Social media platforms like X (formerly Twitter) generate vast volumes of user-generated content that provide real-time insights into public sentiment. Despite the widespread use of traditional machine learning methods, their limitations in capturing contextual nuances in noisy social media text remain a challenge. This study leverages the Sentiment140 dataset, comprising 1.6 million labeled tweets, and develops predictive models for binary sentiment classification using Naive Bayes, Logistic Regression, and the transformer-based BERT model. Experiments were conducted on a balanced subset of 12,000 tweets after comprehensive NLP preprocessing. Evaluation using accuracy, F1-score, and confusion matrices revealed that BERT significantly outperforms traditional models, achieving an accuracy of 89.5% and an F1-score of 0.89 by effectively modeling contextual and semantic nuances. In contrast, Naive Bayes and Logistic Regression demonstrated reasonable but consistently lower performance. To support practical deployment, we introduce SentiFeel, an interactive tool enabling real-time sentiment analysis. While resource constraints limited the dataset size and training epochs, future work will explore full corpus utilization and the inclusion of neutral sentiment classes. These findings underscore the potential of transformer models for enhanced public opinion monitoring, marketing analytics, and policy forecasting.

Background: The COVID-19 pandemic posed significant psychological challenges to frontline healthcare workers (HCWs), including anxiety, stress, and emotional strain. Aim: This study investigates the psychological impact on HCWs during the pandemic and explores coping strategies employed to manage distress. Methods: An integrative review was conducted using 24 studies published between January and December 2020. These studies were analyzed to identify common psychological outcomes and coping mechanisms among HCWs. Results: Healthcare workers experienced significant psychological distress during the COVID-19 pandemic, including anxiety, stress, insomnia, and depression. Anxiety was the most commonly reported issue, particularly among women, younger healthcare workers, and frontline staff. Stress levels were heightened by high workloads, exposure to COVID-19 patients, and inadequate protective measures. Coping strategies and self-care behaviors, such as seeking social support and utilizing institutional resources, varied in effectiveness across populations. Conclusion: The findings highlight the urgent need for targeted mental health support and resilience programs for HCWs, ensuring they are better equipped to face future health crises.

Simulation-based learning (SBL) is widely recognized as an effective educational approach that bridges theory and practice in nursing education. Despite its global adoption, limited research has examined the experiences of Filipino nursing students with SBL, particularly in resource-constrained settings. This study explored the perspectives of Bachelor of Science in Nursing students from a university in Metro Manila, Philippines, on the impact of SBL on their skills, emotional responses, and challenges encountered. A descriptive qualitative design was employed using purposive sampling of ten students who had participated in at least one SBL activity. Data were collected through semi-structured interviews and short written reflections and analyzed thematically following Braun and Clarke’s framework to capture nuanced experiences. Three major themes emerged from the analysis. First, students reported initial anxiety, nervousness, and stress during their early SBL experiences, which gradually transformed into confidence, adaptability, and resilience as they gained familiarity and competence. Second, SBL enhanced technical and cognitive skills such as clinical judgment, decision-making, teamwork, and patient-centered care, supporting students’ readiness for real-world practice. Third, students identified resource limitations, insufficient equipment, and time constraints as significant barriers to optimal learning, though these challenges also fostered creativity and perseverance. The findings demonstrate that SBL fosters technical competence, critical thinking, and professional growth but requires institutional support to address resource constraints and faculty development needs. This study underscores the importance of expanding SBL in Philippine nursing curricula to align with international best practices and to contribute to Sustainable Development Goals 3 (good health and well-being), 4 (quality education), and 5 (gender equality).

Background: Socioeconomic status (SES) is widely associated with improved health, behavioral, and educational outcomes. However, emerging research suggests that these benefits are not uniformly experienced across populations or contexts. The theory of Marginalization-related Diminished Returns (MDRs) has primarily focused on racial and ethnic disparities, showing that individuals from racially marginalized groups often experience weaker protective effects of SES. There is a lack of evidence on geography—particularly rural residence—as a moderator of SES effects. Objective: This review explores how place, especially rural contexts in the U.S., shapes the extent to which SES translates into improved outcomes. We extend the MDRs framework to include place-based and geography-based marginalization, arguing that even among non-Hispanic White populations, rural residence can lead to diminished returns on education, income, and other forms of capital. Content: Drawing on theoretical models such as Fundamental Cause Theory and Bronfenbrenner’s Ecological Systems Theory, and synthesizing empirical findings from studies of academic achievement, substance use, and educational aspirations, this review highlights how structural disadvantages in rural areas weaken the effectiveness of individual and family-level resources. Conclusion: Rural health and educational disparities are not solely due to a lack of resources but may also reflect systemic conditions that erode the value of existing resources. Policy interventions must be place-aware and address the contextual constraints that limit opportunity. Future research should more explicitly test how geography moderates the effects of SES across a range of outcomes and populations.