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Medication Reconciliation in a Rural Outpatient Setting
No full textPurpose: The purpose of this quality improvement project has two primary objectives. First, it aims to establish baseline data on the accuracy of medication lists within a small rural health practice by implementing the Brown Bag Method (BBM) medication reconciliation process. The second primary purpose of the project is to increase knowledge of, confidence in, and intent to perform medication reconciliation by combining education for both staff and patients with the BBM process. The overarching long-term goal is to reduce medication errors, thereby decreasing ADEs, morbidity, and mortality. Background: Each year, nearly 7 million individuals experience medication errors (MEs) and resulting adverse drug events (ADEs), often leading to injury, disability, and even death. Medication reconciliation systematically compares a patient’s current medications with their medication list to ensure accuracy. It has been shown to reduce MEs and ADEs. While primary care settings lack a standardized approach, the Agency for Healthcare Research and Quality (AHRQ) proposes the BBM as an effective and thorough method for completing this task. In this rural practice, the owner and sole medical provider identifies medication reconciliation as a persistent challenge, particularly as patients' medication regimens frequently change due to specialist visits, hospital admissions, and patient preferences. Methods: This project employs a quantitative descriptive design focusing on introducing a formal medication reconciliation process using the Brown Bag Method (BBM) to the practice. The primary investigator (PI) will conduct a BBM reconciliation with practice patients to establish baseline data about the accuracy of medication lists in the practice. The project will also include educational interventions for patients to explain the medication reconciliation process and its importance. By combining formal medication reconciliation with targeted education, the project aims to increase knowledge of medication reconciliation, confidence in the process, and intent to participate. Results : Of the 10 patients who participated, 9 (90%) had at least one discrepancy between their reported and documented medication list. The most common discrepancies included missing over-the-counter medications and duplicate prescriptions. On the post-intervention survey, 8 out of 10 participants agreed or strongly agreed that they learned about the importance of reviewing medications. Seven participants agreed or strongly agreed that they felt more confident keeping an accurate medication list, and six agreed or strongly agreed that they intended to participate in medication reconciliation at future visits. Conclusion: The BBM was feasible to implement in this rural clinic and was well received by participants. It revealed high rates of discrepancies in medication lists and highlighted the value of medication reconciliation in improving medication safety. Though the sample size was small, the intervention showed promise in enhancing patient understanding, confidence, and engagement. Site-specific recommendations were made to support ongoing implementation and sustainability
Integrating Palliative Care Education and Referral Standardization for Head and Neck Cancer Patients in an Outpatient Setting
No full textBackground: Early integration of palliative care (EPC) in oncology care improves symptom management, patient satisfaction, and quality of life for individuals with head and neck cancer (HNC). Despite established benefits, EPC remains underutilized in this patient population due to misconceptions among healthcare providers and patients. Structured education and referral processes are critical strategies to enhance timely palliative care (PC) access, particularly for high-risk populations such as patients with HNC. Purpose: The purpose of this quality improvement project is to increase healthcare provider and nurse knowledge and confidence regarding EPC and to evaluate practice change by measuring the number of PC referrals placed for patients with HNC at the Banner MD Anderson Cancer Center outpatient infusion center in Phoenix, Arizona. Methods: Using the Model for Improvement with the Plan-Do-Study-Act cycle, an educational session for healthcare providers and nurses about the importance of EPC and introduction of a
referral algorithm adapted from the National Comprehensive Cancer Network and American Society of Clinical Oncology to facilitate earlier identification and referral of patients with HNC were given. Pre- and post-education surveys assessed changes in knowledge and confidence levels. Implementation of the referral algorithm was measured by the number of PC referrals before and after the educational session. Results: Survey data from pre- and post-education survey responses was analyzed using descriptive analysis to summarize participant knowledge and levels of confidence. A total of 23 participants completed the pre-education survey and 19 completed the post-education survey. Knowledge scores improved from an average of 72% pre-intervention to 89% post-intervention, and confidence ratings also shifted positively, with more participants reporting being “confident” or “very confident” in discussing EPC. Seven PC referrals were placed for patients with HNC in the one month before the intervention, compared to four referrals in the one month following the intervention. These counts reflect overall referral activity but cannot be accurately evaluated because the total number of HNC patients seen during each timeframe is unknown. Conclusions: The pre- and post-education survey results demonstrated improved knowledge and confidence among participants regarding EPC. These findings support the benefits of structured education and a standardized referral algorithm in preparing oncology staff to initiate timely PC referrals. Referral data, however, showed a decrease from seven pre-intervention referrals to four post-intervention referrals, and interpretation is limited by the absence of denominator data on HNC patients during each timeframe
Improving Maternal Mental Health: Non-Pharmacological Recommendations for Mothers in a Pediatric Setting
No full textBackground: Postpartum depression (PPD) is one of the most common complications of pregnancy, impacting 15% of all women, and affects a mother’s ability to bond and form a secure attachment with her infant (Kallem et al., 2019). Infants of mothers who suffer from postpartum depression have been shown to have impairments in growth, brain development, and cognitive, behavioral, and social development (Lamere et al., 2022). Pharmacological treatments like antidepressants are the mainstay treatment options. However, several mothers, especially those who are breastfeeding, have concerns about safety and side effects. There is an emerging interest in exploring the use of nonpharmacologic modalities as an alternative treatment, and these interventions are well- received by mothers (G et al., 2024). Purpose: The purpose of this project is to educate mothers on holistic, non-pharmacological, evidence-based recommendations to improve symptoms of anxiety and depression. Methods: Participants involved in this quality improvement project were mothers of children at Horizon Health and Wellness who attended well-child visits up to 12 months of age. The project investigator recruited these mothers on the day of their appointment with a consent and disclosure letter. An educational handout on non-pharmacological interventions for postpartum depression and anxiety was given, and a post survey was implemented to measure perceived awareness and benefits of behavior change. Results: After 2 weeks of implementation, 11 mothers participated in the project with a completed written survey. A Likert scale was used to analyze the results of the survey, and answers were interpreted rating from 1 (strongly disagree/highly unlikely) to 5 (strongly agree/highly likely). Average scores were calculated and used to evaluate results. Overall, participants had a better understanding of the impact PPD/PPA can have on children (4.7) and learned something new from the educational handout (4.9). Furthermore, 9 out of 11 women stated they were likely to implement the recommended holistic interventions, with an additional mother answering that she was highly likely to implement changes. Conclusions: This quality improvement project showed that the use of an educational handout was beneficial in enhancing maternal knowledge on the effects PPD/PPA can have on children and introducing new interventions that can be implemented into their daily life. Embedding this education into pediatric care offers a low-cost, accessible way to reduce stigma, support maternal well-being, and ultimately improve outcomes for both mothers and infants
A Multidimensional Framework for Transportation Safety: Linking Perceptions, Policies, and Environmental Contexts
No full textRoad safety remains a complex and evolving challenge shaped by behavioral, institutional, and environmental systems. This dissertation integrates multiple analytical perspectives to advance a holistic understanding of how enforcement technologies, workforce readiness, and environmental context collectively influence transportation safety. Through a combination of crash analysis, behavioral research, organizational assessment, and spatial modeling, this dissertation connects technical effectiveness with human and ecological dimensions of safety. The research begins with an evaluation of red-light camera (RLC) enforcement in Phoenix, combining crash data analysis with professional perspectives to assess the program’s operational and safety impacts. Using a before-during-after design with Empirical Bayes estimation, the findings reveal substantial reductions in angle crashes and severe injury during operation. These safety gains remained largely stable even after the program’s termination indicating lasting behavioral adaptation and long-term benefits of enforcement visibility. Building on this evidence, the next phase transitions from professional assessments and data analysis to public perceptions, examining how fairness, transparency, and safety beliefs shape acceptance of RLCs in Arizona and New York. Structural equation modeling demonstrates that fairness and clarity mediate the relationship between safety beliefs and support, underscoring the central role of legitimacy and communication in sustaining automated enforcement programs. The analysis then shifts toward technological transitions and organizational readiness through an examination of electric vehicle (EV) adoption among Minnesota’s public agencies. Surveys and interviews highlight that while environmental commitment and policy alignment foster optimism, persistent concerns about charging reliability, maintenance, and cold-weather performance continue to constrain large-scale implementation. Extending this focus on sustainability and safety, the final investigation emphasizes that transportation safety is not limited to human protection alone. Applying a grid-based spatial model of wildlife-vehicle collisions in Tucson, Arizona, the investigation reveals that roadway density, travel speed, and population exposure are the strongest predictors of crash likelihood, demonstrating how human infrastructure shapes risks for both people and wildlife. Together, these studies form an integrated framework linking behavioral legitimacy, institutional capacity, and ecological context. The findings contribute empirical and methodological insights that advance the design of transportation systems that are safer, more adaptive, and sustainable for all users
Towards Smarter Wills: Information Extraction and Natural Language Inference for Legal Text Understanding in English
No full textThis dissertation analyzes legal English as a special register of English employed across the legal domain, with specific emphasis on its use in wills, and develops an integrated foundation for transforming natural language wills into executable smart contracts through three interconnected contributions. The first contribution introduces an artificial intelligence-based system that interprets English language wills and computes their expected devolution outcomes. The system architecture and implementation are described in detail, and evaluation on manually annotated test sets shows that it surpasses a baseline model in both accuracy and accountability while maintaining interpretability and traceability. The second contribution extends this framework through a task aware prompt chaining approach for information extraction. The method divides extraction into an initial classification stage and a targeted extraction stage. The classification stage identifies the types of information present in the input and selects the most relevant examples for inclusion in the extraction prompt. Experiments with two different models show improved performance in few-shot settings and reduced token usage. Incorporating this method into the first system enables more detailed information extraction and provides a basis for adding functionality that relies on finer distinctions in the text. The third contribution presents a natural language inference dataset designed to assess the legal validity of will statements. Each validity judgment requires three inputs: a will statement, a law, and the conditions at the testator’s death, resulting in texts that are longer and more complex than those in standard NLI datasets. Eight neural models trained on this dataset achieve over 80 percent macro F1 and accuracy, although group accuracy, a stricter metric evaluating sets of related examples, remains in the mid 80s at best, indicating only superficial task understanding. Ablation and explanation analyses further show that models draw on all three text segments but sometimes rely on semantically irrelevant tokens. While presented as an independent resource, this study can be developed further to extend the first system by supporting automated evaluation of the legal validity of testamentary clauses. Taken together, these studies provide a unified foundation for transforming natural language wills into machine-interpretable smart wills. They also deepen our understanding of the linguistic and structural features of legal documents, including their intricate syntactic patterns, specialized terminology, and reliance on external contextual information such as legal rules and conditions. The findings from this work will support the development of accountable systems for the legal domain and contribute to broader progress toward smarter, more interpretable wills
Spectral Analysis of the Degree-Corrected Laplacian
No full textIn network analysis, community detection aims to partition the nodes into meaningful groups based on their connections. To study this problem, random graph models such as the Stochastic Block Model (SBM) and the Degree-Corrected Stochastic Block Model (DCSBM) are widely used. A common approach for estimating communities is spectral clustering, a dimension reduction technique that maps the nodes to a lower-dimensional space while preserving the relevant information. Specifically, the eigenvectors of a particular matrix are used as a representation of each node. These methods are popular due to their simplicity and relative effectiveness. In this thesis, we investigate the identifiability of the SBM and DCSBM, addressing a gap in the existing literature. We then generalize the matrices commonly used in spectral methods, and introduce the degree-corrected Laplacian, which accounts for the degree heterogeneity introduced by the DCSBM. We prove that the spectral clustering method induced by this matrix is consistent, and show that it enjoys the same asymptotic properties as its classical counterparts. Finally, we propose a spectral clustering algorithm based on the degree-corrected Laplacian and evaluate its performance through simulated and real-world network data
Improving OBGYN Healthcare Providers’ Knowledge on Postpartum Depression and Screening Practices in an Outpatient Clinic
No full textBackground: Postpartum depression (PPD) is the most prevalent mental health condition affecting 1 in 7 women after giving birth, yet it often goes undiagnosed and untreated. Despite recommendations from the American College of Obstetricians and Gynecologists (ACOG), outpatient screening is inconsistent. Guidelines and screening tools are available to assist with early detection and prevention of PPD. Purpose: The aim of this Doctor of Nursing Practice (DNP) quality improvement (QI) project was to improve OBGYN healthcare providers’ knowledge of postpartum depression risk factors and confidence in screening guidelines using the Edinburg Postnatal Depression Scale (EPDS). The primary objectives were for a 90% pass rate in the knowledge section of the post-survey and an overall improvement in the confidence section from the pre to the post survey. Methods: The project was implemented in an outpatient OBGYN clinic in Phoenix, Arizona, using the Institute for Healthcare Improvement’s (IHI) Model for Improvement and Plan-Do-Study-Act (PDSA) framework. A brief PowerPoint educational intervention was delivered. Pre and post- surveys were administered to evaluate changes in provider knowledge on PPD risk factors and confidence regarding PPD screening. Data were analyzed using descriptive statistics and a review of themes presented in the open-ended responses Results: Four physicians were recruited, with three completing the intervention. Post-intervention knowledge increased from 61.9% to 95.2%, particularly in recognizing PPD prevalence, risk factors, and EPDS interpretation. Confidence improved by 6-13% across all domains. Open-ended responses highlighted ongoing barriers such as time constraints and limited counseling access. Conclusions: This quality improvement project demonstrated that a brief, structured educational intervention can effectively enhance OBGYN providers’ knowledge and confidence in PPD. Findings suggest that target education can improve understanding of PPD risk factors, screening, and interpretation, supporting timely and appropriate patient care. Expanding and embedding ongoing education into professional development may further sustain these improvements and enhance maternal mental health outcomes
Love, Lust, or Liberation? Muslim Women through the Soviet and Russian Imagination
No full textThis thesis explores the representation of Muslim women through the lens of two literary works, Chingiz Aitmatov’s Jamilia (1958) and Guzel Yakhina’s Zuleikha (2015) with the goal of finding parallels between how the women are represented in Russian and Central Asian perspectives. By considering the religious, historical, and political landscapes in the texts, this thesis connects the construction of Muslim women within broader societal narratives, drawing upon theories of Orientalism, feminist scholarship, and postcolonial theory. It further explores artistic and visual modes of representation in order to find similar constructions and depictions akin to the literary texts. Additionally, this paper examines the difference between Russian Orientalism and Soviet Orientalism, tracing its evolution from the Imperial period to the Post-Soviet present
Title Page
No full textTitle PageThis material published in Arizona Journal of International and Comparative Law is made available by the James E. Rogers College of Law, the Daniel F. Cracchiolo Law Library, and the University of Arizona Libraries. If you have questions, please contact the AJICL Editorial Board at http://arizonajournal.org/contact-us/
Computational Methods for the Chemical Space Exploration of Metal Halide Perovskite Interfaces
No full textAdditive engineering—the use of small molecules to simultaneously address the inherent chemical instability of metal halide perovskites and enhance the optoelectronic properties of these materials—has been demonstrated to be an effective strategy to accelerate the commercialization of solution-processable perovskite devices. However, the additive selection process for perovskite incorporation is typically based on chemical intuition, leaving a limited understanding of how the chemical, electronic, and geometric properties of additive molecules influence interactions with perovskite interfaces. In this dissertation, I focus on the usage and development of computational physics tools to systematically investigate the impact of perovskite interface functionalization with small molecules on fundamental physical behavior, facilitating the construction of structure-property relationships based on simple molecular features. In Chapter 1, I establish the foundation for discussing surface functionalization in metal halide perovskites, systematically exploring how chemical space is navigated in the search for effective surface ligands. Chapter 2 provides an in-depth overview of the methodologies employed throughout this work, including density functional theory, cheminformatics, and workflow automation. In Chapter 3, I examine the chemical and electronic effects of functionalizing methylammonium lead iodide (\ce{MAPbI3}) perovskite with 5-aminovaleric acid (5-AVA) and two metal halide salts: 5-aminovaleric acid iodide (5-AVAI) and 5-aminovaleric acid chloride (5-AVACl). The film quality, as assessed by X-ray diffraction (XRD) and photoluminescence (PL) spectrophotometry, improves with the inclusion of all additives. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) reveal an increase in grain size and a decrease in film roughness with the incorporation of 5-AVAI and 5-AVACl. DFT calculations, along with X-ray photoelectron spectroscopy, reveal that 5-AVAI and 5-AVACl exhibit stronger surface interactions than 5-AVA with both the \ce{PbI2}-rich and \ce{MAI}-rich surfaces of \ce{MAPbI3}, primarily due to hydrogen bonding, revealing the mechanism behind the improvement in film quality. Furthermore, I reveal that 5-AVACl can spontaneously decompose into 5-AVA and HCl, leading to similarities in film properties between 5-AVA and 5-AVACl after annealing. In chapter 4, I employ a selection of twenty-five heterocyclic molecules to model the effects of heteroatomic species (N, O, S, Se, and P) and heteroatom electron delocalization on the chemical and electronic properties of \ce{MAPbI3} perovskite using DFT calculations. I demonstrate that the interaction strength of each heterocycle increases as the heteroatom electron delocalization (i.e., degree of unsaturation) decreases. We observe that adsorption energies are strongest for N-donors and weakest for O-donors, with P-, S-, and Se-donors exhibiting intermediate adsorption energies. The electronegativity of the heteroatom plays a crucial role in governing surface charge transfer from the adsorbate to the perovskite surface. Higher electronegativity is correlated with a reduced extent of Pb reduction—or potential oxidation in the case of O-donors—highlighting its impact on interfacial electronic interactions. Heteroatom electronegativity also serves as a predictor for surface band gap shifts, with more electronegative donors leading to increased surface band gaps. Conversely, the adsorption of low-electronegativity P-donors generally results in surface band gap reductions. In chapter 5, I introduce an advanced framework for probing the surface functionalization of any perovskite composition using DFT. By combining several open-source Python codes (PyMatGen, ASE, Fireworks), I demonstrate that the analysis performed in chapters 3 and 4 can be automated, simultaneously improving the repeatability and physicality of the DFT predictions. This automated workflow removes the inherent bias of the operator by identifying adsorption sites on any surface using Delaunay triangulation and stochastically places small molecules on these sites using Open Catalyst Project (OCP) subroutines from Meta FAIR Chemistry. I demonstrate the capabilities of this workflow by constructing a subset of 200 small molecules from the ZINC 20 database with fewer than ten heavy atoms, selected to maximize chemical diversity through Tanimoto similarity. The ground-state geometries of these molecules on the \ce{PbI2}-rich surface of formamidinium lead iodide (\ce{FAPbI3}) perovskite were identified using the high-throughput workflow. In this work, I demonstrate that cheminformatics descriptors enable the construction of structure–property relationships linking molecular structure to DFT-calculated adsorption energies and surface charge transfer. Notably, we found that the Wildman–Crippen molar refractivity of the selected additive molecules exhibited a strong correlation with adsorption energy, while the extended topological atom (ETA) index was highly correlated with interfacial charge transfer. These findings suggest the potential of cheminformatics descriptors as predictive metrics for assessing the doping ability of molecular additives in MHPs.Release after 08/04/202