The college investment decision for nontraditional students: Factors affecting the choice of postsecondary enrollment and quality

As the shift in demographics and the aging population of the United States make their presence felt, colleges and universities throughout the country must address the question of supporting the goals of the non-traditional student in higher education. While it is difficult to characterize a typical non-traditional student, it is important to analyze the role that certain demographic factors play in the student\u27s decision to go to college.;We hypothesize that the non-traditional student\u27s decision to attend college is influenced by several factors that, in broad, general terms, include family background characteristics, the student\u27s demographic profile and aptitude, and external economic conditions and labor demand.;The influence of family background is incorporated in our model through variables that describe the environment in which a student has grown up. Parental educational attainment, in particular, serves as a proxy for the attitudes toward education that may have shaped the student\u27s perceptions toward higher education. Other factors such as parental income, the father\u27s Duncan socio-economic index, the number of siblings, and the birth order, describe the family\u27s capacity to invest in higher education. In short, the factors hypothesized to influence student enrollment were indeed shown to have the predicted effects.;By understanding non-traditional students and what influences their enrollment decisions, we will have a better understanding of how to serve this growing segment of the population within higher education. In particular, by determining the type of institutions that these students enroll in, institutions themselves can be made more aware of the particular needs of these students so as to better able to meet them

The impact of Information Communication Technology (ICT) on Brazil, Russia, India and China’s (BRIC) Employment and Economic Indices

This research seeks to describe the impact of Information Communication Technology (ICT) on employment and economic indices in Brazil, Russia, India and China (BRIC). It examines the relationship between the United Nations’ International Telecommunication Union’s ICT sub index, the World Economic Forum’s Networked Readiness Index (NRI), Gross Domestic Product (GDP), unemployment data as well as employment data by sector between the years of 2006 to 2012. The purpose of this research is to quantifiably measure the relationship between technological advancement, economic growth and employment trends across the BRIC countries, while also describing various government initiatives taken to promote technology. Our research concluded that there is in fact a significant positive correlation between technology (as measured by NRI and the ICT Development Index (IDI)) and a nation’s GDP

Effectiveness of Self-Help Mindfulness on Depression, Anxiety and Stress

Introduction: Growing evidence shows that mindfulness based on self-help interventions have advantages for physical and psychological health in different populations. The mindfulness based emotional balance is a new program and efficacy of which has not been investigated in self-help format.  Methods: This study was a randomized control trial (RCT) with wait-list control. After screening and applying inclusion and exclusion criteria eighty students of Kermanshah University of medical sciences were assigned randomly to the mindfulness-based emotional balance self-help or the wait list control group. Anxiety, depression, stress, and mindfulness were measured prior to the intervention after the intervention, and two-month after end of intervention as follow-up. DASS-21 and MAAS were used.  Results: Significant decrease in anxiety, depression, and stress in addition to a significant increase in mindfulness in the experimental group in the posttest. However, there were no significant changes in any of the aforesaid measures in the wait list control group. The resultant benefits were persistent in the two-month follow-up. Conclusion: Based on the results of this study, this type of treatment can be used as an efficient and cost-effective method to improve psychological problems such as stress, depression and anxiety.  Declaration of Interest: Non

MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses using Large Language Models

Large Language Models (LLMs) have shown impressive capabilities in generating human-like responses. However, their lack of domain-specific knowledge limits their applicability in healthcare settings, where contextual and comprehensive responses are vital. To address this challenge and enable the generation of patient-centric responses that are contextually relevant and comprehensive, we propose MedInsight:a novel retrieval augmented framework that augments LLM inputs (prompts) with relevant background information from multiple sources. MedInsight extracts pertinent details from the patient's medical record or consultation transcript. It then integrates information from authoritative medical textbooks and curated web resources based on the patient's health history and condition. By constructing an augmented context combining the patient's record with relevant medical knowledge, MedInsight generates enriched, patient-specific responses tailored for healthcare applications such as diagnosis, treatment recommendations, or patient education. Experiments on the MTSamples dataset validate MedInsight's effectiveness in generating contextually appropriate medical responses. Quantitative evaluation using the Ragas metric and TruLens for answer similarity and answer correctness demonstrates the model's efficacy. Furthermore, human evaluation studies involving Subject Matter Expert (SMEs) confirm MedInsight's utility, with moderate inter-rater agreement on the relevance and correctness of the generated responses

A Comparison between the Ability of Revised Trauma Score and Kampala Trauma Score in Predicting Mortality; a Meta-Analysis

Introduction: Describing injury severity in trauma patients is vital. In some recent articles the Revised Trauma Score (RTS) and Kampala Trauma Score (KTS) have been suggested as easily performed and feasible triage tools which can be used in resource-limited settings. The present meta-analysis was performed to evaluate and compare the accuracy of the RTS and KTS in predicting mortality in low-and middle income countries (LMICs). Methods: Two investigators searched the Web of Science, Embase, and Medline databases and the articles which their exact number of true-positive, true-negative, false-positive, and false-negative results could be extracted were selected. Sensitivity and subgroup analysis were performed using Stata software version 14 to determine the factor(s) affecting the accuracy of the RTS and KTS in predicting mortality and source(s) of heterogeneity. Results: The heterogeneity was high (I2 > 80%) among 11 relevant studies (total n = 20,631). While the sensitivity of the KTS (0.88) was slightly higher than RTS (0.82), the specificity, diagnostic odds ratio, negative likelihood ratio, and positive likelihood ratio of the KTS (0.73, 20, 0.16, 3.30, respectively) were lower than those of the RTS (0.91, 45, 0.20, 8.90, respectively). The area under the summary-receiver operator characteristic curve for KTS and RTS was 0.88 and 0.93, respectively. Conclusion: However, regarding accuracy and performance, RTS was better than KTS for distinguishing between mortality and survival; both of them are beneficial trauma scoring tools which can be used in LMICs. Further studies are required to specify the appropriate choice of the RTS or KTS regarding the type of injury and different conditions of the patient

Self-powered cardiac pacemaker by piezoelectric polymer nanogenerator implant

Self-powered biomedical implants improve the life of patients and lower the risks associated with battery replacement. Piezoelectric energy harvesters that generate electricity from the cardiac motions are among the potential candidates to be used in self-powered implants, such as cardiac pacemakers. In this context, lead-based ceramic piezoelectric nanogenerators (PNGs) were emerged, which are toxic and susceptible to fatigue crack, causing harm to the patients. Polyvinylidene fluoride-trifluoroethylene (PVDF-TrFE)-based films were also developed as cardiac energy harvesters. Here, we show a battery-free heart pacemaker that is powered by the generated electricity of a biocompatible and flexible piezoelectric polymer-based nanogenerator (PNG) from the cardiac motions of the left ventricle. The PNG is comprised of composite nanofibers of poly(vinylidene fluoride) (PVDF) and a hybrid nanofiller made of zinc oxide (ZnO) and reduced graphene oxide (rGO). The composite nanofiber is optimized towards achieving a large power output. In vivo implanted optimized PNG can successfully harvest 0.487 μJ from every heartbeat, which is conveniently larger than the pacing threshold energy for the human heart. The successful demonstration of a self-powered pacemaker places the polymer-based PNGs among the viable candidates for self-powered biomedical implants.</p

Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications

Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health, enabling more personalized and effective care. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs, focusing on their role in integrating disparate healthcare data and enhancing patient care through a unified health perspective. In addition, this review also discusses the complexities of PCKG development, including ontology design, data integration techniques, knowledge extraction, and structured representation of knowledge. It highlights advanced techniques such as reasoning, semantic search, and inference mechanisms essential in constructing and evaluating PCKGs for actionable healthcare insights. We further explore the practical applications of PCKGs in personalized medicine, emphasizing their significance in improving disease prediction and formulating effective treatment plans. Overall, this review provides a foundational perspective on the current state-of-the-art and best practices of PCKGs, guiding future research and applications in this dynamic field

Coping strategies in children of parents deceased from cancer and children of parents healed from cancer

This study aimed to compare coping strategies in children of parents deceased from cancer and children of parents healed from cancer in the city of Shiraz, Iran. One-hundred and fifteen people [58 children of parents healed from cancer and 57 children of parents deceased from cancer] were recruited in this study via a convenience sampling method. Coping Inventory for Stressful Situations was used to measure different types of coping strategies [task-oriented coping strategy, emotion-oriented coping strategy, and avoidance coping strategy]. The results showed that the children of parents healed from cancer used task-oriented coping strategy significantly more than children of parents deceased from cancer. Moreover, the results showed that the use of emotion-oriented coping strategy in children of parents deceased from cancer was significantly more than children of parents healed from cancer. No significant difference was observed between the two groups in the use of avoidance coping. This study highlights the importance of coping strategies in families with a cancer parent which demands the importance of teaching appropriate coping strategies in order to reduce the adverse consequence of cancer in the family