KU ScholarWorks
KU ScholarWorks is the institutional repository of the University of Kansas, featuring scholarly work by KU faculty, staff and students.
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Item Cetinjski filološki dani IV(Fakultet za crnogorski jezik i književnost and Department of Slavic, German & Eurasian Studies, University of Kansas, 2025)Publication Conversion, sacrament, and assurance in the Puritan Covenant of Grace, to 1650(University of Kansas, 1963-12-31)Item DATA: Ecological niche modeling applications to infectious diseases(University of Kansas, 2025-07-10)Ecological niche modeling (ENM) is a widely used analytical approach for predicting species distributions and has been applied to study spatial epidemiology of infectious diseases by identifying potential transmission-risk areas. However, research evaluating the fundamental components and assumptions of ENM in disease systems remain limited, raising concerns about its reproducibility and transparency. To address this gap, we conducted a systematic review and evaluated articles on ENM applications to infectious diseases between 2020 and 2022. We reviewed 78 articles to extract information following a checklist provided by (Zurell et al. 2020) and summarized the information for each component (e.g., study subject, location, duration). The spatial extent of study areas varied from village to global scales, temporal duration ranged from 1 to 101 years, and the organismal levels ranged from individuals (57.7%) to populations (33.3%). Less frequently reported components included temporal autocorrelation tests (2.66%), algorithmic uncertainty (28.21%), temporal resolution (35.90%), background data (44.87%), coordinate reference system (41.02%), model performance of validation data (46.15%), and model averaging (20.51%). Our findings highlight a lack of consistency and transparency in disease ecology and biogeography studies, which may lead to misleading ENM applications in spatial epidemiology. Researchers and reviewers applying ENM to disease systems should clearly report these fundamental modeling components to ensure biologically sound and actionable health. This article outlines the best practices in modeling disease systems and identifies major gaps in the current literature.Publication Depression Screening in Adolescents with Type 1 Diabetes(University of Kansas, 2020-08-31)Introduction: This study aimed to measure the acceptability and diagnostic accuracy of commonly used depression screening measures, determine ideal cut-off scores that sensitively identify depressive disorders and suicidality, and examine demographic and type 1 diabetes (T1D) related participant characteristics that confer risk for depression in adolescents with T1D. Methods: One hundred adolescents (12-17 year-olds) completed a semi-structured diagnostic interview and both long and short versions of five depression screening measures. Measure completion time, cost, and participant ratings were summarized to assess acceptability. Descriptives, area under the receiver operating characteristic (ROC) curve analyses, and paired-sample area differences under the ROC curve were used to assess each measure’s diagnostic validity against the interview. Similar analyses were used to examine the diagnostic accuracy of single suicide screening items and depressive symptom clusters. Finally, additional risk explained by demographic and T1D-related characteristics were examined using binary logistic regressions. Results: Fifteen percent of adolescents endorsed a current depressive disorder and 15% endorsed lifetime suicidality. Measures demonstrated low sensitivity (.33-.67) to detect current depressive disorders using pre-existing cut-off scores. Adjusted cut-off scores increased sensitivity and reduced false negatives. All depression screening measures demonstrated “good” to “excellent” predictive validity, and the CDI-2 Short demonstrated significantly greater diagnostic accuracy than the PHQ-2A. Demographic and T1D-related factors explained a non-significant amount of variance (14.3%) in risk for a current depressive disorder. Conclusion: Clinics should consider using screening measures with the greatest diagnostic accuracy as identified in this study and adjusting measure cut-off scores to increase sensitivity and reduce false negatives. Additional clinically relevant information is discussed.Publication SLANT: A Starter Strategy™ for Class Participation (2nd Edition)(Center for Research on Learning, University of Kansas, 2025)Teachers have long recognized the importance of students active participation in class. Students who participate learn more and teachers who work with these students find teaching to be more rewarding. The SLANT Strategy has been designed to enable students to participate in class in appropriate and productive ways. Students who master SLANT understand why it’s important to actively participate during class and how to do so. The five steps of the strategy are: S = Sit up L = Lean forward A = Activate your thinking N = Name key information T = Track the talker Before you can light students’ intellectual fire, you must have their attention. Teaching the SLANT Strategy is not only a really good way to gain students’ attention but is also a great way to introduce students to the concept of strategies and their benefits. Within a few minutes, students can learn the five basic steps of this simple little strategy that will help them become active participants in any class discussion and better learners.