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Reverence And Social Cohesion: How Does Our Relationship With Elements Of “Beyondness” Relate to Our Relationship With Each Other?
Diab, Ahmad; 201201328; Hanafi, Sari; Ghaddar, Nessreen; Ayoub, Mona; Kassir, Alexandra; MA; Department of Sociology, Anthropology and Media Studies; Faculty of Arts and Sciences
The past couple of decades have witnessed critical socio-economic, political and environmental changes embedded within late modernity’s structures, worldviews and value systems. Climate change, increased social and political polarization, the rise of authoritarianism, global migration and class precariousness are some of the complex phenomena that have emerged within our everyday spheres complemented by exponential technological advancement and global linkages. Parallel to these upheavals and partially as a response to them, inquisitions and research within academic and policy-making circles have increasingly focused on questioning the fundamentals of sociological tools and methods to find solace in post-neoliberal imaginaries. Concepts like social cohesion, a multi-dimensional construct in itself, have garnered more interest in analytical and conceptual endeavors in response to a lived and perceived risk of increased social fragmentation. Within this growing corpus of research, a common call is that of the (re)integration of elements of relationality, symbolism, shared value systems, and phenomenological analysis of solidarity-empowering virtues such as care and social love. These elements are not new to the sociological discipline, yet have been subverted as if floating next to the sinking ship of our hyper-rationalistic, utilitarian and capitalistic global systems. Within this scope, I developed an emerging “reverence index” to partially ground these virtues and to investigate linkages between social cohesion, on the one hand, and proxies for phenomenological attributes of relationships with a “transcendent” based on respect and awe, on the other. I study the effects of personal indicators, traits and values that relate to reverence and religiosity on social cohesion using secondary data from the 7th wave of the World Value Survey covering 66 countries and run regression analyses to explore these relationships on a global, regional and country level. Results confirm a significant positive relationship between reverence and social cohesion even when controlling for country-level and individual-level variables. Further, a negative interaction between reverence and organized faith in relation to social cohesion is witnessed within the global sample. In specific sub-groups, going to one extreme at the sake of the other—high reverence, low religiosity or high religiosity, low reverence—was relatively detrimental to cohesion. Inter-regional analysis highlighted shifting dynamics of the reverence-cohesion relationship and speaks to both secularization theories—showing how reverence may potentially substitute for religiosity in secular societies and vice versa— and, theories of embedded faith and social reward in high‐religiosity societies. The findings support the multi-disciplinary and emerging call to re-integrate the phenomenological, symbolic and reverential within sociological inquiry.
Patterns, Trends and Profiles of Acute Poisoning and Predictors of Hospitalization and Mortality in Prehospital EMS Records in a U.S. Based Dataset
Abdallah, Ghinwa; 202373951; Ghandour, Lilian; Al Hajj, Samar; Mowafi, Hani; MS; Department of Epidemiology and Population Health; Faculty of Health Sciences
Objectives: To describe the distribution of poisoning cases reported in the ESO Collaborative dataset by sociodemographic factors and mechanisms of poisoning. To investigate predictors associated with hospitalization and mortality among poisoned patients treated in the pre-hospital EMS care. Methods: A cross-sectional design, secondary data with a multimethod analysis strategy. The ESO Data Collaborative records of the 2022 calendar year were used. It is a de-identified prehospital EMS electronic health records containing more than 1,300 EMS agencies across the U.S. A sample of 5,278,986 poisoning cases was obtained. Data management, cleaning, and analysis were performed using RStudio version 2025.05.0+496. Results: The annual incidence of poisoning encounters during 2022 was 0.41 based on the ESO dataset. The majority of encounters were adult males aged between 20 and 40 years old. There is an even distribution of poisoning cases over the day, with 3 peak times at mid-day, afternoon, and late afternoon hours. 67.3% of poisoning encounters were among the white race, and more than half of the cases (54.77%) occurred in the southern region of the United States. The majority of cases (67.5%) were in urban areas; however, rural areas had significantly more fatal poisoning, and super rural areas had significantly higher severity of poisoning cases. Thematic content-analysis showed that the majority of poisoning encounters were due to substance use, among substances, opioids and alcohol were the most commonly used. Significant predictors of hospitalization were an increase in weight, the Midwest and western geographical regions, a super rural area, and having at least 1 impairment. Significant predictors of mortality were older age, an increase in weight, female gender, the Midwest and south geographical regions, rural area and having at least 1 impairment, and the patient being unconscious. Conclusion: Identified significant predictors in this study help in preventing future poisoning-related hospitalizations and deaths by rapid prediction of severity (triage at the prehospital level). In the aim of reducing the burden of poisoning, the integration of prehospital EMS records with hospital records provides a comprehensive understanding of patient disposition and clinical outcomes. Predictive modeling and machine learning techniques can be used to facilitate the identification of high-risk patients using real-time prehospital data.
Understanding Post-Conflict Mental Health: Assessment of PTSD, Depression, General Health and Life Events in Civilian Population One Year after the 2006 War in South Lebanon
(Journal of Traumatic Stress Disorders & Treatment, 2013) Farhood, Laila; Dimassi, Hani; Strauss, Nicole L.
Assessing the psychological impact of war is crucial to meet the needs of communities following conflict. To date, mental health in Lebanon’s southern civilians has not been assessed in relation to the 2006 War. In 2007, face-to-face interviews were conducted in ten villages in South Lebanon. The sample, consisting of 991 adults, was chosen through random sampling using a crosssectional design. The study evaluated PTSD, traumatic events, depression and general health status. Of the total sample, 17.8% met threshold criteria for PTSD, 14.7% for depression and the average GHQ score was 4.31. Significant differences were observed across villages. This study revealed that war-related life events and exposure are highly associated with psychiatric problems one year following a violent conflict.
Investigating the Role of Artificial Intelligence in Enhancing Decision-Making and Structural Health Monitoring Processes in Construction Engineering and Management: A Literature Review
(2025-10-14) Ezzeddine, Aya; 100000048; Franzè, Claudia; Sadek, Salah; Abou Chakra, Hadi; MS; Department of Industrial Engineering and Management; Faculty of Engineering; American University of Beirut – Mediterraneo
Artificial Intelligence (AI) is gaining an increasing attention in the Construction Engineering and Management (CEM) industry for its potential to support decision-making processes, improve operational efficiency, and enhance overall infrastructure safety. However, existing research remains fragmented with limited integration between studies focusing on AI- enabled decision-making and those addressing Structural Health Monitoring (SHM). This study conducts a systematic review to examine how AI contributes to these critical areas in the industry. The review followed an approach to ensure transparency and academic rigor by conducting a comprehensive search through different databases. A total of 89 articles were selected based on defined inclusion and exclusion criteria. Findings reveal a growing application of AI in predictive analytics, risk mitigation, real-time analysis and proactive maintenance. However, challenges such as organizational resistance, workforce readiness, data related concerns, and regulatory constrains remain not fully explored. The study proposes a conceptual framework that integrates two separate research area into one unified framework, AI-driven decision making and SHM, linking safety and efficiency outcomes together. The framework offers a structured foundation for future empirical testing and addresses critical gaps in current theory by explaining both the mechanisms and contextual factors shaping AI’s effectiveness in real-world construction settings. The study contributes to theory by offering a systems-oriented perspective that connects technological, human, and organizational factors. Practically, it provides a foundation for developing guidelines and future empirical studies aimed at validating the framework.
Evaluating International AI Governance: Balancing Technological Innovation in the Development of a Global Model for Generative AI
(2025-10-14) Markarian, Marina; Constantinou, Charalampos; Daou, Alain; Pavlou, Christodolous; Nicolaides, Christos; M.S.B.A.; Faculty of Business; American University of Beirut – Mediterraneo
Generative Artificial Intelligence (GenAI) is advancing at an unprecedented pace, transforming industries, societies, and global technological landscapes. This thesis investigates the readiness of existing international AI governance frameworks to manage the projected growth of GenAI while safeguarding human security and fostering technological innovation. Adopting a governance and risk management lens, the thesis integrates perspectives from political science and business to evaluate how international and corporate frameworks align with the projected growth of generative AI. By combining quantitative market analysis with qualitative framework evaluation, the research identifies the trajectory of GenAI adoption through 2027, highlighting critical risks related to economic stability, privacy, and disinformation. Key frameworks, including the EU AI Act, UNESCO Recommendations, Gartner AI TRiSM, and others, are assessed for their effectiveness, transparency, enforcement, adaptability, and global applicability. Findings reveal that while these frameworks provide valuable guidance on ethical AI deployment and risk mitigation, they remain fragmented, unevenly enforced, and insufficiently flexible to keep pace with rapid innovation. Indicators such as cross-sector adoption, frequency of security breaches, and ongoing ethical debates are proposed to guide the development of future governance models. The thesis concludes by emphasizing the urgency of collaborative action among developers, policymakers, and users to establish adaptive, internationally aligned frameworks that balance innovation with human-centered safeguards. By serving as a foundational manuscript, this work provides a roadmap for designing a global AI governance model capable of addressing cross-cultural, sectoral, and technological complexities, ensuring the safe, equitable, and responsible integration of GenAI into society.