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Acceptability and Competency of Motivational Interviewing (MI) Training and MI Spirited Communication in an Undergraduate and Graduate Nursing Program
No full textProblem- There is a move towards patient-centered care in nursing and Motivational Interviewing (MI) has been one approach to accomplish positive communication. While the use of MI has been increasing in healthcare settings, undergraduate and graduate nursing programs have been slow to incorporate it into their curricula.
Aims- This study aimed to determine the acceptability and MI competency for the use of MI Spirited Communication (MISC) among undergraduate and graduate nursing students at a university in the Southeast region of the U.S. Methods- The design of this pilot study was a descriptive, quasi-experiment with no control group. The study was a one-group posttest survey method of acceptability and assessment of competency videos evaluated by the MITI 4.2.1 scale to determine competency following MI training. Participants were recruited through email and those who returned their consent to participate were enrolled in an online platform and attended four hours of MI coaching by the MI trainer.
Analysis- Data was entered into an SPSS® statistical data file and cleaned. Missing data was assessed for both the degree and pattern. Descriptive statistics were computed for all major study variables. MI competency was evaluated using the MITI 4.2.1 competency assessment.
Results- Participants included a total of nine nursing students: four undergraduate and five graduate students. All participants (n=9) reported MI to be acceptable for use in their nursing education and future practice. MITI aggregate scores were 2.73 and demonstrated the movement towards or at competency
Analytics Insights from Text: Machine Learning, AI, and Sentiment Analysis on Beige Books
Full text linkBusiness analytics is about drawing actionable insights from data. These distinct but connected essays represent a novel approach to explore how natural language processing (NLP) advances and machine learning can transform unstructured text data into actionable conclusions. Essay 1 provides a broad framework. Essay 2 strengthens the sentiment analysis with the most recent artificial intelligence methodologies for capturing nuanced sentiment in complex texts. Essay 3 applies those insights to forecast recessions using topics that can be readily interpreted and applied.
The research demonstrates how these methodologies can be applied to enhance understanding of the same dataset, Beige Books. Published by the Federal Reserve eight times yearly, the documents provide anecdotal impressions of current economic conditions from stakeholders representing diverse sectors and geographic regions. NLP quantifies the rich and timely information shared in their perspectives.
Each essay approaches the task from a distinct analytical angle (code and data for each essay is available at https://github.com/ces2222/bbFinal). Essay 1, “Textual Analysis of Beige Books to Predict Regional Macroeconomic Changes,” calculates sentiment measures using a lexical method and key topics based on an unsupervised process. Sentiment features are used in a random forest model to predict growth in a region during a month as measured by the State Coincident Index. Results show an AUC of .79, indicating promising relevance of Beige Book sentiment.
Essay 2, “Large Language Models Predictions of Economic Sentiment Based on Beige Books,” draws upon the emerging AI tool of large language models (LLMs) to comprehend texts in a human-like way that potentially exceeds previous sentiment analysis methods. A BERT (Bidirectional Encoder Representations from Transformers) LLM model is fine-tuned on human-labeled data, classifying tone in a Beige Book. The model developed, which is dubbed BeigeSage, compares favorably against leading LLMs like GPT and Llama in its ability to classify Beige Book sentiment. Comparisons are made on the performance and efficiency of fine-tuned, closed-source, and open-source LLMs, highlighting potential low-cost applications for non-proprietary AI models.
Essay 3, “Economic Forecasting with Interpretable Topic Models: Evidence from Beige Books,” aspires to overcome a common problem with topic modeling in business research: Unsupervised methods create topics that reflect statistical relationships between words, but a lack of interpretability limits practical application. To overcome this issue, topics are pre-selected based on theory about their importance to the economy and then labeled in Beige Books by the researcher. A fine-tuned BERT model learns from the human annotations to assign labels to the entire Beige Book corpus. Then a probit regression forecasts the likelihood of a recession based on counts of 11 economic topics. Findings demonstrate significant value for nowcasting and forecasting recessions with Beige Books. Discussions related to capital spending plans and employment levels hold particular forward-looking importance
Episode 5 - Urban Renewal
No full textThis fifth episode of the series focuses on urban renewal, and the effects on the community that oral history narrators remembered from Mobile\u27s urban renewal program and the construction of Interstate 10.
What Happened Down the Bay? is a podcast created by University of South Alabama students in summer 2025, through the Jean O’Connor-Snyder Internship Program (JOIP) funded by the David Mathews Center for Public Life. Down the Bay is a historic Black neighborhood south of downtown Mobile, Alabama, and this podcast explores the history of Down the Bay as community members have related it through oral history interviews. In preparation for the internship, students participated in an oral history seminar with Drs. Ryan Morini and David Messenger, working with interviews from the Down the Bay Oral History Project collection at the McCall Library before meeting with elders from the community and recording new interviews for the collection. The podcast combines clips from the archived interviews with students’ reflections on how their own home communities and experiences relate to those that people describe from Down the Bay
Down the Bay Oral History Project Newsletter - Fall 2025 (Final Issue)
No full textPublic newsletter sharing information about progress and discoveries during the ongoing Down The Bay Project
Implementing Diabetic Foot Screening for Type 2 Diabetes Patients in an Outpatient Clinic
No full textDiabetic foot ulcers (DFUs) result from the loss of protective sensation, peripheral arterial disease, and foot deformities, making early identification essential in preventing severe complications (Lingyan et al., 2024). Research confirms that incorporating Inlow’s 60-Second Diabetic Foot Screening tool improves patient outcomes by identifying high-risk individuals (Bus et al., 2024). Educating healthcare providers on the benefits of this tool and promoting its use is critical for effective prevention (Lim et al., 2020). Inlow’s 60-Second Diabetic Foot Screen is a cost-effective, simple, and reliable tool widely validated across different settings (Al-Mohaithef et al., 2022). This DNP quality improvement project builds on existing evidence to enhance diabetic foot care, reduce complications, and improve screening practices in clinical settings.https://jagworks.southalabama.edu/con-dnp-posters/1000/thumbnail.jp
Are People More Likely to See Actions as Intentional When They are Forced to Make Their Decisions Quickly? A Replication and Extension of Rosset (2008)
No full textRosset (2008) reported evidence of an intentionality bias, which is a key prediction of a model of human cognition that suggests that people learn to override intentional attributions of behavior, a dual-process model of intention attribution. A model that hypothesizes that when people are deprived of the time to develop deliberate thought processes, their default explanation for actions is intentional, and that this default attribution has to be consciously overridden. Rosset’s main method of testing this was by providing test sentences to participants and placing them into two scenarios where they had different levels of time to process the sentences. Over the years, there have been two main replication attempts at Rosset’s study, with one successfully replicating and the other failing to replicate. This study sought to do its own replication to measure the validity of the Intentionality Bias. This study failed to replicate the findings of Rosset’s 2008 study
Development of a High-Performance, Cost-Effective Architecture for Real-Time Fourier Transform Analysis on an Efinix Trion FPGA
Full text linkThis project presents the development of a high-performance, cost-effective, and powerefficient pipelined architecture designed to execute 10,000 Fast Fourier Transforms (FFTs) per second on an Efinix Trion T120 FPGA. Each FFT processes 4096 signed 13-bit elements, facilitating real-time data analysis for a Time-Domain Impedance Probe (TDIP) operating at a maximum data rate of 490 Mbit/s. The algorithm utilizes the built-in multipliers and dual-port memory cells of the FPGA to optimize data storage, transfer, and processing. It achieves this high performance while only using approximately 4% of the Look-Up Tables (LUTs), 7% of the integrated RAM cells, and 15% of the multipliers available in the FPGA. The Verilog implementation successfully performs one FFT in 6216 clock cycles with a maximum clock rate of 58.8 MHz, permitting 9459 FFTs/second/ module
Tooling for the Creation and Integration of OER Documents and Technologies
Full text linkIn order for Open Educational Resources to live up to their promise as a viable alternative to paid offerings, authors must have access to high-quality software to produce not only static texts, but also interactive and accessible electronic resources. The presenter will demonstrate how the PreTeXt authoring ecosystem puts powerful tools for creating such resources in the hands of authors, without requiring a significant technical background. This talk is based on work funded by the National Science Foundation\u27s Pathways to Enable Open Source Ecosystems grant program
Alexander Asboth: Hungarian Separatist and Union Civil War General
Full text linkThis thesis reveals the experiences of a Hungarian Separatist, Alexander Asboth, and how he applied those experiences in the American Civil War. It examines Asboth’s biography, his expansive view of the Union, and how he differed from many commanding officers in the Western theater. Moreover, this thesis illuminates Asboth’s military service, including his leadership of a diverse force at Barrancas, Florida, and adds to the understudied collaboration of foreign-born officers and Black soldiers in Gulf Coast Civil War history.
During the 1848 Hungarian separatist movement, Asboth served as an Army staff officer under the leader Lajos Kossuth. However, the separatists failed in their bid for independence from the Habsburgs. Asboth and other separatists fled to Turkey as refugees. He eventually received asylum in the US, where he became a citizen and hoped to enjoy the rights denied him by the Austrian Empire. Asboth arrived in the US during the secession crisis. With the outbreak of the Civil War, his European background and interpretation of the sectional struggle led him to side with the US. Considering the secession was a separatist act, my thesis will explain why a Hungarian separatist fought to preserve the Union