Search Results (128)

Background: Family involvement in promoting patient safety is a strategy that is increasingly recognized. Nurses have a major role to play here. This study aims to know the family care process developed by nurses and explore the logistic process identified as support for the development of family care in ensuring patient safety in the hospital. Methods: An interpretative qualitative study was conducted through semi-structured interviews with 10 nurses selected by convenience. Content analysis was performed using Atlas.ti 9.1.7 software and Bardin’s methodology. Results: Two large families were identified according to the nature of the strategies mentioned by the participant/s: assistance process and logistic process, 5 categories and 23 subcategories. Care process categories: initial assessment, planning, and implementation. Categories of the logistic process: material and human resources and organization. Conclusions: To implement patient and family-centered care to ensure patient safety, it is necessary to adjust the care and logistic process. At the care level, the importance of the nursing process in the organization of care is evident. At the logistical level, organizational initiatives are needed to stimulate and support this philosophy of care and to intervene at the level of human and material resources. Full article

Software requirements are primarily classified into functional and non-functional requirements. While research has explored automated multiclass classification of non-functional requirements, functional requirements remain largely unexplored. This study addressed that gap by introducing a comprehensive dataset comprising 9529 functional requirements from 315 diverse projects. The requirements are classified into five categories: ubiquitous, event-driven, state-driven, unwanted behavior, and optional capabilities. Natural Language Processing (NLP), machine learning (ML), and deep learning (DL) techniques are employed to enable automated classification. All software requirements underwent several procedures, including normalization and feature extraction techniques such as TF-IDF. A series of Machine learning (ML) and deep learning (DL) experiments were conducted to classify subcategories of functional requirements. Among the trained models, the convolutional neural network achieved the highest performance, with an accuracy of 93, followed by the long short-term memory network with an accuracy of 92, outperforming traditional decision-tree-based methods. This work offers a foundation for precise requirement classification tools by providing both the dataset and an automated classification approach. Full article

The link between ESG and financial performance is still under debate. In this study, we explore which aspects of ESG specifically drive market valuations through both systematic and idiosyncratic risk channels. We analyze the impact of the three core ESG pillars, 10 subcategories, and associated controversies on market valuations in the energy sector. This analysis reveals that the environmental factor has a stronger impact (regression coefficient = 0.05) than the governance factor (regression coefficient = 0.003), emphasizing the need to prioritize environmental performance in ESG strategies. The positive coefficients for environmental resource use (0.005) and innovation (0.008) indicate that investments in efficiency and clean technologies are beneficial, while the negative coefficient for emissions (−0.004) underscores the risks associated with poor emissions management. These findings suggest that environmental risks currently outweigh governance risks for the energy sector, reinforcing the importance of aligning governance practices with environmental goals. To maximize ESG effectiveness, energy firms should focus on measurable improvements in resource efficiency, innovation, and emissions reduction and transparently communicate this progress to stakeholders. The evidence suggests that energy firms approach the ESG landscape differently, with sustainability leaders benefiting from higher valuations, particularly when ESG efforts are aligned with core competencies. However, many energy companies under-invest in value-creating environmental initiatives, focusing instead on emission management, which erodes value. While they excel in emission control, they lag in innovation, missing opportunities to enhance valuations. This underscores the potential for ESG risk analysis to improve portfolio performance, as sustainability can both create value and mitigate risks by factoring into valuation equations as both risks and opportunities. This study uniquely contributes to the ESG–financial performance literature by disentangling the specific ESG dimensions that drive market valuations in the energy sector, revealing that value is created not through emission control but through strategic alignment with eco-innovation, governance, and social responsibility. Full article

Gender-based violence is a social problem that affects the health of women in all countries, cultures, ages and economic status; its complexity requires a transdisciplinary approach. However, this study will emphasize the care provided by nursing in emergency services. Objectives: To explore the experiences of nursing professionals in the emergency area in relation to the approach to gender-based violence considering care management skills. Methods: A qualitative study with semi-structure interviews was conducted; the saturation was reached with the participation of 20 nursing professionals from emergency rooms. The study employed qualitative content analysis and the software QCAmap for organization and extraction of analysis. Results: The category called “Specific Approaches to Risk and Vulnerability” was characterized by its comprehensiveness, evident in its association with experiences in screening, follow-up, measures to prevent re-victimization, and ensuring privacy. The most relevant subcategories, based on the redundancy, were empathy, which encompasses affective aspects; education on forms of abuse not recognized by the victim; and multidisciplinary and multisectoral action to address patients’ needs effectively. Conclusions: Nursing professionals valued both the psychological and physical aspects of patients, highlighting the importance of multidisciplinary coordination and the protection of integrity. Awareness and offering help are key interventions during the activation of protocols for addressing gender-based violence. Full article

The aging population and the increasing prevalence of chronic diseases necessitate new healthcare models. Case-Management Nurses (CMNs) emerge as a promising alternative to enhance patient care. Objective: To explore CMNs’ perceptions of the impact on health, resources, and professional satisfaction. Methods: A qualitative study using semi-structured interviews was conducted with CMNs from a southern Spanish province. This study adheres to the Standards for Reporting Qualitative Research (SRQR). The population consisted of 61 CMNs. Three analytical categories of a theoretical or deductive nature were identified, directly related to this study’s objective. This study was approved by the Andalusian Biomedical Research Ethics Committee (Code: 1139-N-22) and conducted in accordance with the Declaration of Helsinki. Data analysis was performed using ATLAS.ti. Results: The sample was comprised of 31 CMNs (24 women). The mean age was 56.3 years. A total of 12 CMNs had more than 20 years of experience. Interviews were conducted between October and November 2022. Within each category, different emerging subcategories were identified: 1. Impact on health: Patients and caregivers. 2. Impact on resources: Computer tools, effectiveness/efficiency of the CMNs, and material resources. 3. Impact on professional satisfaction: Positive professional satisfaction and negative professional satisfaction. Conclusions: The findings suggest that the practice of CMNs in primary care improves the health and quality of life of patients and their caregivers while reducing healthcare resource utilization. CMNs reported high levels of job satisfaction. These findings support the implementation of this care model to optimize the management of chronic patients in home and residential care settings. However, larger-scale quantitative studies are needed to confirm these results and explore their generalizability. Full article

The exploration of the Fengcheng Formation has revealed the characteristic orderly coexistence of conventional reservoirs, tight reservoirs, and shale reservoirs, constituting a full spectrum of reservoir types, and is important for unconventional oil and gas exploration and development. Affected by frequent volcanic tectonic movement, hot and dry paleoclimate, and the close provenance supply distance, unique saline–alkaline lacustrine deposits formed during the depositional period of the Fengcheng Formation. The lithologies of the Fengcheng Formation are highly diverse, with endogenous rocks, volcanic rocks, terrigenous debris, and mixed rocks overlapping and forming vertical reservoir changes ranging from meters to centimeters. Owing to the complexity of rock types and scarcity of rock samples, the evaluation of reservoirs in mixed-rock has progressed slowly. Hence, we aimed to evaluate the characteristics of Fengcheng Formation shale oil reservoirs. Centimeter-level core characteristics were analyzed based on the lithological change and structural characteristics. To investigate the lithofacies of the Fengcheng Formation in the Mahu Sag and factors affecting reservoir development, high-frequency sedimentary structures were analyzed using sub-bio-buffering electron microscopy, energy spectrum testing, and fluorescence analysis. The results showed that the shale oil reservoirs in the study area can be divided into four categories: glutenite, volcanic rock, mixed rock, and endogenous rock. The reservoir capacity has improved and can be divided into eight subcategories. Mixed-rock reservoirs can be further divided into four subcategories based on differences in structure and composition. Differences in the bedding and dolomite content are the main factors controlling the differences in the physical properties of this type of reservoir. This study provides a reference for the classification and characteristic study of shale oil reservoirs in saline–alkali lake basins. Full article

As a prevalent metabolic disorder, the increasing incidence of diabetes imposes a significant burden on global healthcare. Flavonoids in natural phytochemical products exhibit notable hypoglycemic properties, making them potential alternatives for diabetes treatment. This article summarizes the hypoglycemic properties of flavonoid subcategories studied in recent years, including flavones, isoflavones, flavonols, flavanols, and others. The relevant targets and signal pathways, such as α-amylase, α-glucosidase, insulin receptor substrate (IRS)/phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT), PKR-like endoplasmic reticulum kinase (PERK)/eukaryotic initiation factor 2α (eIF2α)/activation transcription factor 4 (ATF4)/C/EBP homologous protein (CHOP), etc., are also elaborated. Additionally, flavonoids have also been demonstrated to modulate the gut microbiota and its metabolites. Through the aforementioned mechanisms, flavonoids mainly suppress carbohydrate metabolism and gluconeogenesis; facilitate glucose uptake, glycogenesis, and insulin secretion; and mitigate insulin resistance, oxidative stress, inflammation, etc. Notably, several studies have indicated that certain flavonoids displayed synergistic hypoglycemic effects. In conclusion, this article provides a comprehensive review of the hypoglycemic effects of the flavonoids investigated in recent years, aiming to offer theoretical insights for their further exploration. Full article

Magnetic resonance imaging (MRI) is routinely used to confirm the suspected diagnosis of anterior cruciate ligament (ACL) injury. Recently, many studies explored the role of artificial intelligence (AI) and deep learning (DL), a sub-category of AI, in the musculoskeletal field and medical imaging. The aim of this study was to review the current applications of DL models to detect ACL injury on MRI, thus providing an updated and critical synthesis of the existing literature and identifying emerging trends and challenges in the field. A total of 23 relevant articles were identified and included in the review. Articles originated from 10 countries, with China having the most contributions (n = 9), followed by the United State of America (n = 4). Throughout the article, we analyzed the concept of DL in ACL tears and provided examples of how these tools can impact clinical practice and patient care. DL models for MRI detection of ACL injury reported high values of accuracy, especially helpful for less experienced clinicians. Time efficiency was also demonstrated. Overall, the deep learning models have proven to be a valid resource, although still requiring technological developments for implementation in daily practice. Full article

Pigmented rice, particularly the black and red varieties, is popular due to its better nutritional value. Anthocyanins and proanthocyanidins are two major flavonoid subcategories with broad physiological functions and therapeutic significance. However, pigment deposition is a complex process, and the molecular mechanism involved remains unknown. This review explores the metabolites responsible for the pigmentation in various rice tissues. Moreover, the current challenges, feasible strategies, and potential future directions in pigmented rice research are reported. Full article

This research aimed to evaluate the awareness of pre-service science teachers regarding green chemistry, sustainability, and their perspectives on environmental education. A total of 198 pre-service science teachers from primary and secondary school programs at a teacher education college in Israel participated by completing a 34-item questionnaire. Among these, 29 prospective chemistry and biology teachers engaged in an intervention that explored the chemistry and applications of plastics and bioplastics. Following the intervention, participants completed a post-intervention survey to assess the program’s impact. The pre- and post-survey comparison showed a significant increase in sustainability awareness (1.92 to 4.36) and green chemistry awareness (2.17 to 4.20) among pre-service teachers. Paired-sample t-tests confirmed significant improvements in green chemistry awareness, sustainability awareness, and attitudes toward environmental education. While attitude toward environmental education had the highest pre-survey score, it showed the least variation in post-intervention. All questionnaire subcategories reflected positive increases, demonstrating enhanced awareness and attitudes after the intervention. This innovative approach to integrating sustainability into science education underscores the importance of preparing future educators to address environmental challenges in their teaching. Full article

Reflection can help pre-service teachers in practical phases to process anecdotal evidence (and possible misconceptions contained therein) appropriately for their professional development. In an exploratory approach, we analyze the think-aloud protocols of eleven pre-service teachers for primary education who reflect on anecdotal evidence. First, we examine whether and to what extent they identify and address misconceptions. Then, we use the AIR model of epistemic cognition to investigate which epistemic processes occur when pre-service teachers reflect on anecdotal evidence. Finally, through a qualitative comparison of three protocols, we explore possible connections between the use of epistemic processes and the processing of misconceptions. While all participants noticed a misconception from the field of educational sciences and dealt with it to varying degrees of proficiency, the subject-specific didactic misconception was hardly addressed despite causing irritation, and the content-specific misconception remained unnoticed. We were able to differentiate epistemic processes in the subcategories of noticing, reasoning, conclusion, and alternative actions as well as application of epistemic ideals. Pre-service teachers who recognized a misconception and elaborated on it with scientific knowledge seem to use epistemic processes differently, which is more concretely related to the given lesson plan. We discuss the results with regard to possible implications for university teacher training and further research desiderata. Full article

This study focused on the experiences and perceptions of geriatric healthcare professionals. The research aimed to identify ageism, examine the influencing factors, explore the desired attitudes of geriatric healthcare professionals, and identify institutional changes required for age-integrated efforts and strategies to eliminate these barriers. Interviews with two physicians and eight nurses were conducted in South Korea from August to November 2023, with each session lasting about 1 h, and the transcripts were analyzed using the grounded theory by Strauss and Corbin. The analysis yielded 11 categories, 20 subcategories, and 120 concepts. The central phenomenon was “Ageism that is conducted implicitly and covertly”. Causal conditions affecting the development of ageism were “Difficulties related to the perceived characteristics of older adults” and “Extra burden for older patients and their families”. Contextual conditions were “Provider’s aging anxiety” and “Personal experience about older patients”, and interventional conditions were “Insufficient regular education aimed at ageism prevention” and “Insufficient staffing and resources”. The action/interaction strategies were “Not perceived as a critical issue” and “Perception that it is difficult to change”. After a thorough analysis and materializing of these concepts, the following prevention measures were proposed: “Need for regular education regarding the care of older patients (including ageism) in the nursing educational curriculum” and the “Need for sufficient staffing and resources”. According to the results of this study, medical professionals must satisfy the healthcare needs of the elderly by understanding the proper aging process and tailoring their approach to the specific characteristics of older individuals. To achieve this, organizations should provide adequate resources and personnel. Full article

Large language models, such as ChatGPT-4 and Google Bard, have demonstrated potential in healthcare. This study explores their utility in occupational medicine, a field where decisions rely on compliance with specific workplace health and safety regulations. A dataset of questions encompassing key occupational health topics derived from the Italian Legislative Decree 81/08, which governs workplace health and safety, was utilized. Responses from ChatGPT-4 with contextual information (ChatGPT-4+context) and Google Bard were evaluated for accuracy and completeness, with error categorization used to identify common issues. Subcategories of the topics of the regulations were analyzed as well. In total, 433 questions were included in our analysis. ChatGPT-4+context surpasses Bard in terms of accuracy and completeness in responses, with a lower error rate in the categories analyzed, except for the percentage of missed responses. In the subcategories analyzed, Bard is superior to ChatGPT-4+context only in the areas of the manual handling of loads and physical hazards. ChatGPT-4+context outperformed Bard in providing answers about Italian regulations on health and safety at work. This study highlights the potential and limitations of large language models as decision-support tools in occupational medicine and underscores the importance of regulatory context in enhancing their reliability. Full article

Large Language Models (LLMs), such as the GPT series, LLaMA, and BERT, possess incredible capabilities in human-like text generation and understanding across diverse domains, which have revolutionized artificial intelligence applications. However, their operational complexity necessitates a specialized framework known as LLMOps (Large Language Model Operations), which refers to the practices and tools used to manage lifecycle processes, including model fine-tuning, deployment, and LLMs monitoring. LLMOps is a subcategory of the broader concept of MLOps (Machine Learning Operations), which is the practice of automating and managing the lifecycle of ML models. LLM landscapes are currently composed of platforms (e.g., Vertex AI) to manage end-to-end deployment solutions and frameworks (e.g., LangChain) to customize LLMs integration and application development. This paper attempts to understand the key differences between LLMOps and MLOps, highlighting their unique challenges, infrastructure requirements, and methodologies. The paper explores the distinction between traditional ML workflows and those required for LLMs to emphasize security concerns, scalability, and ethical considerations. Fundamental platforms, tools, and emerging trends in LLMOps are evaluated to offer actionable information for practitioners. Finally, the paper presents future potential trends for LLMOps by focusing on its critical role in optimizing LLMs for production use in fields such as healthcare, finance, and cybersecurity. Full article

Augmented reality (AR) technologies offer innovative possibilities for creating virtual artworks that seamlessly blend digital and physical elements, thereby enhancing public spaces and engaging users with both their environment and the AR content. Key concepts for assessing user experiences in AR environments include immersion, with subcategories that further elaborate on this concept. This research aims to explore the potential of AR as a tool for presenting and evaluating artworks in public spaces, with a particular focus on user engagement and immersion in AR content. Additionally, it seeks to determine whether significant differences in immersion levels exist between specific categories of virtual sculptures. Ten sculptures were displayed as mobile AR experiences in a selected public square. To achieve these objectives, a standardized questionnaire designed to measure immersion in location-based AR applications was adapted, validated, and applied. The results provide valuable insights into user experiences with different categories of AR artworks, such as digitized versus digitally created models and figurative versus abstract representations. Full article