Interdisciplinary

Post-Deployment analysis: Traumatic Brain Injury, Post-Traumatic Stress Disorder, and Memory loss in Post 9/11 Veterans
 

Rocio Ojeda-Barajas
Sacramento State University

 
Two of the most common medical conditions experienced by veterans of Post 9/11 are, Traumatic Brain Injury at 20% (Swan 2020) and post-traumatic stress disorder (PTSD) at 23% (Fulton, 2020).  Previous studies show that veterans tend not to seek medical treatment (Stecker, 2013). This allows TBI and PTSD to go untreated, resulting in an increase in mental health disorders (Hoge, 2004). The hypothesis for this research study asks, how does the presence of a Traumatic Brain Injury, post-traumatic stress disorder, and Memory Loss impact a veteran's ability to learn following Post 9/11 deployment? Exploring their medical challenges such as TBI and PTSD, is crucial to understanding the needs of our veterans' post deployment on veterans’ memory and ability to learn post-deployment. This research will contribute to the current literature on Post 9/11 veterans. It is meant to inform society, influence policy conversations, and ensure veterans have access to resources and support. Recruitment for this study was through several California UC’s and Junior Colleges, social media platforms, and Veteran Resource Offices. This study used a Chi Test to look at correlations between different variables. The data gathered showed 90% of Post 9/11veterans reported having experienced symptoms of PTSD at some point. The data for a traumatic brain injury showed no correlation for memory loss. This study acknowledges several limitations therefore, the reporting for this survey does not represent the voice for the majority of Post 9/11 veterans who were deployed. Future direction, a more robust sample.

Factors Contributing to Executive Function in Young Adults

Lindy Stevenson
Harding University

Executive functions (EF) are skills used to complete desired behaviors and inhibit undesired ones. These skills are necessary for everyday life, and even more so in higher education and the workplace. This study examined the combined effect of sleep and exercise on five facets of EF in young adults enrolled in undergraduate or graduate courses. It also examined how perceived effort and performance in college classes interact with EF. The first five hypotheses studying sleep and exercise were analyzed using a 2x2 between-groups ANOVA. The data divided participants into those averaging fewer than 7 hours of sleep a night and those averaging at least 7 hours a night. Exercise was divided into yes/no, depending on the frequency participants reported. Data for the dependent variable was collected using the Short Executive Function Scale (Karr, 2024) and were divided into subskills. The scores for planning, inhibition, working memory, shifting, and emotional control were based on confidence in those areas. Participants in this study did not differ significantly in sleep amount or exercise with respect to EF scores across the five subskills. The last five hypotheses were analyzed using one-way ANOVA tests. Students categorized themselves based on their perceived effort and success in their courses. Those who reported trying and succeeding in their courses had higher mean scores in planning and inhibition than those whose effort and success varied. This study was limited by reliance on self-reported data. Future research could examine the interactions among these factors and other subskills of EF.

Bias in AI-Assisted Resume Screening Tools: A Meta Analysis

Ana Sofia Garcia Garibay
University of Texas at Arlington

As organizations increasingly rely on artificial intelligence (AI) to evaluate job applicants, ensuring the fairness of these models has become important. Prior research has acknowledged the potential for AI-assisted screening tools to produce biased outcomes across demographic groups (O’Connor & Liu, 2023). Thus, methods have been developed to detect bias in AI-powered resume screening tools. However, findings across studies remain inconsistent, and conditions under which disparities are likely to emerge are not well understood. This meta-analysis synthesizes data from audits, experiments, and field studies to estimate the extent to which AI models used for resume screening, ranking, shortlisting, or callback decisions produce biased outputs based on applicants’ gender. Study characteristics, including AI model type, human involvement, resume equivalence across demographic groups, demographic signal type and strength, occupation, and selection stage, were also coded to explore potential sources of variation across studies. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, seven databases were searched to identify eligible studies. Included studies (n = 22) examined large language models, natural language processing systems, machine-learning algorithms, or other AI-supported tools applied to real, synthetic, or audit resumes. Reported statistics will be converted to a common effect-size metric, when sufficient information is available, and synthesized in R using meta-analytic procedures. Available outcomes included binary selection decisions, continuous screening scores or rankings, and model-based estimates reported as unstandardized regression coefficients. This meta-analysis will clarify the magnitude and variability of demographic disparities in AI-supported resume screening and describe methodological and contextual characteristics of the available evidence.