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EFFECT OF TRAUMATIC BRAIN INJURY ON QUALITY OF LIFE
Get PDFTraumatic brain injury (TBI) results from external events, such as falls and motor vehicle accidents leading to brain damage. This study investigated quality of life (QoL) in individuals with TBI and its relation to demographic and clinical variables, including gender, age, educational level, time since TBI onset, fatigue, depression and cognitive function. Twelve individuals with TBI participated in this cross-sectional study. Quality of life was assessed using Quality of Life after Brain Injury and ASHA Quality of Communication Life Scale measures. Fatigue was measured using the Fatigue Severity Scale, depression was measured with the Geriatric Depression Scale, and cognitive function was measured by the Cognitive Linguistic Quick Test. Results indicated a significant reduction in QoL, particularly in the domains of daily life and autonomy, social relationships and self-perception. Survivors with fatigue, depression and cognitive impairments reported lower QoL than those without these conditions. These findings underscore the importance of addressing these factors in rehabilitation to optimize recovery and improve overall well-being. Targeted interventions aimed at managing fatigue and depression, and improving cognitive function may enhance QoL outcomes in individuals with TBI
Exploring Effective Weight Loss Interventions Among Three FQHC-Based Clinical Programs in Central and Southern Illinois
Get PDFBackground: Obesity affects 1 in 3 United States adults is associated with several chronic diseases. Disparities in obesity are seen in many vulnerable populations such as low-income groups, racial and ethnic minorities, and rural residents. Although primary care is often presented as a solution to the obesity crisis, management of obesity is complex and numerous challenges in helping patients achieve weight loss are still present. The U.S. Preventive Services Task Force recommended in 2018 that primary care providers screen for obesity and offer obese patients to intensive, multicomponent behavioral obesity treatment. However, there is little research involving primary care physicians or practices providing intensive behavioral counseling. Our study compares three independently created Federally Qualified Health Care (FQHC)-based weight loss clinical programs in order to further address gaps in medical knowledge around best practices for successful weight loss programming in the FQHC setting.
Methods: A retrospective chart review was conducted on a total 581 patients that were referred to any one of the three Center for Family Medicine FQHC clinics from August 2017 to August 2021. Patient demographics, weight change, percent weight change, medical history, weight loss medications, and several other variables were collected during the review. Comparisons between clinics was done using Fisher’s Exact test or Analysis of Variance. Logistic regression models were used to determine the association between clinically significant weight change and study variables.
Results: A majority of patients were female (83.4%), white (71%), and had private insurance (37%). Across all sites, approximately 34% of the patient population showed clinically significant weight loss during the study period. There was a significant difference in percent weight change among the weight loss clinics (p=0.0024). Regression models showed that patients from Clinic 3 were more likely to show clinically significant weight loss compared to Clinic 2 (OR 1.85, CI: 1.03-3.35; p=0.0406), and black patients were less likely to show clinically significant weight loss compared to white patients (OR 0.57, CI: 0.35-0.93; p=0.023).
Conclusion: This study highlights that despite a difference in composition of weight loss clinic, resources available, and patient populations, one in three patients were able to achieve clinically meaningful weight loss. Also, when there is intentionality and management, this study shows that other FQHCs can be successful in offering weight loss clinics, even when resources and staff are limited
Real-Time Fault Detection and Classification in Radial Power Distribution Network using DNNs
No full textElectric power distribution systems are evolving rapidly with increased integration ofdistributed generation (DG), flexible load, and bidirectional power flows. While this is a new norm of modern power systems and advantageous in many ways, it also comes with its own share of added complexity and problems that need due attention. One such example is of the protection systems in the power grid, which are conventionally designed to function with a fixed direction of current flow and pre-defined logic of operations. With added variability and uncertainties discussed above, the conventional protection and fault detection systems in the power grid sometimes fall short in identifying and classifying faults with the desired accuracy. Inspired by the wide range of applications of neural networks in complex operations, this thesis proposes a data-driven, lightweight Deep Neural Network (DNN) based solution methodology for accurate and real-time fault detection and classification in radial distribution networks with DG penetration. This involves simulating the modified IEEE 4 bus distribution network in ePHASORsim solver based on OPAL-RT hardware simulator with numerous fault scenarios to generate a dataset with respective fault level, train and optimize different DNN architectures (MLP and 1-D CNN) based on the dataset, and deploy the most efficient trained DNN for real-time fault detection and classification in the radial power distribution network. Different distribution network parameters like fault resistance, connected load, etc., were varied over a wide range in the simulated scenario to emulate the complexity and anomalies of real-world cases. Likewise, the feature extraction and point of detection or control were chosen such that they closely match the natural position of conventional power systems protection relays and sensing elements. The performance of the DNN models was closely monitored during the training in terms of accuracy and inference time by using accuracy percentage, confusion matrix, etc., as the metrics. Likewise, the hyperparameters of the DNN models were tuned using Bayesian Optimization while co-optimizing the accuracy and inference time using the Pareto front. Once trained, the models acted as independent black boxes capable of analyzing the power distribution network features—current and voltage—in real time and making inferences on the go. The results section highlights the proposed methodology’s performance in terms of accuracy and inference time, and confirms its effectiveness, robustness, and applicability in modern radial power distribution networks
No Night So Dark
No full textNo Night So Dark is a lyric elegy that confronts grief, addiction, and the struggle to endure after loss. Moving between Midwestern landscapes and New York City, these poems strive for survival, a love for the dead that becomes living prayer, and a new appreciation for the body, the flesh, and the people we still have left
SOCIO-TECHNICAL SYSTEM APPROACHES TO UNDERSTAND SMART DIVIDE AND BROADBAND RESILIENCE: INSIGHTS FROM SOUTHERN ILLINOIS COMMUNITIES
No full textThe COVID-19 pandemic exposed critical vulnerabilities in digital infrastructure systems, highlighting how communities with limited broadband access and slow adoption of smart services struggled to maintain essential functions. As educational institutions transitioned to remote learning, healthcare shifted to telehealth platforms, and government services moved online, communities lacking robust digital infrastructure found themselves unable to access basic services that had become digitally dependent overnight. This crisis highlighted the emergence of the “smart divide,” a multifaceted concept representing disparities in smart infrastructure penetration, variations in smart service adoption, and entrenched digital inequalities across communities. This study examined the underlying factors of this divide quantitatively from the lens of a socio-technical system (STS), emphasizing the interwoven roles of social and technological factors. A theoretical model explaining the interplay between digital infrastructures, social infrastructures, socio-economic factors and smart divide is developed and evaluated using structural equation modeling (SEM). The model tests three relationships: insufficient digital connectivity and disadvantaged human factors proportionately correlate to smart divide occurrence, and social infrastructure acts as a mediator, reducing the negative impact of these two. A total of 262 data points were collected via mail survey from two remote towns in southern Illinois- Carbondale and Cairo. Findings reveal that human factors and social infrastructure have a significant influence on the smart divide, accounting for 38.2% of its variance. Human factors had the strongest positive effect (β = 0.371, p \u3c 0.05). However, digital connectivity showed no significant effect, highlighting that access alone is insufficient to bridge the smart divide without addressing broader social dimensions. Social infrastructure showed a significant negative direct effect (β = -0.273, p \u3c 0.05) which underscores its role in reducing the smart divide. However, social infrastructure did not significantly mediate the effects of digital connectivity or human factors as originally hypothesized.Moreover, broadband infrastructure can play a crucial role in bridging the smart divide. The COVID-19 pandemic highlighted how deeply society relies on broadband connectivity and how vulnerable that infrastructure can be to disruption. Building resilient broadband systems ensures reliable access during future crises. This study argues that the resilience of the broadband infrastructure system should consider the interconnected nexus of social and technical dimensions which is broadly framed as “socio-technical resilience”. Socio-technical resilience refers to the ability of the interconnected social and technical components of a system to recover from or respond positively to crises. While this concept has been extensively explored within the context of physical infrastructures such as power systems, transportation networks, and organizational contexts, it has not yet been extended to broadband infrastructure systems. This study aims to examine how resilience emerges from the integration of social and technical aspects of a broadband infrastructure system drawing on Actor-Network Theory (ANT). Using case studies from Carbondale and Cairo, this study examines how broadband infrastructures interacted with broadband-dependent communities to counteract external impacts and shocks during the COVID-19 pandemic. Findings reveal that Carbondale demonstrated stronger resilience thanks to its strong technological and social support. Conversely, Cairo exhibited partial resilience, struggling primarily due to technological gaps despite having similar social infrastructure and digital capabilities as Carbondale
Jenifer Michaels. A History.
No full textJenifer Michaels was a legendary subcultural star within the gay liberation film milieu of 1970s Los Angeles. Taking three paths to approaching and interpreting Michaels’s filmic and archival image, this article is structured by an analysis of Michaels’s documentary appearances, her performance in sex films, and her statements published in a textual interview. Working against what I call historiographic gentrification, the article utilizes archival and micro-historical methods to think through Michaels’s subcultural stardom as a nexus for complicating biography, documentary, and the politics of veracity
Once Upon a Time: A Kinesthetic Approach to Teaching Evidence
No full textThis Article describes and evaluates a kinesthetic, simulation-based approach to teaching Evidence at Southern Illinois University Simmons Law School. Departing from the traditional Langdellian, case-method model, one section of Evidence requires students to memorize and apply the Federal Rules of Evidence through a series of five scaffolded mini-trials built around fractured fairy tales and nursery rhymes. Drawing on the MacCrate Report, Bloom’s taxonomy, and Vygotsky’s theory of scaffolding, we situate this pedagogy within the broader movement toward experiential legal education and argue that Evidence—because of its centrality to litigation practice—is an ideal doctrinal course in which to integrate trial advocacy and skills training.
The Article explains how the course is structured, including the formation of “law firms,” rotating student judges, and progressively more complex trial problems that require students to move from simple recall of rules to higher-order skills such as application, analysis, evaluation, and creation. We present both quantitative and qualitative data from student evaluations and surveys, as well as the teaching assistant’s observations and personal testimony, to demonstrate that this kinesthetic model increases engagement, deepens understanding of evidentiary doctrine, and improves students’ confidence and performance in courtroom settings, externships, and mock trial competitions.
Ultimately, we contend that embedding episodic, low-stakes trials in a required Evidence course offers a powerful way to help students internalize the Federal Rules of Evidence, develop professional identity, and practice lawyering skills in a supportive, scaffolded environment. We conclude by suggesting how this method can be adapted to other doctrinal courses, challenging law schools to reconsider the sharp divide between “doctrinal” and “skills” instruction in favor of a more integrated, practice-ready curriculu
The Impact of Treatment Modality, Security, and Risk Level Matching on Socioecological Treatment Change among Youth Adjudicated for a Sexual Offense
No full textThe effective rehabilitation of youth adjudicated for a sexual offense (YASO) relies upon successfully implementing the Risk-Need-Responsivity (RNR) model, a theoretically and empirically supported approach to rehabilitation used in juvenile justice settings. In line with RNR, research suggests that reductions in recidivism are most likely to occur when: (1) treatment intensity and security placement matches risk level (youth at lower risk to reoffend receive lower dosages of treatment in the least restrictive environment), and (2) treatment is individualized to target criminogenic needs (needs that, if changed during an intervention, result in reduced reoffending). Less is known about how matching or mismatching treatment type (e.g., family therapy), security placement (e.g., residential vs. community), and risk level (lower or higher risk to reoffend) affects changes in socioecological treatment needs for YASOs. Socioecological treatment needs are factors within the YASO’s environment that serve as a strength or area of concern (i.e., perceived family relationship support, home environment stability, community resource support) for general well-being. Six multilevel models were run to examine three research questions assessing the effect of risk-placement match or mismatch and needs-service match or mismatch on socioecological need score changes across a 12-month period among a sample of 299 YASOs. Results indicate that whether a YASO was appropriately placed or not, based on their risk level, did not contribute to significantly different changes in socioecological intervention treatment needs over time. In contrast, whether YASOs baseline socioecological treatment needs were appropriately met with the presence or absence of family therapy significantly predicted changes in socioecological need scores over time. Results demonstrated that there were no differences in socioecological needs over time based on how risk-placement and need-intervention co-occurred. The findings of this study have bolstered the empirical support for adhering to the RNR model in a YASO population. To date, YASOs are frequently subjected to unique mandated management provisions as they are viewed as a homogeneous higher-risk group in need of a one-size-fits all sex offense specific treatment (Chaffin et al., 2016). However, years of research has consistently suggested otherwise and slowly shifted this perspective, indicating that YASOs are a heterogeneous group with diverse treatment needs and are unlikely to reoffend, and if they do, it is more likely to be a non-sexual offense. Consistent with previous literature, the results of this study suggest that YASO’s are a heterogeneous group. Thus, the results of this study can inform individualized treatment and management practices for YASOs to reduce potential harm, improve treatment success, and thereby reduce recidivism and unnecessary costs while increasing public safety