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The University of Missouri Kansas City (UMKC) is an urban research university with a special emphasis on fostering diversity. In 2021, the Center for Advancing Faculty Excellence created a Faculty Fellows program to develop comprehensive resources and support in four areas: teaching and learning, service and engagement, research and creativity, and faculty life and leadership. This article examines one of the projects under the service and engagement area was to collaborate with deans, chairs, and faculty members across campus to facilitate a better understanding of student outcomes data. Working in collaboration with the Vice Provosts for Curriculum and Assessment, Institutional Effectiveness, and Faculty Affairs, a gap analysis was conducted using student data to determine if discrepancies among demographic groups existed. For this project, gap analysis is defined as a method to determine if a difference exists between what an academic program expects a student to be able to perform and the reality of the student's actual performance. The disaggregation of data, that is, separating data into smaller units, was the foundation of conducting the gap analysis. This separation can be done by gender, ethnicity, country of origin, financial aid status, geographic location, socioeconomic status, or disability status.

Articles published in 22 counseling journals from 2010-2019 were compared on 26 author and article characteristics. Women now constitute majorities of authorship and counseling journals are publishing larger proportions of research articles, 63% of which used quantitative approaches and about 11% of which studied counseling interventions.

INTRODUCTION Recovery from alcohol use disorder (AUD) or other substance use disorder (SUD) is an evolving concept. This article reviews youth recovery, as little is known about the prevalence, pathways, [...]

Ihis paper revisits Jeffrey Jerome Cohen's Monster Theory: Reading Culture (1996) and maps theories of disability representation onto Cohen's seven monster theses. This connection between Cohen's work and disability is not new, but there is yet to be an explicit chronicle of this relationship. Starting first with the idea that monster films have always been fascinating because, for better or worse, it is the one genre that has been explicit about disability, this paper argues that the horror genre may be the only genre willing to explore disability within a political context. 'Ihis paper gives specific examples of where to find the monster in popular culture, and how those monsters, despite not being labelled explicitly as disabled, serve to bring discourses of marginalization into mainstream popular culture. Each of Cohen's theses is explained and related both to classic tropes of disability representation as well as possible avenues for addressing more nu·anced depictions of disability in future analyses, with the potential to track the monster for notions of inclusion and integration-but also acceptance and fluidity in identity-in real life. [ABSTRACT FROM AUTHOR]

This article explores the statistical methodologies used in demonstration and effectiveness studies when the treatments are applied across multiple settings. The importance of evaluating and how to evaluate these types of studies are discussed. As an alternative to standard methodology, the authors of this article offer an empirical binomial hierarchical Bayesian model as a way to effectively evaluate multisite studies. An application of using the Bayesian model in a real-world multisite study is given. (Contains 1 figure and 2 tables.)

The purpose of this study is to develop a text clustering–based analysis of COVID-19 research articles. Owing to the proliferation of published COVID-19 research articles, researchers need a method for reducing the number of articles they have to search through to find material relevant to their expertise. The study analyzes 83,264 abstracts from research articles related to COVID-19. The textual data are analysed using singular value decomposition (SVD) and the expectation–maximisation (EM) algorithm. Results suggest that text clustering can both reveal hidden research themes in the published literature related to COVID-19, and reduce the number of articles that researchers need to search through to find material relevant to their field of interest. [ABSTRACT FROM AUTHOR]

Credit scoring is the process of determining whether applicants should be granted a financial loan. When a financial institution decides to create a credit scoring model for all applicants, the institution only has the known good/bad loan outcomes for accepted applicants. This causes inherent bias in the model We address a gap in the reject inference literature by developing a methodology to simulate rejected applicants. A methodology to illustrate how to simulate rejected applicants must be developed so that the reject inference techniques can be studied and appropriate reject inference techniques can be selected. This study uses a peer-to-peer financial loan information from accepted and rejected financial loan applicants to perform Monte Carlo simulation of rejected applicants. Using simulated data, the researchers compare the performance of three widely used reject inference techniques. [ABSTRACT FROM AUTHOR]

We compared the accuracy of 3 data-mining models, neural-network, decision-tree, and logistic-regression, in predicting the 5-year survival of patients with colorectal cancer. The database consisted of patient demographics, pathologic features, and levels of expression of 2 biomarkers (p53 and Bcl-2). All 3 methods demonstrated acceptable accuracy, from 64% to 70%. The neural-network model had the best specificity (80%) and accuracy (70%) but lowest sensitivity (59%). Both logistic-regression and decision-models demonstrated comparable sensitivity (72%).