University of Central Arkansas

African refugee male sex workers (ARMSWs) in Europe experience overlapping forms of marginalization, including racism, legal precarity, and exclusion from formal labour markets, which collectively shape HIV vulnerability. However, little is known about how economic precarity drives engagement in sex work and HIV-related behaviors among ARMSWs in Italy. We used a sequential exploratory mixed-methods design within the Refugee Initiative for Sexual Health (RIfESH). First, we conducted 20 in-depth interviews and 2 focus group discussions with ARMSWs in Verona, Turin, and Milan to explore pathways into sex work, migration-stage experiences, and health-care access. Qualitative themes informed the development of a structured REDCap survey administered to 150 ARMSWs recruited through venue-based and snowball sampling. We used descriptive statistics and chi-square tests with Cramér's V to examine associations between economic drivers (e.g., need for food, rent, and lack of other income) and sexual risk-taking, HIV service awareness, health-care discrimination, and reliance on traditional medicine. Integration occurred through side-by-side comparison and a joint display. Most participants (79%) relied on sex work as their sole income source, and 72% had entered sex work by age 25. Economic pressures related to rent, food, and having no other income were significantly associated with condomless sex, lower HIV service awareness, greater use of traditional medicine, and higher reports of hospital stigma and unmet health-care needs. Qualitative narratives showed how poverty, migration-stage coercion, racism, and client power constrained condom negotiation and access to care, often forcing participants to accept unsafe practices to survive. Engagement in sex work among ARMSWs in Italy is primarily a survival response to structural exclusion rather than individual "risk-taking." HIV prevention and care must be coupled with economic empowerment, anti-racist and stigma-free health services, and policies that recognize male refugee sex workers as a priority population.

The purpose of this cross-sectional, exploratory study was to investigate which topics under the second edition of the National Sex Education Standards (NSES) were valued and which high school school-based sex education program (abstinence vs. comprehensive) was preferred among selected college students at a mid-sized Southern university. The survey was used to explore what National Sex Education Standards hold the most or least value based on personal beliefs. The survey instrument included every 10th-grade standard from the NSES and demographic questions to determine the perceived importance of each topic within the NSES, as well as differences among groups based on demographics and other variables. Results indicated that the majority of participants perceived all standards and topics as important and preferred that high school students receive a comprehensive sex education curriculum. Findings from this study have implications for curriculum development or expansion, guiding administrators and educators in enhancing the comprehensiveness of high school school-based sex education programs.

From stay-at-home mom to successful founder, Rhea Lana Riner of Rhea Lana’s Children’s Consignment found herself catapulted into an unexpected and highly public legal battle with a major federal agency. When the United States Department of Labor declared that her business did not comply with employment laws, Rhea Lana’s entire business model was at risk. How did a loving mother who built a grassroots enterprise to help her family and community become the target of a federal agency? This incident examines how the founder of a small business responded when the federal government challenged the foundation of her business model. The critical incident highlights the important decisions she made under pressure and explores how her actions illustrated both transformational leadership and crisis management.

In recent years, understanding the emotional dynamics of patient-clinician interactions has emerged as a critical topic in healthcare research. Speech Emotion Recognition (SER) provides critical insights to enhance patient care, diagnostic precision, and therapeutic effectiveness. In this paper, we present a text-based framework for Speech Emotion Recognition specifically designed for healthcare scenarios, integrating advanced transformer-based models including T5, BERT, and XLNet. Our proposed framework analyzes transcribed textual data, enabling the identification of potential emotions expressed by patients and healthcare providers. Audio recordings from interactions between patients and clinicians-including doctors and psychiatrists-are transcribed using the Whisper model, ensuring high transcription quality. We evaluated the framework’s performance on a dataset comprising clinical conversations capturing a variety of emotional expressions relevant to healthcare contexts. Our experimental results demonstrate that our framework predicts six primary emotional states, including Happiness, Anger, Fear, Sadness, and Surprise, as well as distinguishing between positive and negative sentiments. Among the evaluated models, T5 exhibited the highest mean confidence score at $89.12 \%$, significantly outperforming RoBERTa ($78.44 \%$) and XLNet ($36.02 \%$) in capturing emotional content from clinical dialogues. These findings highlight the potential of SER to aid healthcare professionals by providing deeper insights into patients’ emotional states, supporting communication, and improving understanding of patients’ sentiment.

Ophthalmic disorders such as diabetic retinopathy, agerelated macular degeneration, and glaucoma are primary preventable causes of blindness. Early and precise diagnosis is critical to ensuring early therapeutic intervention and mitigating long-term ophthalmic and systemic complications. In this study, we propose a novel deep learning based hybrid framework, RetinaXNet, that integrates DenseNet201 with MultiHead Self-Attention (MHSA) to enhance diagnostic performance and model interpretability in fundus image analysis. The hybrid framework integrates tailored preprocessing techniques to optimize retinal image quality and leverages explainable AI (XAI) through Gradient-weighted Class Activation Mapping (Grad-CAM). This is particularly to provide clinically relevant visualizations of disease-localized regions. Unlike prior models that have limitations in generalizability, poor interpretability, or excessive computational overhead, the proposed approach achieves a balanced trade-off between predictive accuracy, transparency, and efficiency. Experimental evaluation on benchmark datasets demonstrates a test accuracy of 99.26%, a cross-validation accuracy of 99.05%, an F1-score of 99.26%, and a Cohen’s Kappa coefficient of 0.9902. The performance highlights not only high predictive fidelity but also strong inter-rater agreement. Grad-CAM visual outputs align consistently with ophthalmologist-marked pathological regions for reinforcing the model’s potential for clinical adoption. Moreover, the lightweight design supports real-time deployment via a web-based interface, making it suitable for use in low-resource or point-of-care settings.

Artificial intelligence (AI) is transforming corporate decision-making; however, current Corporate Social Responsibility (CSR) frameworks often fall short in addressing AI’s ethical risks. This paper proposes Corporate Algorithmic Responsibility (CAR), a governance model that extends CSR by integrating techno-moral accountability. Built on four pillars, CAR draws from AI ethics, corporate governance, and regulatory studies. The framework is actionable, aligned with global standards, and tailored for sectors like fintech, healthtech, and digital retail. By embedding accountability into AI systems, CAR offers a robust, interdisciplinary approach to ensure corporate AI deployment is ethical, equitable, and resilient in real-world contexts.

Suicide is more prevalent in rural areas than in their urban counterpart. Social isolation, loss of manufacturing, unemployment, and poor access to health care are causes of higher fatal suicides in rural areas. These causes are rooted in Emile Durkheim’s concepts of social integration and social regulation. Durkheim argued that religion could provide individuals with sufficient levels of social integration and regulation to prevent suicidal behavior. However, today, rural areas are much different than at the time of Durkheim’s writing. Individuals in rural areas have more options for what type of religion they can choose. Increased religious heterogeneity may lessen the social integration and regulation powers of a religion by breaking social networks. Examining 1,318 rural counties, the analysis found that as a county became more religiously heterogeneous, there was an increase in fatal suicides. Moreover, non-adjacent to metro rural counties had significant increases in suicide when the county had increased levels of religious heterogeneity. The findings also demonstrate that religion does provide an overall protective effect against suicide.