When something's gotta give: shifts and stability in parenting among migrants in four Filipino-American communities

Parenting in the context of migration involves navigating competing socialization norms between one’s receiving community and those from one’s natal home. Although research shows that parenting acculturates to some degree after migration, the complexity of this process has not been adequately explored such as how some parenting domains shift more readily than others and how migrant parents deal with incongruences. This study examined shifts and stability in parenting beliefs and practices among Filipino migrants in four communities in the United States with varying levels of co-ethnic density. Drawing from indigenous Filipino psychology methods, findings reveal the adaptive nature of parenting, with some domains shifting to better match norms in receiving communities. Findings also highlight the powerful force that culturally embedded beliefs impose on socialization, with most parenting goals and practices continuing to reflect natal notions. Regardless of community, respondents reported stresses around migrant parenting and various strategies to cope with competing childrearing notions

Exploring social work practitioners’ perspectives on the contributors to burnout since the COVID-19 pandemic

[EMBARGOED UNTIL 02/14/2026] Burnout has a historic and disproportionate impact on social workers and is one important contributor to the ongoing health and behavioral health workforce crisis in the United States. Little is known, however, about social workers’ experiences of burnout and their perceptions of factors that contribute to burnout since the COVID-19 pandemic. This study sought to explore this by answering the following research questions: 1) To what extent are social workers in [state] experiencing burnout? and 2) What do [state] social workers view as the top reasons for burnout in their professional role? Seventy social work practitioners and leaders from [state] completed an online survey during Fall 2022 that included the Copenhagen Burnout Inventory and an open-ended question focused on identifying their perceptions of the top three reasons for burnout in the profession. Findings suggest social workers in this study are experiencing moderate levels of burnout since the COVID-19 pandemic and report primarily organizational (83%) contributors to burnout. They also identified individual (36%), systemic (29%), and interpersonal (27%) contributors to burnout. Implications are discussed related to policy and practice responses to prevent and address burnout among social workers.This is a pre-copyedited, author-produced version of an article accepted for publication in Social Work following peer review. The version of record Tasha M Childs, Aidyn L Iachini, Melissa Reitmeier, Teri Browne, Dana DeHart, Ala Bengel, My’Ashia Haynesworth, Exploring Social Work Practitioners’ Perspectives on the Contributors to Burnout since the COVID-19 Pandemic, Childs, T.M., Iachini, A.L., Reitmeier, M., Browne, T., DeHart, D., Bengel, A., Haynesworth, M. (in press). Exploring Social Work Practitioners' Perspectives on the Contributors to Burnout since the COVID-19 Pandemic. Social Work. is available online at: https://doi.org/10.1093/sw/swae005.Includes bibliographical references

Family–School Partnerships for Rural U.S. Latine English Learners: A Systematic Review

[Embargo until 2026-09-13]The expansion of Latine communities into rural areas of the U.S. highlights a need for research exploring Latine rurality, particularly in education. Schools have significant potential to implement family-engaged, culturally responsive pedagogy, particularly for English Language Learner (ELL) students. Establishing the current state of relevant research for family–school partnerships for rural Latine ELLs will support efforts to develop best practices for culturally responsive education. The present study was a systematic review of literature on family–school partnerships for rural Latine ELL youth in the U.S. Findings highlighted (a) the prevalence of empirical, qualitative research methods; (b) the use of guiding theories from multiple disciplines (including critical, sociocultural and relational, and developmental and educational perspectives); (c) the prevalence of teacher participants in the reviewed studies (relative to family participants); and (d) study findings included teacher factors, family–school relationship factors, family and community factors, and student factors. Findings offer insights into how research investigations may explore youth and family experiences in shifting educational environments (particularly through diverse theoretical perspectives, using methods that emphasize understanding lived experiences, and the role of rurality in research investigations). Study limitations, future research directions, and practical implications are discussed.This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Latinos and Education on March 12, 2025, available at: https://doi.org/10.1080/15348431.2025.2477496

Evaluation of a mental health first aid educational intervention for nursing professors

Background: A lack of mental health awareness and crisis intervention among nursing professors may contribute to students not receiving needed mental health support. This pilot QI project aimed to assess the impact of a Mental Health First Aid (MHFA)/Mental Health Matters (MHM) training program. Project objectives included: 1. A 10% increase in WCC nursing faculty confidence in identifying and intervening during a nursing student health crisis 2. A 5% increase in nursing students seeking assistance from WCC nursing faculty for referral to WCC personal counseling (PC) services 3. A 5% interest of the remaining WCC faculty in participation in the piloted program as a college-wide intervention Methods: After attending a faculty in-service regarding MHFA/MHM, a sample of nursing professors (n = 5) were administered the Mental Health Literacy Scale (MHLS) pre- and post-MHFA certification. A presentation of the MHM referral program was then conducted with the student body. After project implementation, preliminary results were shared with the remaining college-wide faculty, and interest was polled. Results: The Wilcoxen Signed-Rank Test was statistically significant for two MHLS categories. The Mann-Whitney U identified an increase (8.3%) in the MHLS mean scores. The Vargha and Delaney (A) effect size measures determined the clinical significance of the MHLS (question 18, very small, and question 25, no difference). Conclusions: Due to increased nursing faculty confidence, increased student referrals, and significant college-wide faculty interest, it is recommended that WCC support the implementation of this pilot QI project, college-wide. IRB Approval: IRB approval through full/expedited/exempt review

Physical Activity Assessments for Obesity Management in Primary Care

Obesity is an epidemic and chronic disease that affects health and quality of life. Physical activity is a modification encouraged in obesity care due to its benefits which reach beyond weight loss in obesity management and influence multiple body and organ systems. The purpose of the proposed quasi-experimental, evidence-based improvement project is to explore if routinely assessing for physical activity in the primary care setting using a standardized screening tool can address physical inactivity. The anticipated number of participants utilizing G*Power analysis software is a minimum of 15 and will be comprised of individuals from St. Louis, MO. The evidence-based practice intervention is the implementation of a standardized physical activity assessment screening questionnaire in the primary care setting. The primary outcome of physical activity level will be measured using the International Physical Activity Questionnaire- Short Form. Utilizing a tool to identify physical inactivity has the potential to impact the knowledge of providers regarding the current trend in physical activity/inactivity and assist them in the identification, tailored management, communication, and improvement of clinical outcomes related to the difficult phenomenon of obesity

Improving PRN pain medication reevaluation compliance : a quality improvement project

The International Association for the Study of Pain (IASP, 2020) defines pain as an unpleasant sensory and emotional experience related to actual or potential tissue damage, and it is one of the leading causes of emergency department visits globally (Cisewski & Motov, 2019). Chronic pain significantly impacts the adult population and is one of the common reasons adults seek medical care in 2021 (Rikard et al., 2023). An estimated 20.9% (51.6 million) of United States adults experience chronic pain, and around 6.9% of the population (17.1 million) suffer from high-impact chronic pain (Rikard et al., 2023). This prevalence highlights the critical importance of effective pain management in the healthcare system, as chronic pain affects not only the physical well-being of patients but also their emotional and mental health. Nurses, with their pivotal roles, ensure that patients receive the necessary pain medication, as their responsibilities include assessing pain, administering medication, evaluating, and documenting its effectiveness (Van Cleave et al., 2021). Regulatory bodies such as the Joint Commission (TJC, 2017) have established pain assessment and management standards for all JC-accredited healthcare organizations, ensuring that pain is adequately addressed, including providing education resources and programs to its staff. These standards necessitate hospitals to identify pain assessment and management efforts and formulate and oversee performance improvement activities (TJC, 2017). The Veterans Health Administration (VHA) has implemented policies for safe and effective pain management. However, compliance with pain level reevaluation for pro re nata (PRN) medications at VHA medical centers is challenged by policy variations, lack of standardization, and barcode medication administration (BCMA) system limitations

An initial assessement of moral attitude dynamic model for defusing culture war tensions

[EMBARGOED UNTIL 08/01/2025] This dissertation introduces Moral Attitude Dynamic Model (MADM) for defusing conflicts tensions by incorporating the key constructs of Moral Foundation Theory into The Contingency Theory of Accommodation (Contingency Theory). MADM advances Contingency Theory by incorporating both the key conceptual constructs and methodological approaches of Moral Foundation Theory. Consequently, it allows for real-time tracking and analysis of target public's attitudes towards a certain issue as it allows for the employment of computational methods. Therefore, this model could effectively inform conflicts management and political polarization. Accordingly, MADM extends the application of Contingency Theory in the context of social controversies with a long history. Moreover, this study applies MADM on defusing the tensions of two culture issues (GM food and Abortion) as an initial assessment. Concretely, issue related tweets were scraped through academic API provided by Twitter, and issue supporters' and opponents' tweets were identified by supervised machine learning classifiers for each issue respectively. Distributed Dictionary Representation, a Natural Language Processing tool was adopted for quantify supporters' and opponents' moral stances along the enhanced contingency continuum, the key construct of MADM, and multilevel linear modeling was adopted to investigate the quantified moral stances. Accordingly, pro-GM food and pro-abortion messages were constructed based on the moral stance diagnosis results and an experiment was conducted for message and model effectiveness evaluation. The experiment results show the potential of MADM as an effective tool for conflict management. The moral reframed pro-abortion message constructed based on MADM significantly decreases the participants' anger and the affective polarization. In sum, the results suggest that MADM could be a great tool for monitoring the public's opinion, developing quick responses, and evaluating the intervention. In other words, MADM could be a great tool for mitigating polarization and for real-time conflict management in the ever-changing environment.Includes bibliographical references

Applications of deep learning in protein structure prediction : from complexes to intrinsically disordered proteins

[EMBARGOED UNTIL 05/01/2025] Proteins are essential biomolecules that play crucial roles in various biological processes within organisms. Their complex interactions and structures are fundamental to understanding cellular mechanisms and developing therapeutic strategies. However, traditional experimental methods like X-ray crystallography and NMR spectroscopy, despite their accuracy, are costly and time-consuming. This has prompted the exploration of computational models, particularly deep learning techniques, to predict protein structures, especially of complexes, more efficiently. This dissertation presents the development of DNCON2_Inter, DeepComplex, and Disformer. DNCON2_Inter predicts inter-chain contacts in homooligomers using deep convolutional neural networks, leveraging monomeric multiple sequence alignments (MSAs) and co-evolutionary features to enhance prediction accuracy. Using the predicted inter-chain contacts as distant restraints, quaternary structures can be produced. High precision of the inter-chain contacts leads to better quality models of the complexes. DeepComplex, a web server, extends this approach to predict inter-chain contacts and reconstruct quaternary structures of both homodimers and heterodimers. Finally, Disformer, was proposed in the prediction of intrinsically disordered proteins (IDP) which only gain structural and functional importance upon interaction with other molecules. Disformer employs a transformer-based dual graph approach combining Graph Attention Networks (GAT) and Graph Convolutional Neural Networks (GCN). It excels in predicting intrinsically disordered regions (IDRs) by leveraging both sequence-based and structure-based features for a comprehensive graph-based binary node classification. These contributions collectively enhance the predictive capabilities of protein structural analysis, providing new insights into protein interactions and disorder. This research not only advances our understanding of protein dynamics but also paves the way for future developments in the prediction and analysis of protein complexes and IDPs.Includes bibliographical references

The lived experience of bilateral risk-reducing mastectomy and impact on body image in young women with increased lifetime hereditary breast cancer risk

[EMBARGOED UNTIL 05/01/2025] Previvor is a term applied to a person with an identified, elevated lifetime cancer risk but who lacks a cancer diagnosis. Management strategies can be undertaken to decrease the risk of developing certain cancers. For women with a pathogenic variant that increases the predisposition for a breast cancer diagnosis, a bilateral risk-reducing mastectomy (BRRM) is the most effective cancer prevention strategy. Currently, there is a dearth of literature examining BRRM and its effects on young women. This study sought to understand the lived experience of BRRM, along with its impact on body image, in young female previvors in the first 12 months following surgery. Two qualitative methods, descriptive phenomenology and photo-elicitation, were utilized to describe the lived experience of BRRM and body image. Narrative data served as the primary data source, augmented by participant-provided visual data. A sample of 13 women were interviewed. Eight themes were found to describe how young previvors process an increased lifetime breast cancer risk, select BRRM and reconstruction methods, and express the effect of BRRM on body image. Findings provide a rich description of risk-reduction and body image outcomes in young previvors. Results from this project will be used to design future research for improving the physical and psychosocial health of this unique population.Includes bibliographical references

Deep learning enabled materials design and characterization

[EMBARGOED UNTIL 05/01/2025] In this dissertation, deep learning methodologies are applied to innovative computational approaches for the development and characterization of materials in the material science field. The first part of the research focuses on a physics-informed machine learning workflow for virtual experimentation in the 3D printing of thermoplastics, where traditional methods are limited by complex chemical reactions and extensive design possibilities. Utilizing a dataset of 62 formulations and 216 Stress-Strain curves, this method employs dimension reduction and a novel machine learning model with physics-informed descriptors to simulate over 100,000 virtual experiment sets in under one minute, significantly enhancing the speed of material discovery. The second part improves the analysis of characterization data, specifically X-ray diffraction (XRD) patterns, by using Transformer-based models that surpass previous CNN-based models in training speed and accuracy. This segment introduces a novel data augmentation technique that simulates experimental errors and uses interpretability analysis to show how the model captures long-distance interactions between XRD peaks. It also explores the potential of transfer learning from XRD to Fourier-transform infrared spectroscopy (FTIR) data, broadening the model's applicability and improving the efficiency and accuracy of material characterization. Overall, this dissertation demonstrates how deep learning can revolutionize material science research, providing faster, more accurate tools for material development and analysis.Includes bibliographical references