Temple University Research Articles

Newborn RSV immunization rates and reasons compared to family COVID-19 and influenza immunization status.

Respiratory syncytial virus (RSV) is a common viral infection with the potential for severe illness in infants, leading to thousands of pediatric hospitalizations annually. In late 2023, Beyfortus (nirsevimab), a long-acting monoclonal antibody, became available to provide passive RSV immunization for all newborns meeting eligibility criteria. This study aimed to explore parental decision-making regarding RSV immunization, particularly in comparison to family uptake of COVID-19 and influenza vaccines, within an urban, predominantly Medicaid population in North Philadelphia. This qualitative study was conducted at Temple University Hospital and Temple Pediatric Care outpatient clinic. Semi-structured interviews were performed with 25 parents and primary caregivers of newborns who received RSV immunization during the 2023-2024 season. Participants were recruited from a retrospective list and interviewed by phone using a standardized questionnaire. Grounded Theory methodology was applied for data analysis using iterative coding to identify themes related to immunization perceptions and acceptance. Participants expressed high levels of trust in healthcare providers and prioritized safety and efficacy when making immunization decisions. Parents accepted RSV immunization due to personal or family experiences with RSV, general desire to protect their newborns, or pediatrician recommendation. In contrast, COVID-19 and influenza vaccine decisions were less consistent. Concerns about side effects and perceived lack of effectiveness were common reasons for declining COVID-19 vaccines, despite similar motivations for protection. Social media and political beliefs had minimal reported influence. Parents reported a lack of consistent, reliable online sources for vaccine information, often relying on general internet searches. Many misunderstood immunization/vaccine efficacy, equating it with complete immunity rather than reduced disease severity. Parental acceptance of RSV immunization was driven by protective instincts for newborns and familiarity with RSV as a disease. Hesitancy toward COVID-19 vaccination stemmed largely from concerns about effectiveness and confusion around immunization/vaccine purpose. Trust in medical professionals influenced decision-making, though participants lacked a consistent source of immunization information. Improved public education on immunization efficacy and centralized access to trustworthy information may enhance immunization uptake and address ongoing hesitancy across all pediatric immunizations.

Hierarchical Deep Learning for Comprehensive Epileptic Seizure Analysis: From Detection to Fine-Grained Classification

Epileptic seizure detection and classification from EEG recordings faces significant challenges due to extreme class imbalance. Analysis of the Temple University Hospital Seizure (TUSZ) dataset reveals imbalance ratios of 150:1 between common and rare seizure types, with high temporal heterogeneity (seizure durations of 1–1638 s). We propose a cascaded deep learning architecture with two specialized CNNs: a binary detector followed by a multi-class classifier. This approach decomposes the classification problem, reducing the maximum imbalance from 150:1 to manageable levels (9:1 binary, 5:1 type). The architecture implements a high-confidence filtering mechanism (threshold = 0.9), creating a 99.5% pure dataset for type classification, dynamic class-weighted optimization proportional to inverse class frequencies, and information flow refinement through progressive stages. Loss dynamics analysis reveals that our weighting scheme strategically redistributes optimization attention, reducing variance by 90.7% for majority classes while increasing variance for minority classes, ensuring all seizure types receive proportional learning signals regardless of representation. The binary classifier achieves 99.64% specificity and 98.23% sensitivity (ROC-AUC = 0.995). The type classifier demonstrates >99% accuracy across seven seizure categories with perfect (100%) classification for three seizure types despite minimal representation. Cross-dataset validation on the University of Bonn dataset confirms robust generalization (96.0% accuracy) for binary seizure detection. This framework effectively addresses multi-level imbalance in neurophysiological signal classification with hierarchical class structures.

1420-P: Adherence to ADA SOC Guidelines for CKD in Individuals with Diabetes at an Academic Primary Care Outpatient Practice

Introduction and Objective: Diabetes is the most common cause of renal failure accounting for ~50% of cases in the US. In the last 5 years, several new therapies have become available to halt the progression of CKD. Current guidelines from the American Diabetes Association recommend treatment and surveillance of CKD based on the stage of disease. Whether these guidelines are followed by primary care providers, who are increasingly managing diabetes, is unclear. Methods: We performed a retrospective analysis of patients at the Temple University Hospital Internal Medicine Clinic between January 1, 2022- December 31, 2023. We included patients with an ICD10 diagnosis of either type 1 or 2 diabetes and two visits within the two-year study period. We excluded those with no prescription of anti-glycemic therapy within 12 months of the first study visit and those with type 1 diabetes diagnosed <5 years prior. Comparisons between groups were performed with one-way ANOVAs and chi-square tests. Results: We found that 73.3% of patients had at least one serum creatinine and 37.6% of patients had both one serum creatinine and urine albumin/creatinine ratio completed in the first year of the study period. In those with CKD and albuminuria, ACE/ARBs, GLP1RA, SGLT2i, and MRAs were prescribed to 79.3%, 58%, 48.8%, and 13.7% of patients, respectively. ACE/ARBs were prescribed earlier than other medications (p<0.0001). The average number of renoprotective medications prescribed increased with CKD stage (p=0.0009) and albuminuria (p=0.042). Although differences were observed between races with respect to CKD stage (p<0.0001), there was no difference in the number (0.096) and type of medications prescribed (p=0.54). Conclusion: Compared to prior studies, we saw higher rates of screening and medications prescribed, specifically GLP1RA and SGLT2i. However, these results may draw attention to lower rates of ongoing assessment for albuminuria amongst those on evidence-based therapy. Disclosure V. Rao: None. J. Turella: None. A.D. Rao: Advisory Panel; Corcept Therapeutics. Consultant; Spruce Biosciences.

ECR Spotlight – Brooke Quinn

ECR Spotlight is a series of interviews with early-career authors from a selection of papers published in Journal of Experimental Biology and aims to promote not only the diversity of early-career researchers (ECRs) working in experimental biology but also the huge variety of animals and physiological systems that are essential for the ‘comparative’ approach. Brooke Quinn is an author on ‘ Unsupervised learning reveals rapid gait adaptation after leg loss and regrowth in spiders’, published in JEB. Brooke conducted the research described in this article while an undergraduate researcher in Tonia Hsieh's lab at Temple University. She is now a PhD candidate in the lab of Sharon Swartz at Brown University, investigating biomechanics and perturbation recovery in animals ranging from spiders to bats.

Abstract P3-01-05: The Effect of Individual and Neighborhood-level Socioeconomic Disparities in Advanced and Early-Onset Female Breast Cancer

Abstract Background: Race is a significant factor in the diagnosis and outcomes of breast cancer (BC); Black women are diagnosed with more aggressive disease, and have higher mortality from BC compared to White women. Race may be a surrogate for other social determinants of health (SDOH), which may explain observed disparities. Further study is needed to assess the effect of all 5 domains of SDOH (education access and quality, health care access and quality, neighborhood and built environment, social and community context, and economic stability) on BC. In the absence of individual-level information, neighborhood-level socioeconomic status (nSES) can be useful in determining patterns of disparity. This study aims to determine associations between early-onset BC and advanced-stage BC at diagnosis across all 5 domains of SDOH using both individual and nSES measures. Methods: A retrospective analysis of female patients who presented to Fox Chase Cancer Center and Temple University Hospital between January 1st, 2018-December 31st, 2022, was conducted. Patient demographics (age, race, insurance, address, family history of cancer) and clinical characteristics (stage, receptor phenotype) were obtained. Patient addresses were used to geocode patients to a US Census Tract corresponding to known nSES variables obtained from the US Census Bureau, the Centers for Disease Control and Prevention, and the SEER Database. Primary outcomes included early-onset BC (defined as diagnosis <45 years) and advanced disease (AD; defined as stages 3 and 4 at diagnosis). Data was analyzed via univariate logistic regression models to compare patient, clinical, and neighborhood characteristics. Multivariate logistic regression models controlling for age at diagnosis, year of diagnosis, insurance, BC phenotype, and family history of cancer were used to assess associations between individual variables or nSES and the outcomes. Lasso regression controlling for race, insurance, and family history of BC was used to assess for significant associations between nSES variables and outcomes. Results: 5172 female patients were diagnosed with BC, of which 1248 (24.1%) patients were Non-Hispanic Black, 3023 (58.4%) Non-Hispanic White, 151 (2.9%) Asian/Pacific Islander, 419 (8.1%) Hispanic and 331 (6.4%) Other/Unknown. Mean age at diagnosis was 60.1 years. 374 (7.2%) patients were diagnosed with ER-/PR-/HER2- breast cancer (TNBC). 641 (12.4%) patients were diagnosed with early-onset BC, and 744 (14.4%) patients had AD at diagnosis. On multivariate analysis, patients with Medicaid and Medicare had higher odds of being diagnosed with AD compared to those with private insurance (OR 2.116 [1.494-2.998], p<0.001; and OR 1.383 [1.013-1.888], p=0.041, respectively). Hispanic patients had lower odds of AD compared to non-Hispanic White patients (OR 0.544 [0.322-0.917], p=0.022). Patients with TNBC and ER-/PR-/HER2+ disease had increased odds of AD at diagnosis (OR 3.391 [2.543-4.521], p<0.001; and OR 1.382 [1.019-1.874], p=0.037, respectively). Patients with TNBC and ER-/PR-/HER2+ disease also had increased odds of early-onset disease (OR 1.756 [1.256-2.455], p=0.001; and OR 1.427 [1.036-1.966], p=0.029, respectively). Lasso regression indicated the nSES variables of Technical/Professional/Managerial Employment, Household Income, and Index of Concentration at the Extremes (ICE; high-income White vs. low-income Black) was associated with advanced vs. localized disease at diagnosis. Conclusion: Patients with Medicare and Medicaid have higher risk of AD at diagnosis compared to patients with Private insurance. Patients with TNBC and ER-/PR-/HER2+ BC have higher risk of both AD at diagnosis and early onset disease. Lasso regression indicates that rate of Technical/Managerial/Professional Employment, Household Income, and ICE (high-income White vs. low-income Black) within a patient’s census tract is associated with advanced vs. localized disease. Citation Format: Anumita Chakraborty, Lauren Correia, Jill Hasler, Gaetano Romano, Antonio Giordano, Shannon Lynch, Aruna Padmanabhan. The Effect of Individual and Neighborhood-level Socioeconomic Disparities in Advanced and Early-Onset Female Breast Cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-01-05.

MVP CAN model: Accelerating cancer diagnosis for underserved inpatients to bridge health gaps.

e13546 Background: Temple University Hospital-Main Campus (TUH) of TU health system (TUHS) is in North Philadelphia, Pennsylvania (USA) where 40% of residents live below the poverty line. Patients (pts) encounter multiple social determinants of health (SDOH) and face cancer health disparities. Institutional data from Fox Chase Cancer Center (FCCC of TUHS), show that pts with cancer at TUH present with more advanced stage and have worse outcomes. Incidental suspected cancer findings occur during inpatient hospitalizations for unrelated conditions. An approach is needed to avoid delayed workup and missed diagnoses, as oncologic care requires complex diagnostic testing and specialty input. The Multi Visit Patient Cancer Clinic (MVP CAN), led by TUH and in collaboration with FCCC, was created to bridge the gaps and streamline diagnostic workup for suspected cancer findings from the inpatient setting. Methods: MVP CAN model expedites outpatient diagnostic workups for suspicious cancer findings upon inpatient providers placing an electronic order. MVP CAN model includes i) an in-person transitional clinic led by a physician internist for those pts without a PCP or facing health literacy challenges, and/or ii) virtual coordination and an e-consult with the appropriate subspecialist to assess urgency and determine next steps, inclusive of procedural outpatient interventions. MVP CAN includes a navigator who coordinates tests, follow-ups, specialty appointments, and addresses SDOH with a trauma-informed approach. Here, we present results of the pilot implementation. Results: MVP CAN launched in May 2024 with data cutoff Dec 2024. 156 inpatients were referred, with 33% seen in-person and 67% managed virtually. Additionally, 56% had a subspecialty e-consult. Characteristics included: median age 62 yrs; 63% Black, 20% Hispanic, 11% White, 2% Asian; 59% Male, 41% Female; and 12 patients initially uninsured. Of 134 who completed workup: 51% were diagnosed with cancer including 42% referred to Oncology for treatment and 9% to Hospice, while 30% required ongoing surveillance. 13% were lost to follow up and 3% died. The most common cancers ( > 5) were NSCLC, colon, pancreatic, and prostate, with 18% stage III & 69% stage IV. Median time from program entrance to first oncology visit was 22 days (range 3-110). Conclusions: MVP CAN addresses a critical gap in transitioning care from inpatient to outpatient for underserved pts with suspected cancer while concurrently addressing SDOH. Over half of pts referred and completing workup were diagnosed with cancer, with most presenting at advanced stages. This highlights the need for navigation structured programs to enhance timely diagnosis and treatment and address access barriers and SDOH. Ongoing analysis will evaluate time to treatment and survival to assess the impact of MVP CAN on advancing health equity.

Optimizing tarlatamab therapy for relapsed small cell lung cancer: Biomarker identification and addressing treatment delivery disparity.

e23106 Background: SCLC is a highly aggressive malignancy with limited treatment options and high relapse rates. Tarlatamab, a BiTE targeting DLL3, demonstrates promise with a 40% ORR and a median OS of 12 months. However, challenges persist, including biomarker discovery for response prediction, managing serious toxicities (CRS and ICANS), and addressing barriers to access among underserved populations. Tarlatamab requires 24–48 hours of monitoring after initial doses, posing logistical challenges, particularly in communities with significant social determinants of health (SDOH). Methods: We propose a framework to concurrently advance biomarker discovery and mitigate access disparities at Fox Chase Cancer Center (FCCC) and Temple University Hospital (TUH) in North Philadelphia, where 40% of residents live below the poverty line. After informed consent, a bio-specimen registry will collect pre- and on-treatment blood samples and archival tumor tissues from patients with previously treated extensive-stage SCLC undergoing tarlatamab therapy. Simultaneously, interventions will address logistical barriers to ensure adherence to toxicity monitoring guidelines. Results: Advanced blood- and tissue-based analyses, including flow cytometry and single-cell RNA sequencing, will identify biomarkers predictive of efficacy (ORR, PFS, OS) and toxicity (e.g., TRAEs, CRS, ICANS). Targeted interventions include: 1) Complimentary accommodations within 1 mile of an emergency care facility. 2) Transportation support to ensure timely access to therapy. 3) Outpatient monitoring kits with thermometers, sphygmomanometers, pulse oximeters, and emergency contacts. These kits will include educational materials for side-effect management and caregiver guidance. Patient and caregiver surveys will assess the utility of these interventions and the overall experience. These services are expected to launch in Spring 2025. Conclusions: This framework integrates biomarker discovery with equity-focused interventions to enhance the clinical utility of tarlatamab while addressing disparities in treatment access. By improving outcomes for underserved populations, this approach also offers a model for broader implementation of T-cell engager therapies. This study is supported by the Ride Hard Breathe Easy Foundation, Philadelphia.

Book Review: Proper Women: Feminism and the Politics of Respectability in Iran by Fae Chubin Proper Women: Feminism and the Politics of Respectability in Iran. By ChubinFae. Philadelphia, PA: Temple University Press, 2024, 208 pp., $89.50 (cloth); $29.95 (paper).

Book Review: Proper Women: Feminism and the Politics of Respectability in Iran by Fae Chubin Proper Women: Feminism and the Politics of Respectability in Iran. By ChubinFae. Philadelphia, PA: Temple University Press, 2024, 208 pp., $89.50 (cloth); $29.95 (paper).

Automatic diagnostics of electroencephalography pathology based on multi-domain feature fusion.

Electroencephalography (EEG) serves as a practical auxiliary tool deployed to diagnose diverse brain-related disorders owing to its exceptional temporal resolution, non-invasive characteristics, and cost-effectiveness. In recent years, with the advancement of machine learning, automated EEG pathology diagnostics methods have flourished. However, most existing methods usually neglect the crucial spatial correlations in multi-channel EEG signals and the potential complementary information among different domain features, both of which are keys to improving discrimination performance. In addition, latent redundant and irrelevant features may cause overfitting, increased model complexity, and other issues. In response, we propose a novel feature-based framework designed to improve the diagnostic accuracy of multi-channel EEG pathology. This framework first applies a multi-resolution decomposition technique and a statistical feature extractor to construct a salient time-frequency feature space. Then, spatial distribution information is channel-wise extracted from this space to fuse with time-frequency features, thereby leveraging their complementarity to the fullest extent. Furthermore, to eliminate the redundancy and irrelevancy, a two-step dimension reduction strategy, including a lightweight multi-view time-frequency feature aggregation and a non-parametric statistical significance analysis, is devised to pick out the features with stronger discriminative ability. Comprehensive examinations of the Temple University Hospital Abnormal EEG Corpus V. 2.0.0 demonstrate that our proposal outperforms state-of-the-art methods, highlighting its significant potential in clinically automated EEG abnormality detection.

SDoH Impact on Periodontal Disease Using Machine Learning and Dental Records.

The impact of social determinants of health (SDoH) on periodontal disease (PD) is critical to study, as a deeper understanding of SDoH offers significant potential to inform policy and help clinicians provide holistic patient care. The use of machine learning (ML) to analyze the association of SDoH with PD provides significant advantages over traditional statistical methods. While statistical approaches are effective for identifying trends, they often struggle with the complexity and unstructured nature of data from dental electronic health records (DEHRs). The objective of this study was to determine the association between PD and SDoH using big data through linked DEHR and census data using ML. We used the records of 89,937 unique patients (754,414 longitudinal records) from the Temple University School of Dentistry who received at least 1 treatment between 2007 and 2023. Patient PD outcomes were categorized based on progression, improvement, or no change, using longitudinal data spanning up to 16 y. We applied ML models, including logistic regression, Gaussian naive Bayes, random forest, and XGBoost, to identify SDoH predictors and their associations with PD. XGBoost demonstrated the best performance with 94% accuracy and high precision, recall, and F1 scores. SHapley Additive exPlanations (SHAP) values were used to provide explainable ML analysis. The leading predictors for PD progression were higher social vulnerability index, poverty, population density, fewer dental offices, more fast-food restaurants, longer travel times, higher stress levels, tobacco use, and multiple comorbidities. Our findings underscore the critical role of SDoH in PD progression and oral health inequity, advocating for the integration of these factors in PD risk assessment and management. This study also demonstrates the potential of big data analytics and ML in providing valuable insights for clinicians and researchers to study oral health disparities and promote equitable health outcomes.

Seizure information enrichment in ECG through spectral whitening for improving epileptic seizure prediction.

Seizure information enrichment in ECG through spectral whitening for improving epileptic seizure prediction.

Clinical utility of 1:16 serum dilution as a predictor of response to therapeutic plasma exchange for HLA antibody-mediated rejection treatment and overall survival in lung transplant recipients: A two center study.

Clinical utility of 1:16 serum dilution as a predictor of response to therapeutic plasma exchange for HLA antibody-mediated rejection treatment and overall survival in lung transplant recipients: A two center study.

Book Review: From South Central to Southside: Gang Transnationalism, Masculinity, and Disorganized Violence in Belize City , By Adam Baird From South Central to Southside: Gang Transnationalism, Masculinity, and Disorganized Violence in Belize City. By BairdAdam. Philadelphia, PA: Temple University Press, 2024, 187 pp., $29.95 (paper); $94.50 (cloth).

Book Review: <i>From South Central to Southside: Gang Transnationalism, Masculinity, and Disorganized Violence in Belize City</i> , By Adam Baird From South Central to Southside: Gang Transnationalism, Masculinity, and Disorganized Violence in Belize City. By BairdAdam. Philadelphia, PA: Temple University Press, 2024, 187 pp., $29.95 (paper); $94.50 (cloth).

Labor Games, Citizenship, and Control: Book Review Essay of Weststar &amp; Legault and Wu LegaultM.WeststarJ. (2024). Not All Fun and Games: Videogame Labour, Project-based Workplaces, and the New Citizenship at Work. Montreal: Concordia University Press. 464 pp. $49.69 (paper).WuT. (2024). Play to Submission: Gaming Capitalism in a Tech Firm. Philadelphia: Temple University Press. 245 pp. $30.95 (paper).

As the game and tech industries face ongoing challenges, such as layoffs, employee overwork, and burnout, as well as responses, such as rising pushes for unionization, we have seen increasing amounts of scholarly work on these industries and their workers. Many existing studies, however, emerge from media studies, game studies, and cultural industries spaces, meaning they tend to theorize the game industry through these lenses, rather than engaging existing research in labor and occupation studies. The two books reviewed here begin the process of marrying these fields more closely, using theories of citizenship at work and labor games to explore worker agency and structural constraints in the game and tech industries. This review essay summarizes both titles and provides an overview of their strengths and weaknesses. It concludes that both books provide excellent additions to the field of game production studies, promoting new approaches to understanding what work does and could look like in these industries.

Global Trends in Assessing Social and Emotional Development in Early Childhood Education: A Bibliometric Analysis (2020–2025)

This bibliometric study uses extensive Scopus database records and VOS viewer software for detailed visualisation to examine worldwide research trends in the evaluation of social and emotional development in early childhood education from 2020 to 2025. In addition to highlighting important contributors like the University of Toronto, Macquarie University, and Temple University, the study finds notable increases in scholarly attention during this time frame. It also reveals changing research priorities that integrate digital media, mental health, and nutrition into developmental assessments. The study highlights the growing use of interdisciplinary approaches in the fields of public health, psychology, and education by identifying prevalent and developing themes using systematic keyword co-occurrence analysis. The results show that interdisciplinary research and cooperative efforts have grown significantly, making significant contributions to the advancement of international educational policies and practices. This research highlights the significance of evidence-based practices and international collaboration in promoting holistic child development, in addition to outlining the vast area of early childhood development assessment and offering crucial insights for educators and policymakers. The study’s findings point to a dynamic movement in early childhood education frameworks towards the integration of technology and holistic health views, providing a roadmap for future research and real-world applications.

A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation.

Electroencephalography (EEG) plays a crucial role in the diagnosis of various neurological disorders. However, small hospitals and clinics often lack advanced EEG signal analysis systems and are prone to misinterpretation in manual EEG reading. This study proposes an innovative hybrid artificial intelligence (AI) system for automatic interpretation of EEG background activity and report generation. The system combines deep learning models for posterior dominant rhythm (PDR) prediction, unsupervised artifact removal, and expert-designed algorithms for abnormality detection. For PDR prediction, 1530 labeled EEGs were used, and the best ensemble model achieved a mean absolute error (MAE) of 0.237, a root mean square error (RMSE) of 0.359, an accuracy of 91.8% within a 0.6 Hz error, and an accuracy of 99% within a 1.2 Hz error. The AI system significantly outperformed neurologists in detecting generalized background slowing (p = 0.02; F1: AI 0.93, neurologists 0.82) and demonstrated improved focal abnormality detection, although not statistically significant (p = 0.79; F1: AI 0.71, neurologists 0.55). Validation on both an internal dataset and the Temple University Abnormal EEG Corpus showed consistent performance (F1: 0.884 and 0.835, respectively; p = 0.66), demonstrating generalizability. The use of large language models (LLMs) for report generation demonstrated 100% accuracy, verified by three other independent LLMs. This hybrid AI system provides an easily scalable and accurate solution for EEG interpretation in resource-limited settings, assisting neurologists in improving diagnostic accuracy and reducing misdiagnosis rates.

Book Review: Refounding democracy through intersectional activism: How Progressive Era feminists redefined who we are, and what it means today by Sarvasy, W. SarvasyW. (2024). Refounding democracy through intersectional activism: How Progressive Era feminists redefined who we are, and what it means today. Temple University Press. Paperback. EAN: 9781439924259. 314 pages. $39.95.

Book Review: <i>Refounding democracy through intersectional activism: How Progressive Era feminists redefined who we are, and what it means today</i> by Sarvasy, W. SarvasyW. (2024). Refounding democracy through intersectional activism: How Progressive Era feminists redefined who we are, and what it means today. Temple University Press. Paperback. EAN: 9781439924259. 314 pages. $39.95.

Ruth Hall (1948-2023).

Ruth Hall, born on June 7, 1948, grew up in Painesville, Ohio and died after a prolonged illness on June 22, 2023. Ruth was a clinician, a university professor, and a scholar. In each role, she advocated for inclusion and diversity. Her scholarship addressed mental health issues of women of color, and she was very frequently invited to consult and provide diversity training to varied groups. Ruth had a lifelong interest in exercise and athletics. Midcareer, she earned a second master's degree in sports psychology at Temple University (1996). Ruth was a leader and advocate for diversity in multiple professional associations and won many awards for her contributions. Ruth was a serious and creative scholar and teacher, generous mentor, insightful clinician, and gracious leader. She was a consummate professional but should also be remembered as a spirited, fun-loving, adventurous woman with an infectious sense of humor. Hers is a light that will be sorely missed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Adaptasi Budaya Mahasiswa Indonesia pada Program Study of The U.S. Institutes (SUSI) 2024 di Amerika Serikat

This study aims to examine the communication strategies of Indonesian students in adapting during the Study of the U.S. Institutes (SUSI) 2024 program in the United States. The background of this research stems from the challenges participants face in adjusting to a multicultural environment, including language differences, social systems, and cultural expressions. Intercultural communication skills are a key factor in the success of this program. This study employs a qualitative design with a phenomenological approach to understand participants' direct experiences. Data were collected through in-depth interviews with four students who participated in SUSI at the University of Nevada, Reno, and Temple University, Philadelphia. The sample was selected using the convenience sampling method, and data analysis was conducted through reduction, presentation, and conclusion drawing. The results indicate that participants employed convergence, divergence, and over-accommodation strategies to adapt. Additionally, behavioral changes were observed after returning to Indonesia, such as cultural assimilation and integration. This study enriches the understanding of cultural adaptation in student exchange programs and serves as a reference for further research on the long-term impact of intercultural interactions on participants' identities and communication patterns. The implication of this research for future studies is the need for further exploration regarding the impact of cultural exchange programs on participants' identities and communication patterns in the long term.

OCR concerned with delayed accommodations

Case name: Letter re: Temple University, No. 03242023 (OCR 04/19/24).