potential effects in disaster morbidity and mortality. Also, to evaluate the effectiveness and usefulness of these assessments methods and the data available on environmental health deficiencies in risk management to protect those at greater risk in shelter facilities during disasters.

Land-cover detection and landscape structure analysis in the Pachitea Basin, Peruvian Amazon

Classification procedures, including atmospheric correction satellite images as well as classification performance utilizing calibration and validation at different levels, have been investigated in the context of a coarse land-cover classification scheme for the Pachitea Basin. Two different correction methods were tested against no correction in terms of reflectance correction towards a common response for pseudo-invariant features (PIF). The accuracy of classifications derived from each of the three methods was then assessed in a discriminant analysis using crossvalidation at pixel, polygon, region, and image levels. Results indicate that only regression adjusted images using PIFs show no significant difference between images in any of the bands. A comparison of classifications at different levels suggests though that at pixel, polygon, and region levels the accuracy of the classifications do not significantly differ between corrected and uncorrected images. Spatial patterns of land-cover were analyzed in terms of colonization history, infrastructure, suitability of the land, and landownership. The actual use of the land is driven mainly by the ability to access the land and markets as is obvious in the distribution of land cover as a function of distance to rivers and roads. When considering all rivers and roads a threshold distance at which disproportional agro-pastoral land cover switches from over represented to under represented is at about 1km. Best land use suggestions seem not to affect the choice of land use. Differences in abundance of land cover between watersheds are more prevailing than differences between colonist and indigenous groups.

Network Construction and Graph Theoretical Analysis of Functional Language Networks in Pediatric Epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient’s extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.