搜索结果：Nursing research and practice

Demographic data collection is essential in education research, as demographic data allows researchers to better describe the participant population they study and to contextualize findings. However, current research practices for neurodiversity demographics often rely on prescriptive methods (e.g., requiring participants to report official diagnoses) rather than allowing participants to self-identify. This approach can: a) not allow participants to express their intersecting identities in ways that are authentic; and b) limit trustworthiness and reliability of the data and interpretation. In addition, inconsistent dissemination and representation of demographic data across studies hinder the accessibility and usability of this work. Through a literature review of neurodivergent student experiences with learning and performing STEM, we identified widespread discrepancies in how demographic information is collected and reported. This paper explores how neurodivergent identities can be more accurately and inclusively represented in education research. We present findings of a thematic analysis on the ways neurodivergent demographic data collection is done in the literature using data

Remote patient monitoring (RPM) involves the remote collection and transmission of patient health data, serving as a notable application of data-driven healthcare. This technology facilitates clinical monitoring and decision-making, offering benefits like reduced healthcare costs and improved patient outcomes. However, RPM also introduces challenges common to data-driven healthcare, such as additional data work that can disrupt clinician's workflow. This study explores the daily practices, collaboration mechanisms, and sensemaking processes of nurses in RPM through field observations and interviews with six stakeholders. Preliminary results indicate that RPM's scale-up pushes clinicians toward asynchronous collaboration. Data sensemaking is crucial for this type of collaboration, but existing technologies often create friction rather than support. This work provides empirical insights into clinical workflow in nursing practice, especially RPM. We suggest recognizing data sensemaking as a distinct nursing practice within data work and recommend further investigation into its role in the workflow of nurses in RPM.

The development of modern nursing and consequently nursing research in Ex- Yugoslavia is about a century old. To profile the development, volume, and content of nursing research we completed a performance and spatial bibliometric analysis combined with synthetic content analysis to identify the most productive countries and institutions, most prolific source titles, country cooperation, publication production trends, the content of research and hot topics. The corpus was harvested from the Web of Science All databases and contained 1380 papers. Slovenia was the most productive country, followed by Croatia and Serbia. The synthetic content analysis demonstrated that nursing research in ex-Yugoslavian countries is growing both in scope and number of publications, notwithstanding the fact that research content differs between countries and it seems that each country is focused on their local health problems. A substantial part of the research is published in national journals in national languages however, it is noteworthy to note that some ex-Yugoslavian authors have succeeded in publishing their research in top nursing journals. The study also revealed substantial international coop

This paper presents a scientometric analysis of research output from the University of Lagos, focusing on the two decades spanning 2004 to 2023. Using bibliometric data retrieved from the Web of Science, we examine trends in publication volume, collaboration patterns, citation impact, and the most prolific authors, departments, and research domains at the university. The study reveals a consistent increase in research productivity, with the highest publication output recorded in 2023. Health Sciences, Engineering, and Social Sciences are identified as dominant fields, reflecting the university's interdisciplinary research strengths. Collaborative efforts, both locally and internationally, show a positive correlation with higher citation impact, with the United States and the United Kingdom being the leading international collaborators. Notably, open-access publications account for a significant portion of the university's research output, enhancing visibility and citation rates. The findings offer valuable insights into the university's research performance over the past two decades, providing a foundation for strategic planning and policy formulation to foster research excellence

As the aging population increases and the shortage of healthcare workers increases, the need to examine other means for caring for the aging population increases. One such means is the use of humanoid robots to care for social, emotional, and physical wellbeing of the people above 65. Understanding skilled and long term care nursing home administrators' perspectives on humanoid robots in caregiving is crucial as their insights shape the implementation of robots and their potential impact on resident well-being and quality of life. This authors surveyed two hundred and sixty nine nursing homes executives to understand their perspectives on the use of humanoid robots in their nursing home facilities. The data was coded and results revealed that the executives were keen on exploring other avenues for care such as robotics that would enhance their nursing homes abilities to care for their residents. Qualitative analysis reveals diverse perspectives on integrating humanoid robots in nursing homes. While acknowledging benefits like improved engagement and staff support, concerns persist about costs, impacts on human interaction, and doubts about robot effectiveness. This highlights compl

The healthcare sector contributes approximately 4.4% of global greenhouse gas emissions, yet research on the organizational determinants of sustainable behaviors among healthcare workers remains limited. This study examines how green transformational leadership and ethical climate influence sustainable clinical behaviors among registered nurses, with green psychological climate as a mediator and perceived organizational hypocrisy as a moderator. Data were collected from 760 nurses across 11 public and private hospitals in Jordan using a cross-sectional survey design. Structural equation modeling with bootstrapping was employed to test the hypothesized relationships. The results revealed that both green transformational leadership and ethical climate positively predicted sustainable clinical behaviors. Green psychological climate partially mediated both relationships. Perceived organizational hypocrisy significantly weakened the positive effects of green transformational leadership and ethical climate on sustainable behaviors. The model explained 35.7% of the variance in sustainable clinical behaviors. These findings highlight that fostering sustainability in healthcare requires not

Over the last years, civic technology projects have emerged around the world to advance open government and community action. Although Computer-Supported Cooperative Work (CSCW) and Human-Computer Interaction (HCI) communities have shown a growing interest in researching issues around civic technologies, yet most research still focuses on projects from the Global North. The goal of this workshop is, therefore, to advance CSCW research by raising awareness for the ongoing challenges and open questions around civic technology by bridging the gap between researchers and practitioners from different regions. The workshop will be organized around three central topics: (1) discuss how the local context and infrastructure affect the design, implementation, adoption, and maintenance of civic technology; (2) identify key elements of the configuration of trust among government, citizenry, and local organizations and how these elements change depending on the sociopolitical context where community engagement takes place; (3) discover what methods and strategies are best suited for conducting research on civic technologies in different contexts. These core topics will be covered across session

Nurses should rely on the best evidence, but tend to struggle with statistics, impeding research integration into clinical practice. Statistical significance, a key concept in classical statistics, and its primary metric, the p-value, are frequently misused. This topic has been debated in many disciplines but rarely in nursing. The aim is to present key arguments in the debate surrounding the misuse of p-values, discuss their relevance to nursing, and offer recommendations to address them. The literature indicates that the concept of probability in classical statistics is not easily understood, leading to misinterpretations of statistical significance. Much of the critique concerning p-values arises from such misunderstandings and imprecise terminology. Thus, some scholars have argued for the complete abandonment of p-values. Instead of discarding p-values, this article provides a comprehensive account of their historical context and the information they convey. This will clarify why they are widely used yet often misunderstood. The article also offers recommendations for accurate interpretation of statistical significance by incorporating other key metrics. To mitigate publication

The concurrent effect of various internal and external factors on IT Outsourcing (ITO) decision making has seldom been investigated in a single study. Furthermore, research on external factors is scarce and there is no comprehensive theory that can fully explain ITO decisions made in practice. This paper explains how key decision factors, both internal and external, influence ITO decision making in a large Australian University. We also tested the feasibility of a highly regarded descriptive model of ITO decisions as the basic foundation of an ITO decision theory. The model failed to fully explain ITO decisions in our case organisation. We draw researchers' attention to the need for more exploration of external factors as well as clarification of contingency factors that may explain inconsistencies between ITO decision theories and practice, and call for more research for 'practicable' ITO decision aids. Implications for practice are also discussed in the paper.

Nursing homes and other long term-care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in U.S. nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, Washington to other skilled nursing facilities. The full extent of staff connections between nursing homes---and the crucial role these connections serve in spreading a highly contagious respiratory infection---is currently unknown given the lack of centralized data on cross-facility nursing home employment. In this paper, we perform the first large-scale analysis of nursing home connections via shared staff using device-level geolocation data from 30 million smartphones, and find that 7 percent of smartphones appearing in a nursing home also appeared in at least one other facility---even after visitor restrictions were imposed. We construct network measures of nursing home connectedness and estimate that nursing homes have, on average, connections

The application of deep learning to nursing procedure activity understanding has the potential to greatly enhance the quality and safety of nurse-patient interactions. By utilizing the technique, we can facilitate training and education, improve quality control, and enable operational compliance monitoring. However, the development of automatic recognition systems in this field is currently hindered by the scarcity of appropriately labeled datasets. The existing video datasets pose several limitations: 1) these datasets are small-scale in size to support comprehensive investigations of nursing activity; 2) they primarily focus on single procedures, lacking expert-level annotations for various nursing procedures and action steps; and 3) they lack temporally localized annotations, which prevents the effective localization of targeted actions within longer video sequences. To mitigate these limitations, we propose NurViD, a large video dataset with expert-level annotation for nursing procedure activity understanding. NurViD consists of over 1.5k videos totaling 144 hours, making it approximately four times longer than the existing largest nursing activity datasets. Notably, it encompa

Latest advances in the field of natural language processing (NLP) enable new use cases for different domains, including the medical sector. In particular, transcription can be used to support automation in the nursing documentation process and give nurses more time to interact with the patients. However, different challenges including (a) data privacy, (b) local languages and dialects, and (c) domain-specific vocabulary need to be addressed. In this case study, we investigate the case of home care nursing documentation in Switzerland. We assessed different transcription tools and models, and conducted several experiments with OpenAI Whisper, involving different variations of German (i.e., dialects, foreign accent) and manually curated example texts by a domain expert of home care nursing. Our results indicate that even the used out-of-the-box model performs sufficiently well to be a good starting point for future research in the field.

Camera traps have long been used by wildlife researchers to monitor and study animal behavior, population dynamics, habitat use, and species diversity in a non-invasive and efficient manner. While data collection from the field has increased with new tools and capabilities, methods to develop, process, and manage the data, especially the adoption of ML/AI tools, remain challenging. These challenges include the sheer volume of data generated, the need for accurate labeling and annotation, variability in environmental conditions affecting data quality, and the integration of ML/AI tools into existing workflows that often require domain-specific customization and computational resources. This paper provides a guide to a low-resource pipeline to process camera trap data on-premise, incorporating ML/AI capabilities tailored for small research groups with limited resources and computational expertise. By focusing on practical solutions, the pipeline offers accessible approaches for data transmission, inference, and evaluation, enabling researchers to discover meaningful insights from their ever-increasing camera trap datasets.

This paper explores the application of large language models (LLMs) in nursing and elderly care, focusing on AI-driven patient monitoring and interaction. We introduce a novel Chinese nursing dataset and implement incremental pre-training (IPT) and supervised fine-tuning (SFT) techniques to enhance LLM performance in specialized tasks. Using LangChain, we develop a dynamic nursing assistant capable of real-time care and personalized interventions. Experimental results demonstrate significant improvements, paving the way for AI-driven solutions to meet the growing demands of healthcare in aging populations.

The production of knowledge has become increasingly a global endeavor. Yet, location related factors, such as local working environment and national policy designs, may continue to affect what kind of science is being pursued. Here we examine the geography of the production of creative science by country, through the lens of novelty and atypicality proposed in Uzzi et al. (2013). We quantify a country's representativeness in novel and atypical science, finding persistent differences in propensity to generate creative works, even among developed countries that are large producers in science. We further cluster countries based on how their tendency to publish novel science changes over time, identifying one group of emerging countries. Our analyses point out the recent emergence of China not only as a large producer in science but also as a leader that disproportionately produces more novel and atypical research. Discipline specific analysis indicates that China's over-production of atypical science is limited to a few disciplines, especially its most prolific ones like materials science and chemistry.

This scientometric study analyzes Avian Influenza research from 2014 to 2023 using bibliographic data from the Web of Science database. We examined publication trends, sources, authorship, collaborative networks, document types, and geographical distribution to gain insights into the global research landscape. Results reveal a steady increase in publications, with high contributions from Chinese and American institutions. Journals such as PLoS One and the Journal of Virology published the highest number of studies, indicating their influence in this field. The most prolific institutions include the Chinese Academy of Sciences and the University of Hong Kong, while the College of Veterinary Medicine at South China Agricultural University emerged as the most productive department. China and the USA lead in publication volume, though developed nations like the United Kingdom and Germany exhibit a higher rate of international collaboration. "Articles" are the most common document type, constituting 84.6% of the total, while "Reviews" account for 7.6%. This study provides a comprehensive view of global trends in Avian Influenza research, emphasizing the need for collaborative efforts ac

Based on an ethnographic action research study for a Digital Twin (DT) deployment on an automated highway maintenance project, this paper reports some of the stumbling blocks that practitioners face while deploying a DT in practice. At the outset, the scope of the case study was broadly defined in terms of digitalization, and software development and deployment, which later pivoted towards the concept of Digital Twin during the collective reflection sessions between the project participants. Through an iterative learning cycle via discussions among the various project stakeholders, the case study led to uncovering the roadblocks in practice faced by the Architecture, Engineering, and Construction (AEC) practitioners. This research finds that the practitioners are facing difficulty in: (1) Creating a shared understanding due to the lack of consensus on the Digital Twin concept, (2) Adapting and investing in Digital Twin due to inability to exhaustively evaluate and select the appropriate capabilities in a Digital Twin, and (3) Allocation of resources for Digital Twin development due to the inability to assess the impact of DT on the organizational conditions and processes.

Objective. To perform a modern bibliometric analysis of the research based on the Roy Adaptation Model, a founding nursing model proposed by Sor Callista Roy in the1970s. Method. A descriptive and longitudinal study. We used information from the two dominant scientific databases, Web Of Science and SCOPUS. We obtained 137 publications from the Core Collection of WoS, and 338 publications from SCOPUS. We conducted our analysis using the software Bibliometrix, an R-package specialized in creating bibliometric analyses from a perspective of descriptive statistics and network analysis, including co-citation, co-keyword occurrence and collaboration networks. Results. Our quantitative results show the main actors around the research based on the model and the founding literature or references on which this research was based. We analyze the main keywords and how they are linked. Furthermore, we present the most prolific authors both in number of publications and in centrality in the network of coauthors. We present the most central institutions in the global network of collaboration. Conclusions. We highlight the relevance of this theoretical model in nursing and detail its evolution. Th

Objectives: Electronic health records (EHRs) are only a first step in capturing and utilizing health-related data - the challenge is turning that data into useful information. Furthermore, EHRs are increasingly likely to include data relating to patient outcomes, functionality such as clinical decision support, and genetic information as well, and, as such, can be seen as repositories of increasingly valuable information about patients' health conditions and responses to treatment over time. Methods: We describe a case study of 423 patients treated by Centerstone within Tennessee and Indiana in which we utilized electronic health record data to generate predictive algorithms of individual patient treatment response. Multiple models were constructed using predictor variables derived from clinical, financial and geographic data. Results: For the 423 patients, 101 deteriorated, 223 improved and in 99 there was no change in clinical condition. Based on modeling of various clinical indicators at baseline, the highest accuracy in predicting individual patient response ranged from 70-72% within the models tested. In terms of individual predictors, the Centerstone Assessment of Recovery Le