Drury University

Over the past decades, effort-based decision-making paradigms have become popular and effective tools to assess the neurocognitive mechanisms that support motivated behavior in humans and animals. These paradigms are built on a common neuroeconomic framework that conceptualizes motivation as the result of a cost-benefit decision-making process wherein organisms weigh potential rewards against the effort necessary to obtain them. However, specific effort-based choice paradigms often differ substantially in key design aspects, including the effort domain (physical vs. cognitive), the specific task used to manipulate effort, the presence or absence of rewards, punishments or other decision costs (e.g., time, probability), temporal component (i.e., probing effort/reward evaluation before or after effort exertion), and whether task demand is communicated explicitly or whether it has to be inferred through experience. This methodological heterogeneity may explain inconsistencies in the literature regarding the neural substrates underlying effort valuation and how these processes may be affected by a broad range of neuropsychiatric conditions, limiting the explanatory value of single-task studies. In this review, we provide a detailed overview of widely used effort-based decision-making paradigms, discuss associated advantages and challenges, and integrate key findings regarding neural correlates and observed changes in clinical populations. Finally, we offer practical guidance for selecting paradigms best suited to specific research questions.

Abstract Background: Access to rehabilitation services is a critical component of equitable health care delivery, yet disparities in service availability and geographic accessibility remain understudied. This study examined spatial accessibility to outpatient physical and occupational therapy services across Texas, a large and demographically diverse state, to identify regional disparities and inform workforce and policy planning. Methods: A descriptive, cross-sectional geospatial analysis was conducted using outpatient clinic location data from the Texas Health and Human Services database (2022) and population data from the 2020 U.S. Census. Clinic addresses were verified and geocoded, and accessibility was analyzed in two ways: an origin–destination cost matrix to calculate travel time to the nearest clinic, and the two-step floating catchment area (2SFCA) method to determine the accessibility index. Hot and cold spots were identified using the Getis-Ord Gi* statistic. Analyses were performed in ArcGIS Pro. Results: The study included 2,255 outpatient rehabilitation clinics across 6,896 census tracts. Travel times to the nearest clinic varied widely, with rural regions experiencing the longest travel times. The 2SFCA analysis revealed disparities, with low-accessibility clusters concentrated in rural and border regions, and high-accessibility clusters in urban metropolitan areas. Hot spot analysis confirmed statistically significant disparities across the state. Conclusions: There are substantial geographic disparities in outpatient rehabilitation access in Texas, particularly in rural and border regions. These findings underscore the need for targeted workforce placement, policy interventions, and improved transportation infrastructure to address inequities in rehabilitation access. Future research should incorporate longitudinal data and expand to additional rehabilitation professions to inform comprehensive health service planning.

Tracking emotion fluctuations in adolescents' daily lives is essential for understanding mood dynamics and identifying early markers of affective disorders. This study examines the potential of text-based approaches for emotion prediction by comparing nomothetic (group-level) and idiographic (individualized) models in predicting adolescents' daily negative affect (NA) from text features. Additionally, we evaluate different Natural Language Processing (NLP) techniques for capturing within-person emotion fluctuations. We analyzed ecological momentary assessment (EMA) text responses from 97 adolescents (ages 14-18, 77.3% female, 22.7% male, NEMA=7,680). Text features were extracted using a dictionary-based approach, topic modeling, and GPT-derived emotion ratings. Random Forest and Elastic Net Regression models predicted NA from these text features, comparing nomothetic and idiographic approaches. All key findings, interactive visualizations, and model comparisons are available via a companion web app: https://emotracknlp.streamlit.app/. Idiographic models combining text features from different NLP approaches exhibited the best performance: they performed comparably to nomothetic models in R2 but yielded lower prediction error (Root Mean Squared Error), improving within-person precision. Importantly, there were substantial between-person differences in model performance and predictive linguistic features. When selecting the best-performing model for each participant, significant correlations between predicted and observed emotion scores were found for 90.7-94.8% of participants. Our findings suggest that while nomothetic models offer initial scalability, idiographic models may provide greater predictive precision with sufficient within-person data. A flexible, personalized approach that selects the optimal model for each individual may enhance emotion monitoring, while leveraging text data to provide contextual insights that could inform appropriate interventions.

The olefination of oximes and hydrazones with ruthenium alkylidenes has recently been developed by matching substrate/metal orbital energy and polarization. We herein report detailed mechanistic investigations showing that the reaction pathway is distinct from previously established olefin-olefin metathesis reactions. The ruthenium alkylidene-mediated metathesis of olefins and hydrazones or oximes proceeds via a pathway that form intermediate ruthenium nitrides, which undergo subsequent bimolecular extrusion of molecular nitrogen. Detailed insights into the controlling features of this transformation presented herein are key to the development of future catalytic protocols for the olefination of oximes and hydrazones.

ABSTRACT Background The National Commission for Health Education Credentialing, Inc. (NCHEC) has established and updated the specific knowledge and skills necessary to become a Certified Health Education Specialist (CHES). To prepare students, it is important that coursework addresses the Areas of Responsibility (AoR); however, research examining student preparation has been limited. Purpose The purpose of this study was to assess perceived knowledge, skills, and confidence to apply selected sub-competencies from AoR I, II, IV, and VII through a community health assessment (CHA) among undergraduate health majors in Community Health courses in spring of 2021. Methods A 20 question pre and post-survey was administered to measure and compare student confidence and perceived knowledge/skills of the Eight AoR. Results There was a statistically significant difference (p < .05) between the pre and post-test measuring confidence for 38 AoR and for 31 AoR assessing perception of knowledge and skills. Discussion The findings from this study help instructors identify gaps in the course and in the preparation program. Translation to Health Education Practice This assessment of the CHA project is a useful pedagogical tool to determine if students are gaining fundamental skills in competencies needed in health education and promotion.

Team-based learning (TBL) is a type of active learning. While TBL was first used in medical instruction at the start of the 2000s, its efficacy within the physician assistant (PA) education has yet to be demonstrated. The objective of this study was to determine existing information about TBL in PA education and to demonstrate the need for further study. A mixed scoping review was undertaken. Articles were identified across the search engines CINAHL Complete, Embase, ERIC, and PubMed. Findings were then evaluated using Rayyan and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Forty-nine articles were identified across all databases, with 2 independent reviewers screening records. Ultimately, 6 studies were included. Four studies were original articles, and the other 2 were conference abstracts. Based on the limited data available, TBL was generally beneficial in all available studies; however, results should be interpreted with caution given the paucity of available data. Studies included both hybrid curriculum and TBL alone. Studies demonstrated significant findings when evaluating TBL course outcomes, examination grades, and student confidence. All articles evaluated different outcomes, so comparison between results is challenging. The strength of research from conference abstracts was limited. Further study is necessary to determine if TBL is effective in other programs. If demonstrated to be effective in PA education, TBL may be used to improve outcomes such as examination performance, course grades, long-term retention of knowledge, or student confidence.

With the fast evolving advances in technology to create immersive learner experiences in Virtual Reality (VR), there are significant opportunities for developing immersive and engaging VR Learning Environments (VRLEs) to train neurodiverse individuals. Integrating Pedagogical Agents (PAs) in these VRLEs has the potential for supporting neurodiverse learners by providing personalized, adaptive guidance tailored to their unique needs. However, a key challenge lies in ensuring the PAs can adapt effectively to the diverse circumstances of the learners. In this paper, we present a novel VRLE viz., uSucceed that leverages a Reinforcement Learning (RL)-driven PA to provide adaptive, personalized support to enhance the training experiences by tailoring the VRLE to the needs of neurodiverse learners. By designing the RL-driven PA algorithm to use Deep Q-Network, we study its capability in guiding the actions of the student in an ongoing learning basis. Simulation experiment results of the speed, accuracy and efficiency of the RL-driven PA in a VRLE demonstrate the benefits of our approach. These results build a strong base for future dynamic VRLE research that can be programmed to adapt based on the learners' experience.

The coronavirus disease has caused a worldwide pandemic impacting higher education.Scientists are gaining an understanding that the virus has a propensity to travel via a person's respiratory droplets and is more likely to spread when people are nearby.As a result, the U.S. government issued a stay-at-home order during the spring semester of 2020.The institution was one of the few schools that reopened its doors for the 2020-2021 calendar year.The shutdown and reopening drastically altered course pedagogies as the traditional seated classrooms morphed into online and hybrid or blended courses.The problem was that the transition did not align with the original instructional design.The instructional strategy became uncoupled and was no longer compatible with the learning objectives and student outcomes.A personal experience narrative was performed to describe the nature of the teaching experience as the pandemic intervened in the classroom.This paper provides the hallmarks of best practices and lessons learned when implementing online education into the structural engineering courses at a small, Midwestern liberal arts, private institution.