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The purpose of this paper is to present and discuss pedagogical frameworks and approaches to developing, delivering, and evaluating a new interdisciplinary course within the domain of energy informatics at both Master's and PhD levels. This study is needed because many papers on sustainable energy engineering education concentrate on course content but provide very little information on the pedagogical methods employed to deliver that content. The proposed new course is called "smart energy and power systems modelling" and is aimed at discussing how mathematical optimization, in the context of computer science, can contribute to more effectively managing smart energy and power systems. Different pedagogical frameworks are discussed and adapted for the specific domain of energy informatics. An ASSURE model coupled with Bloom's taxonomy is presented for the design of the course and identification of learning objectives; self-regulated learning strategies are discussed to enhance the learning process; a novel model called GPD (Gaussian Progression of Difficulty) for lecture planning was proposed; a teaching-research nexus is discussed for the course planning and enhancement. Adopting qualitative analyses and an inductive approach, this paper offers a thorough reflection on the strengths and weaknesses of the new course, together with improvement possibilities based on fieldwork and direct experience with the students and colleagues. Opportunities and challenges of interdisciplinary teaching are presented in light of real-world experience, with a particular focus on the interaction between mathematics and computer science to study the specific application of energy and power systems. [ABSTRACT FROM AUTHOR]

Why you should read this article: • To enhance your understanding of the function of, and qualities required from, nurse leaders • To explore the features and potential benefits of the quantum leadership style • To stimulate your reflection on your own role as nurse leader or manager Healthcare is continually evolving in response to new global challenges, changes in society, limitations on resources and the growing use of technology. In light of these changes and challenges, this article discusses the benefits for nurse leaders to explore a new approach known as quantum leadership. This is a relationship-focused and value-based leadership style in which leaders understand that healthcare organisations have been in a state of flux and recognise how to respond constructively to change in the future. Quantum leadership enables nurse leaders to create and maintain synergistic team working, whereby the team works together towards the common goal of delivering optimal person-centred care. The author suggests that quantum leadership is an engaging and realistic approach to adopt, with benefits of all staff delivering healthcare services and ultimately for patients. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has found broad applicability in quantum information science in topics as diverse as experimental design, state classification, and even studies on quantum foundations. Here, we experimentally realize an approach for defining custom prior distributions that are automatically tuned using ML for Bayesian quantum state estimation methods that generally better conform to the physical properties of the underlying system than standard fixed prior distributions. Previously, researchers have looked to Bayesian quantum state tomography for advantages like uncertainty quantification, the return of reliable estimates under any measurement condition, and minimal mean-squared error. However, practical challenges related to long computation times and conceptual issues concerning how to incorporate prior knowledge most suitably can overshadow these benefits. Using both simulated and experimental measurement results, we demonstrate that ML-defined prior distributions reduce net convergence times and provide a natural way to incorporate both implicit and explicit information directly into the prior distribution. These results constitute a promising path toward practical implementations of Bayesian quantum state tomography. [ABSTRACT FROM AUTHOR]

Recently, a promising autoregressive large language model (LLM), I G i enerative I P i re-trained I T i ransformer (GPT)-3 trained with 175 billion parameters via cloud computing [[1]] has been made available to the public online (released by OpenAI on November 30, 2022; https://chat.openai.com/). Theoretically, healthcare IT can integrate LLMs into the electronic medical record (EMR) so as to use the generative AI capability to write discharge summaries [[23]], assist with data entry, and optimize patient check-in for visits (e.g., by assimilating necessary patient data prior to consultation and treatment). Understanding large language models (LLMs) LLMs are a specific application of Natural Language Processing (NLP), which is a subfield of AI that focuses on the interaction between computers and human language. [Extracted from the article]

[Display omitted] In order to assess the research status, hot issues of artificial intelligence in nursing to provide reference for scholars. This work conduct a quantitative analysis on related literature in WOS from 2011 to 2022 by mathematical and statistical methods, including publication trend, journals, author, institution, national and regional, keyword, literature co-citation. The hotspots and trends were revealed. The results showed that: (1) scholars' attention to it showed a steady increasing. (2) There were 20 journals published more than 6 papers, published 205 papers, that's 30.83 % of the total, Journal of Healthcare Engineering published the most papers, that's 8.78 %. (2) Kendrick Cato, Lisiane Pruinelli published the most papers, that's 2.95 %, which have strong cooperation. (3) Harvard Medical School, Columbia University and University of Minnesota published 114 papers, that's 8.21 % of the total, were the core unit, Harvard Med Sch, Columbia Univ and Univ Penn had strong cooperation. (4) The United States, China, Japan, Australia and United Kingdom are the top five countries for publishing papers, that's 80.3 % of the total. The cooperation degree of the each are 0.51, 0.36, 0.07, 0.04, and 0.06. (6) "electronic health record", "risk prediction" and "supervised machine learning" are current research hotspots. [ABSTRACT FROM AUTHOR]

As an implementation tool of data intensive scientific research methods, machine learning (ML) can effectively shorten the research and development (R&D) cycle of new materials by half or even more. ML shows great potential in the combination with other scientific research technologies, especially in the processing and classification of large amounts of material data from theoretical calculation and experimental characterization. It is very important to systematically understand the research ideas of material informatics to accelerate the exploration of new materials. Here, we provide a comprehensive introduction to the most commonly used ML modeling methods in material informatics with classic cases. Then, we review the latest progresses of prediction models, which focus on new processing–structure–properties–performance (PSPP) relationships in some popular material systems, such as perovskites, catalysts, alloys, two‐dimensional materials, and polymers. In addition, we summarize the recent pioneering researches in innovation of material research technology, such as inverse design, ML interatomic potentials, and microtopography characterization assistance, as new research directions of material informatics. Finally, we comprehensively provide the most significant challenges and outlooks related to the future innovation and development in the field of material informatics. This review provides a critical and concise appraisal for the applications of material informatics, and a systematic and coherent guidance for material scientists to choose modeling methods based on required materials and technologies. [ABSTRACT FROM AUTHOR]

In 2018, PLOS Biology announced CellProfiler 3.0, which has become one of the most used pieces of image analysis software in biology. The rapid adoption of this software speaks to the importance of user experience to disseminate new methods of bioimage informatics. In 2018, PLOS Biology announced CellProfiler 3.0, which has since become one of the most used pieces of image analysis software in biology. This Perspective looks at the past 20 years of bioimage informatics and highlights the importance of software usability for diffusing new methods within the field. [ABSTRACT FROM AUTHOR]

In modern drug discovery, the combination of chemoinformatics and quantitative structure–activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure–activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences. [ABSTRACT FROM AUTHOR]

Nursing is the largest workforce in health care with nurses increasingly required to work with digital health technologies. However, despite the adoption of nursing informatics in Australia in the mid-1980s, nursing graduates are not being adequately equipped to use these technologies in a way that benefits the profession and improves patient care. Using a scoping review approach, this paper presents an analysis of contemporary published literature and describes the barriers to faculty engagement with digital health technologies in undergraduate nursing education. Thirty five articles were included and identified faculty lack of understanding of nursing informatics and resistance to technologies, limited infrastructure and expenditure, and limited educational resources and best practice recommendations as significant barriers to the integration of nursing informatics into undergraduate nursing curricula. Recommendations for faculty development will be explored. [ABSTRACT FROM AUTHOR]

Clinical diagnostic laboratories produce one product—information—and for this to be valuable, the information must be clinically relevant, accurate, and timely. Although diagnostic information can clearly improve patient outcomes and decrease healthcare costs, technological challenges and laboratory workflow practices affect the timeliness and clinical value of diagnostics. This article will examine how prioritizing laboratory practices in a patient-oriented approach can be used to optimize technology advances for improved patient care. [ABSTRACT FROM AUTHOR]

Objetive: to validate the Self-assessment Nursing Informatics Competence Scale (SANICS-MX) in mexican nurses. Method: descriptive prospective and multistage design. The steps were: translation and linguistic validation, validation by judges, pilot test and reliability, final test on a representative sample, and reliability and factorial analysis. Likewise, the sample consisted of 160 nursing professionals from health institutions. The period of the study was from January 2022 to February 2023. Results: the validity index per Item (3.0) was evaluated; validity criterion (16.0%) and content validity index (8.33) and the second version of the instrument was obtained. A pilot test was carried out on 30 nursing professionals to verify the consistency, persistence and comprehension of the items, obtaining a Cronbach's Alpha of 0.83, which indicates that it is a reliable scale. A final test was carried out on 160 nursing professionals. The factorial analysis and the level of reliability of the final version of the SANICS-MX scale was carried out, in which a Cronbach's Alpha of 0.943 was determined. Conclusion: SANICS-MX is valid and reliable instrument to measure computer skills and competencies in nurses. [ABSTRACT FROM AUTHOR]

Oftentimes, in practice, the observed process changes statistical properties at an unknown point in time and the duration of a change is substantially finite, in which case one says that the change is intermittent or transient. We provide an overview of existing approaches for intermittent change detection and advocate in favor of a particular setting driven by the intermittent nature of the change. We propose a novel optimization criterion that is more appropriate for many applied areas such as the detection of threats in physical computer systems, near-Earth space informatics, epidemiology, pharmacokinetics, etc. We argue that controlling the local conditional probability of a false alarm, rather than the familiar average run length to a false alarm, and maximizing the local conditional probability of detection is a more reasonable approach versus a traditional quickest change detection approach that requires minimizing the expected delay to detection. We adopt the maximum likelihood (ML) approach with respect to the change duration and show that several commonly used detection rules (cumulative sum [CUSUM], window-limited [WL]-CUSUM, and finite moving average [FMA]) are equivalent to the ML-based stopping times. We discuss how to choose design parameters for these rules and provide a comprehensive simulation study to corroborate intuitive expectations. [ABSTRACT FROM AUTHOR]

The rapid growth of omics-based data has revolutionized biomedical research and precision medicine, allowing machine learning models to be developed for cutting-edge performance. However, despite the wealth of high-throughput data available, the performance of these models is hindered by the lack of sufficient training data, particularly in clinical research (in vivo experiments). As a result, translating this knowledge into clinical practice, such as predicting drug responses, remains a challenging task. Transfer learning is a promising tool that bridges the gap between data domains by transferring knowledge from the source to the target domain. Researchers have proposed transfer learning to predict clinical outcomes by leveraging pre-clinical data (mouse, zebrafish), highlighting its vast potential. In this work, we present a comprehensive literature review of deep transfer learning methods for health informatics and clinical decision-making, focusing on high-throughput molecular data. Previous reviews mostly covered image-based transfer learning works, while we present a more detailed analysis of transfer learning papers. Furthermore, we evaluated original studies based on different evaluation settings across cross-validations, data splits and model architectures. The result shows that those transfer learning methods have great potential; high-throughput sequencing data and state-of-the-art deep learning models lead to significant insights and conclusions. Additionally, we explored various datasets in transfer learning papers with statistics and visualization. [ABSTRACT FROM AUTHOR]

The present study aims to assess the digital literacy skills among Indian Library and Information Science Professionals of IITs and to find out its relationship with the delivery of research support services to their institutional researchers in the digital era. A simple random sampling method was used in this study and data was collected by sharing the Google form of the email Id of library employees of IITs. A total of 56 responses were received from library professionals. All the respondents indicate that they possess the ability to use plagiarism software, surf / browse the internet, and can retrieve documents from e-databases, e-documents, and websites, 52(92.8%) respondents can use search engines and different search strategies efficiently, followed by 48(85.7%) respondents possess digitization skill, can work on digital repository management software and able to provide remote access service to their users. The findings further show that the skills to use reference management systems, web mining, and ontology & taxonomy are not possessed by all the respondents. The study recommends developing the expertise of library and information science professionals to improve the delivery of RSSs effectively and efficiently revising the LIS curriculum according to emerging technologies in the library and recommends library schools to give more emphasis on practical classes than theoretical ones. [ABSTRACT FROM AUTHOR]

Purpose: To analyze the AI research in the field of nursing, to explore the current situation, hot topics, and prospects of AI research in the field of nursing, and to provide a reference for researchers to carry out related studies. Methods: We used the VOSviewer 1.6.17, SciMAT, and CiteSpace 5.8.R3 to generate visual cooperation network maps for the country, organizations, authors, citations, and keywords and perform burst detection, theme evolution, and so forth. Findings A total of 9318 articles were obtained from the Web of Science Core Collection database. Four hundred and thirty‐one AI research related to the field of nursing was published by 855 institutions from 54 countries. CIN‐Computers Informatics Nursing was the top productive journal. The United States was the dominant country. The transnational cooperation between authors from developed countries was closer than that between authors from developing countries. The main hot topics included nurse rostering, nursing diagnosis, nursing decision support, disease risk factor prediction, nursing big data management, expert system, support vector machine, decision tree, deep learning, natural language processing, and nursing education. Machine learning represented one of the cutting‐edge and most applicable branches of artificial intelligence in the field of nursing, and deep learning was the hottest technology among many machine learning methods in recent years. One of the most cited papers was published by Burke in 2004 and cited 500 times, which critically evaluated AI methods to deal with nurse scheduling problems. Conclusions: Although AI has been paid more and more attention to the field of nursing, there is still a lack of high‐yielding authors who have been engaged in this field for a long time. Most of the high contribution authors and institutions came from developed countries; therefore, more transnational and multi‐disciplinary cooperation is needed to promote the development of AI in the nursing field. This bibliometric analysis not only provided a comprehensive overview to help researchers to understand the important articles, journals, potential collaborators, and institutions in this field but also analyzed the history, hot spots, and future trends of the research topic to provide inspiration for researchers to choose research directions. [ABSTRACT FROM AUTHOR]

Chemoinformatics involves integrating the principles of physical chemistry with computer-based and information science methodologies, commonly referred to as "in silico techniques", in order to address a wide range of descriptive and prescriptive chemistry issues, including applications to biology, drug discovery, and related molecular areas. On the other hand, the incorporation of machine learning has been considered of high importance in the field of drug design, enabling the extraction of chemical data from enormous compound databases to develop drugs endowed with significant biological features. The present review discusses the field of cheminformatics and proposes the use of virtual chemical libraries in virtual screening methods to increase the probability of discovering novel hit chemicals. The virtual libraries address the need to increase the quality of the compounds as well as discover promising ones. On the other hand, various applications of bioinformatics in disease classification, diagnosis, and identification of multidrug-resistant organisms were discussed. The use of ensemble models and brute-force feature selection methodology has resulted in high accuracy rates for heart disease and COVID-19 diagnosis, along with the role of special formulations for targeting meningitis and Alzheimer's disease. Additionally, the correlation between genomic variations and disease states such as obesity and chronic progressive external ophthalmoplegia, the investigation of the antibacterial activity of pyrazole and benzimidazole-based compounds against resistant microorganisms, and its applications in chemoinformatics for the prediction of drug properties and toxicity—all the previously mentioned—were presented in the current review. [ABSTRACT FROM AUTHOR]

This paper is an attempt to explore the scope and origin of the new domain 'Digital Humanities (DH)' in the context of library and information science. Besides, author also mentioned other two modes of origin of the subject DH. It also stated the role of librarians for the development of DH. It was basically theoretical research where author used desk research technique through analysing published literature on the domain. The main gap in literature review which author identified was 'mode of formation of the subject DH from LIS point of view'. Author here tried to match between the librarians' acquired knowledge base with need of DH scholars and pointed out the areas where LIS professionals can contribute toward developing DH. Finally, author theoretically developed a model to explain the origin of the subject DH from in the context of LIS domain and also presented other two existing concepts in a very simple, graphical ways. [ABSTRACT FROM AUTHOR]

The article addresses role of the nurse informatics leader in driving value. Topics discussed include challenges informatics leaders face, examples of common informatics outcome and value-driven informatics outcome, and standard tools for demonstrating value. It also mentions one of the most impactful competencies for informatics leaders.

Maintaining and enhancing aviation safety at all times is an essential factor at various airports around the world. It is not enough to keep it at that level, as we also see from history that the volume of air traffic doubles almost every 15 years. The number of aircraft in traffic is constantly increasing, and airport infrastructure needs to be developed. Consequently, the challenges for aviation are also greater. Electronic Terrain- and Obstacle Data Collection (eTOD) at airports and their surroundings are an essential procedure for aviation safety that aims to organize databases of potentially dangerous flight obstacles. The process is based on passive optical aerial remote sensing from a fixed-wing aircraft. With the help of point cloud and geoinformatics software created during the processing of in-flight recordings and additional data, strictly defined terrain- and obstacle data can be organized into databases, the format of which is regulated by international standards and documents, according to which the obstacle database can be used internationally. These data sets are needed not only because they are required by law, but also because they form the basis for the design of various aircraft procedures and make a major contribution to safe aviation. [ABSTRACT FROM AUTHOR]

We will analyze the bibliometric metrics of the global scientific production in thanatology for nursing care. Methods: A crosssectional and retrospective study with a bibliometric approach evaluated publications indexed in Scopus from 2017 to 2022. MESH terms were selected, and together with the Boolean operators AND OR a search strategy was developed and applied on December 20, 2022. We also analyzed the metadata of the publications included in the study using Elsevier’s Scival program. Results: Scientific production has increased from 29 in 2017 to 48 in 2022. Most of the articles analyzed had national cooperation (45.2%) and single authorship was presented in a lower proportion (9.6%). The United States (55) is the country with the most publications; in addition, South Korea had the lowest production (10) and lowest weighted impact (FWCI: 0.35). The articles are mainly published in first-quartile journals, such as the Journal of Pain and Symptom Management. The University of Toronto has 71% more citations than expected. Lester David and Dadfar Mahboubeh lead the subject with four published articles each. Conclusion: Thanatology in the field of nursing is a topic that has increased in recent years, and its main means of dissemination are the scientific journals of the Q1 and Q2 quartile. The leading country in this area was the United States, while Brazil was the only Latin American country with institutions among the most productive. [ABSTRACT FROM AUTHOR]

Minerals are information-rich materials that offer researchers a glimpse into the evolution of planetary bodies. Thus, it is important to extract, analyze, and interpret this abundance of information to improve our understanding of the planetary bodies in our solar system and the role our planet's geosphere played in the origin and evolution of life. Over the past several decades, data-driven efforts in mineralogy have seen a gradual increase. The development and application of data science and analytics methods to mineralogy, while extremely promising, has also been somewhat ad hoc in nature. To systematize and synthesize the direction of these efforts, we introduce the concept of "Mineral Informatics," which is the next frontier for researchers working with mineral data. In this paper, we present our vision for Mineral Informatics and the X-Informatics underpinnings that led to its conception, as well as the needs, challenges, opportunities, and future directions of the field. The intention of this paper is not to create a new specific field or a sub-field as a separate silo, but to document the needs of researchers studying minerals in various contexts and fields of study, to demonstrate how the systemization and enhanced access to mineralogical data will increase cross- and interdisciplinary studies, and how data science and informatics methods are a key next step in integrative mineralogical studies. [ABSTRACT FROM AUTHOR]

Differential privacy provides a strong form of privacy and allows preserving most of the original characteristics of the dataset. Utilizing these benefits requires one to design specific differentially private data analysis algorithms. In this work, we present three tree-based algorithms for mining redescriptions while preserving differential privacy. Redescription mining is an exploratory data analysis method for finding connections between two views over the same entities, such as phenotypes and genotypes of medical patients, for example. It has applications in many fields, including some, like health care informatics, where privacy-preserving access to data is desired. Our algorithms are the first tree-based differentially private redescription mining algorithms, and we show via experiments that, despite the inherent noise in differential privacy, it can return trustworthy results even in smaller datasets where noise typically has a stronger effect. [ABSTRACT FROM AUTHOR]

A better understanding of differences in the nature of human computer interaction (HCI) relating to GIS software can help with learning spatial concepts and spatial thinking skills. This motivates our study, which aims to create a standard measure of interaction with GIS software that can be used individually or along with existing measurement instruments for spatial thinking to generalize results across studies for evaluating GIS interaction and usability. The study developed a composite taxonomy identifying types of user interactions and strategies with GIS in relation to different spatial concepts and processes of reasoning. The taxonomy was used as a framework to create tasks which users can complete with GIS software as a measure interaction with GIS. Study data were collected from 35 students enrolled in an online undergraduate course covering fundamentals of geographic information science and technology. Following three course assignments using GIS software to demonstrate a range of spatial concepts for problem solving, students completed various tasks constituting the standard measure of GIS interaction. The results indicate that the standard measure succeeds in measuring how individuals interact with GIS and strategize to complete different spatial tasks. The standard measure of GIS interaction will be useful for the development of usability guidelines for GIS and further contributes to research in the acquisition and application of spatial concepts and reasoning in learning environments. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to introduce Japanese informatics education from elementary school through high school. To this end, we discussed what competencies are expected to be developed by programming education, the Japanese curriculum standard for informatics subjects, and what should be considered in future programming education. To concretize the discussion, we examined previous research about problem-solving skills, especially in Computational Thinking, the characteristics of the high school informatics I text, and standardized tests of informatics which are deeply related to the Japanese curriculum standard. [ABSTRACT FROM AUTHOR]

The United Kingdom is recognised worldwide as a leader in genomics. The use of genomic technologies in the National Health Service (NHS) is expected to deliver faster and more accurate diagnoses, supporting personalized treatments to improve patient outcomes. The ambition of embedding genomic medicine in the diagnostic pathway requires involvement of the front-line clinical workforce, known as 'mainstreaming'. Nurses and midwives are the largest professionally qualified workforce in the National Health Service thus, it is anticipated that they will play key roles in mainstreaming. This study investigated the level of competence/confidence of practicing nurses and midwives to support mainstreaming and their perception of the importance of genomics in delivery of patient care. A literature review of genetics/genomics competency frameworks, semi structured interviews of lead nurses and stakeholders were conducted to identify relevant competencies needed for mainstreaming. These were then used to survey four cohorts of nurses (n = 153) across England in four consecutive years (2019-22). The confidence level of these professionals in all aspects of genomics was 2.07 ± 0.47 measured on a 5-point Likert scale (1"Low confidence"; 5 "High confidence"). Intriguingly, these professionals all appreciated the importance of genomics for their patient care (4.01 ± 0.06). Whilst the importance scores increased, the confidence scores declined at the time when major genomic transformation took place in the NHS (e.g.: launch of the Genomic Medicine Service, the National Genomic Test Directory). To bridge this gap, relevant genomic education can play key roles. However, nurses and midwives were found to be grossly underrepresented in formal genomic education courses offered by Health Education England Genomics Education Programme since 2014. This may result from the lack of direct applicability of the currently offered courses for their practice and role. Thematic analysis revealed that nurses and midwives wish to support their patients by providing more information on their condition, inheritance, and treatment options in combination with the use of relevant genetic counselling skills. This study identified easy to follow competencies for embedding genomics into routine clinical care. We propose a training programme that addresses the gap that nurses and midwives currently have, to enable them to harness genomic opportunities for patients and services. [ABSTRACT FROM AUTHOR]

Background: Graduate education is the main approach to training high-level innovative elites. With the expansion of the scale of graduate education in China, it has gradually emerged that the lack of innovation ability of graduate students is recognized as the primary problem in graduate education. How to comprehensively improve the quality of postgraduate teaching has become the core task of educational reform and development. However, data about the current cultivation and development of the innovative ability of graduate students in China is limited. Methods: A questionnaire survey was conducted among medical postgraduate students. Descriptive statistics and multiple regression analysis methods were used to analyze the data to describe the current innovation ability in advanced medical education and potential influence factors. Results: A total of 1241 medical students were surveyed, according to the results of questionnaire data analysis. The proportion of subjects who participated in the College Student's Entrepreneurship and Innovation program or any other scientific research programs are fairly high which are 46.82% and 29.20% respectively. Most of the participants are observed with high levels of self-motivation and active learning and have good performance in creative thinking. However, only a small number of participants (16.6%) reported academic achievements such as publications. Most of the students are satisfied with the current scientific research environment and think that the current postgraduate training system is qualified for the cultivation of innovation ability, and expects the inclusion of course specialized in systemic medicine and medical informatics in the curricula. Multiple logistic regression results showed that among the factors studied, gender, medical specialties, and types of master's degrees are associated with cognition & skills, academic performance, and creativity. Conclusions: It will be important to incorporate more techniques for creating and improving creativity in the curricula of the current postgraduate education, especially for courses such as systemic medicine and informatics. Guidance in earlier school life can stimulate creativity and an early introduction to scientific research work will facilitate innovative thinking and behavior. Scientific research programs such as the National Innovation and Entrepreneurship Training for the universities of PRC have been widely implemented in the undergraduate education system throughout the country. However, the training effectiveness of the current scientific research programs is worth improving. [ABSTRACT FROM AUTHOR]

Objective: Intensive Care Units are one of the areas with the lowest digitization rate. This study aims to measure the effect of digitizing medical records kept in paper forms in ICUs on time-saving and paper consumption. In our study, care forms in ICUs were transferred to digital media. In our research, care forms in ICUs were transferred to digital media. Methods: The time required to fill out the nursing care forms on paper and digital media was measured, the change in paper and printer costs was determined, and the results were compared. Two volunteer nurses working in the ICU of a university hospital in Istanbul measured the time it took to fill out the forms of patients on paper. Then, a future projection was made using digital form data of 5,420 care days of 428 patients hospitalized between October 2017 and September 2018. Only anonymous data of patients hospitalized in the general ICU were used, and other untempered were not included in the study. Results: When the forms were filled in digitally by the nurses, one nurse per patient per day saved 56.82 min (3.95% per day). Discussion: Health care services are provided in hospitals in Turkey with 28,353 adult intensive care beds and an occupancy rate of 68%. Based on the occupancy rate of 68%, the number of full beds is 19,280. When 56.82 min are saved per bed from the forms filled by the nurses, 760.71 care days are dedicated. Considering the salary of 1,428.67 US dollars per nurse, the savings to be achieved are estimated to be 13,040,804.8 US dollars per year. [ABSTRACT FROM AUTHOR]

Background: The prevalence of violence in acute psychiatric wards is a critical concern. According to a meta-analysis investigating violence in psychiatric inpatient units, researchers estimated that approximately 17% of inpatients commit one or more acts of violence during their stay. Inpatient violence negatively affects health-care providers and patients and may contribute to high staff turnover. Therefore, predicting which psychiatric inpatients will commit violence is of considerable clinical significance. Objective: The present study aimed to estimate the violence rate for psychiatric inpatients and establish a predictive model for violence in psychiatric inpatients. Methods: We collected the structured and unstructured data from Chinese nursing electronic medical records (EMRs) for the violence prediction. The data was obtained from the psychiatry department of a regional hospital in southern Taiwan, covering the period between January 2008 and December 2018. Several text mining and machine learning techniques were employed to analyze the data. Results: The results demonstrated that the rate of violence in psychiatric inpatients is 19.7%. The patients with violence in psychiatric wards were generally younger, had a more violent history, and were more likely to be unmarried. Furthermore, our study supported the feasibility of predicting aggressive incidents in psychiatric wards by using nursing EMRs and the proposed method can be incorporated into routine clinical practice to enable early prediction of inpatient violence. Conclusions: Our findings may provide clinicians with a new basis for judgment of the risk of violence in psychiatric wards. [ABSTRACT FROM AUTHOR]

Higher vocational colleges need to use video system technology to transform teaching methods to transform ideological and political teaching into more vivid, more attractive and infectious forms, thereby effectively assisting students to actively learn. This article studies a video system based on machine learning and has developed a type of high vocational nursing course political teaching system. The system uses the principle of machine learning to build a learning early warning monitoring algorithm, and then, the performance and accuracy of the model are evaluated by using the algorithm. This article finds that the module can extract the hidden characteristics of traffic and is not affected by the knowledge. The design of the high vocational nursing course political teaching system is equipped with multiple modules such as video on-demand, management, interaction, and recording. Compared with the group comparison, it is very efficient to find that the high vocational nursing course political teaching system in this article is very efficient. This article studies machine learning technology and applies it to the construction process of high vocational nursing curriculum political teaching video systems. [ABSTRACT FROM AUTHOR]

The aim of this study is to determine the problem-solving skills and anxiety levels of nursing students towards scientific research. The population of the study, which was planned as a cross-sectional type, consisted of students (N=567) who continued their education in a nursing faculty and took the "Research Process in Nursing" courses in the 2019-2020 academic year. Data were collected from the students who voluntarily participated in the study, without making a sample selection (n=308), reaching 54.32% of the population. In data collection, the "Problem Solving Inventory" (PSI), which evaluates the subjective perceptions of individuals about problem solving skills, and the "Research-Oriented Anxiety" (ROS) scale to measure their anxiety levels towards research were used. 82.5% of the students participating in the research are women. While 56.8% of the students stated that they could look at facts or situations with a critical eye, 48.4% evaluated their problem-solving skills at "moderate" level. The median PSI score of the students is 100.50 (min=32, max=192). The median item score of the ROS scale is 38.00 (min=12, max=60). There is a moderately significant inverse relationship between the two scales (r=-0.424, p=0.01). While students' perceptions of their problem solving skills were found to be insufficient with a score close to the general scale average, their anxiety about doing research is at a moderate level. It can be said that as students' problem solving skills increase, they worry less about doing research. Considering the fact that the quality of care in nursing practices will increase with the development of problemsolving skills of future nurse candidates during the vocational training they receive, it is recommended that training contents that will support the development of both problem-solving and research skills should be included in the nursing education curriculum in detail. [ABSTRACT FROM AUTHOR]

Aims and objectives: To measure text similarity in electronic nursing progress notes and determine factors associated with text similarity. Background: Electronic clinical notes with redundant information masks clinically relevant information, increases clinicians' cognitive burden and undermines patient safety. Design: Retrospective review of electronic medical record nursing progress notes. Methods: The study was conducted between November 2018 and February 2019 in two Australian Paediatric Intensive Care Units. De‐identified, randomly selected inpatient data were extracted from the network's database. Manually classified shift summary progress notes for each admission were sequenced from admission to discharge. Text similarity was calculated for consecutive pairs of nursing progress notes. Linear regression was undertaken to determine the association between the similarity scores and variables of interest: note word count, total number of notes and unit. The STROBE checklist was used for reporting. Results: 921 shift summary nursing progress notes were analysed. Similarity scores were widely distributed with a median of 10.37%. Only 17.2% (n = 144) of the notes have similarity scores above 20%. Of these, 5% (n = 47) were above 50% similar in comparison with a previously written note. Similarity above 50% was observed as early as the first note pair in the course of a patient's admission. A significant difference was found between the similarity scores of Unit 1 and Unit 2. Hospital unit was the only variable of interest significantly associated with similarity scores. Conclusion: Text similarity among electronic nursing progress notes in Australian Paediatric ICUs is minimal; however, notes with >50% similarity have been identified. Text analytics provides measurable data and insights about electronic clinical documentation to inform future nursing practice, research and eMR design. Relevance to clinical practice: Findings have implications for nursing practice in the way that nursing staff are educated to maintain data quality, professional accountability and effective communication in electronic documentation and to avoid unnecessary repetition of text. [ABSTRACT FROM AUTHOR]

Community pharmacists’ responsibilities are expanding to foster optimal patient-centered care, which dictates postgraduate continuous education to enhance their competency and ability to face job challenges. We employed a cross-sectional online questionnaire-based study to evaluate community pharmacists’ attitudes towards continuous education, their preferred modes of program delivery, factors they consider before joining a program, their preferred topics to learn about, and potential perceived barriers against continuous education. A total of 358 community pharmacists completed the questionnaire. The majority of them (86.9 %) were interested in continuous education. However, most pharmacists (70.1%) had never attended any continuous education activity before. The most preferred type of delivery was self-learning through the internet (44.2%). Program cost and location were the major factors considered before accepting any activity (96.9%, and 96.6%, respectively). Among diseases, infectious disease was the most interesting topic for community pharmacists (92.7%). Regarding pharmaceutical topics, they were mostly interested in learning pharmacology and pharmacotherapy (94.1%), whereas pregnant and nursing mothers was the most desired patient group to learn about (92.2%). Job constraints and lack of time were the most reported barriers (89.4% and 89.1%, respectively). Community pharmacists’ have positive attitudes towards continuous education. However, many obstacles restrain them from effective participation in it. We provided sufficient data for policy makers to consider in future planning for continuous education activities that meet the needs of today’s pharmacists to advance their practice. [ABSTRACT FROM AUTHOR]

Nowadays, data science is emerging as an integral concept of nearly every discipline. Thus, the library and information science professionals have also become interested in data science and as a result have started to contribute the scholarly literature on this topic of significance. This article discussed a study dedicated towards mapping the published literature on data science and library and information science through EBSCO library and information science source till December 2021. The results of this study show that the majority of the literature was produced in the year 2020, single authors, sole universities, United States of America (USA) country, in the Journal of Medical Internet Research and Proceedings of the Association for Information Science & Technology journals and on theme of 'data science concept'. [ABSTRACT FROM AUTHOR]

The 21 authors of this study, 19 of whom are non-native English speakers, reflect on the barriers to publishing academic journal articles in top international journals. Each author responded to the same set of questions pertaining to educational (PhD) opportunities for emerging scholars, financial conditions for researchers, and challenges in publishing their work. Limited English language skills, lack of research funding, and different research topics were identified as the most significant barriers to publish in the journals. Recruiting volunteers or using electronic translations for accepted articles for journals, reducing software and subscription to academic journals costs, expanding journal scope, and providing guidance and collaboration across borders are possible measures to reduce the negative impact of the barriers. [ABSTRACT FROM AUTHOR]

目的 分析压力性损伤智能化“评估-决策-控制”系统在脑血管病重症患者中的应用效果。 方法 对压力性损伤管理系统进行智能优化，将系统优化前（2018年1月-2019年12月）应用患者（2448例）设置为对照组，系统优化后（2020年1月-2021年12月）应用患者（3547例）设置为观察组，比较两组患者入科2 h内压力性损伤评估完成率、风险评估准确率、伤口评估准确率、伤口处理正确率、院内压力性损伤发生率的差异。 结果 对照组和观察组患者入科2 h内压力性损伤风险评估完成率分别为94.7%和99.0%（χ2=7.3，P=0.007），风险评估准确率分别为89.0%和96.3%（χ2=11.9，P=0.001），伤口评估准确率分别为74.0%和90.0%（χ2=4.3，P=0.037），伤口处理正确率分别为84.0%和96.0%（χ2=4.0，P=0.046），差异均有统计学意义。对照组院内压力性损伤发生率为0.04％（1/2448），观察组院内压力性损伤发生率为0（0/3547）。 结论 压力性损伤智能化“评估-决策-控制”系统，可以帮助护士准确评估、判断和记录压力性损伤，并做出合理的临床护理决策。 Abstract: Objective To analyze the application effect of pressure injury intelligent "assessment-decision-control" system in severe patients with cerebrovascular disease. Methods The pressure injury management system was optimized, and the severe cerebrovascular disease patients (2448 cases) before system optimization (January 2018-December 2019) were selected as the control group, and the severe cerebrovascular disease patients (3547 cases) after system optimization (January 2020-December 2021) were selected as the observation group. The rate of pressure injury assessment, accuracy rate of risk assessment, accuracy rate of wound assessment, correct rate of wound treatment, and incidence of in-hospital pressure injury within 2 hours of admission were compared between the two groups. Results The above indexes results between the two groups were as follows (control vs. observation): the rate of pressure injury assessment was 94.7% vs. 99.0% (χ2=7.3, P=0.007); the correct rate of risk assessment was 89.0% vs. 96.3% (χ2=11.9, P=0.001); the correct rate of wound assessment was 74.0% vs. 90.0% (χ2=4.3, P=0.037); the correct rate of wound treatment was 84.0% vs. 96.0% (χ2=4.0, P=0.046), with all the above differences having statistical significance (P

Abstract Background Nursing information systems embedded with standardized nursing language and clinical decision support have been increasingly introduced in health care settings. User experience is key to the adoption of health information technologies. Despite extensive research into the user experience with nursing information systems, few studies have focused on the interaction between user, technology and organizational attributes during its implementation. Guided by the human, organization and technology-fit framework, this study aimed to investigate nurses’ perceptions and experiences with transition to a new nursing information system (Care Direct) 2 years after its first introduction. Methods This is a mixed-method study using an embedded design. An online survey was launched to collect nurses’ self-reported use of the new system, perceived system effectiveness and experience of participation in system optimization. Twenty-two semi structured interviews were conducted with twenty nurses with clinical or administrative roles. The quantitative and qualitative data were merged using the Pillar Integration Process. Results The average score of system use behavior was 3.76 ± 0.79. Regarding perceived system effectiveness, the score of each dimension ranged 3.07–3.34 out of 5. Despite large variations in approaches to participating in system optimization, nurses had generally positive experiences with management and technical support. Eight main categories emerged from the integrated findings, which were further condensed into three themes: perceptions on system content, structure, and functionality; perceptions on interdisciplinary and cross-level cooperation; and embracing and accepting the change. Conclusions Effective collaboration between clinicians, administrators and technical staff is required during system promotion to enhance system usability and user experience. Clear communication of organizational missions to staff and support from top management is needed to smooth the system implementation process and achieve broader system adoption.

Objectives Academic electronic medical records (AEMRs) can be utilized for a variety of educational programs that can enhance nursing students’ nursing informatics and clinical reasoning competencies. This study aimed to identify the applicability and effectiveness of Simulation Education Incorporating AEMRs. Methods We developed simulation education scenarios incorporating AEMRs and evaluated them with 76 third- and fourth-year nursing students from five nursing schools using a mixed-methods design. We incorporated three simulation case scenarios involving preeclampsia, diabetes mellitus, and myocardial infarction into the AEMRs. After the simulation education, participants’ feedback on the usability of the AEMR system and their self-efficacy for AEMR utilization were collected via self-reported surveys. Subsequently, the simulation education incorporating AEMRs was evaluated through a focus group interview. The survey data were examined using descriptive statistics, and thematic analysis was done for the focus group interview data. Results The average mean scores for the AEMR system’s usability and participants’ self-efficacy for AEMR utilization were 5.36 of 7 and 3.96 of 5, respectively. According to the focus group interviews, the participants were satisfied with the Simulation Education Incorporating AEMRs and recognized their confidence in AEMR utilization. In addition, participants addressed challenges to Simulation Education Incorporating AEMRs, including the need for pre-education and AEMR utilization difficulties. Conclusions Nursing students were satisfied with and recognized the value of Simulation Education Incorporating AEMRs. Although the actual application of simulation education incorporating AEMRs remains challenging, further research can help develop and implement this approach for nursing students.

Objectives This study explored the current status of nursing informatics education in South Korea and developed a standardized curriculum for it. Methods Data were collected in two stages: first, an online survey conducted from December 2020 to February 2021 among 60 nursing schools to analyze the current status of nursing informatics education; and second, a two-round Delphi survey with 15 experts from March to April 2021 to determine the mean and standard deviation of the demand for each learning objective in nursing informatics education. A standardized curriculum proposal was developed based on the results of the two-round Delphi survey. Results Nursing informatics was most commonly taught in the fourth year (34%), with two credits. The proportion of elective major subjects was high in undergraduate and graduate programs (77.4% and 78.6%, respectively), while the proportion of nursing informatics majors was low (21.4%). The curriculum developed included topics such as nursing information system-related concepts, definitions and components of healthcare information systems, electronic medical records, clinical decision support systems, mobile technology and health management, medical information standards, personal information protection and ethics, understanding of big data, use of information technology in evidence-based practice, use of information in community nursing, genome information usage, artificial intelligence clinical information systems, administrative management systems, and information technology nursing education. Conclusions Nursing informatics professors should receive ongoing training to obtain recent medical information. Further review and modification of the nursing informatics curriculum should be performed to ensure that it remains up-to-date with recent developments.

Objective Falls are one of the most frequently occurring adverse events among hospitalized patients. The Morse Fall Scale, which has been widely used for fall risk assessment, has the two limitations of low specificity and difficulty in practical implementation. The aim of this study was to develop and validate an interpretable machine learning model for prediction of falls to be integrated in an electronic medical record (EMR) system. Methods This was a retrospective study involving a tertiary teaching hospital in Seoul, Korea. Based on the literature, 83 known predictors were grouped into seven categories. Interpretable fall event prediction models were developed using multiple machine learning models including gradient boosting and Shapley values. Results Overall, 191,778 cases with 272 fall events (0.1%) were included in the analysis. With the validation cohort of 2020, the area under the receiver operating curve (AUROC) of the gradient boosting model was 0.817 (95% confidence interval [CI], 0.720–0.904), better performance than random forest (AUROC, 0.801; 95% CI, 0.708–0.890), logistic regression (AUROC, 0.802; 95% CI, 0.721–0.878), artificial neural net (AUROC, 0.736; 95% CI, 0.650–0.821), and conventional Morse fall score (AUROC, 0.652; 95% CI, 0.570–0.715). The model’s interpretability was enhanced at both the population and patient levels. The algorithm was later integrated into the current EMR system. Conclusion We developed an interpretable machine learning prediction model for inpatient fall events using EMR integration formats.

Objective. To study prevalence, risk factors, and consequences of the COVID-19 pandemic related to Burnout syndrome (BOS) among Thai healthcare providers (HCPs) during the COVID-19 pandemic. Methods. We performed a cross-sectional study among HCPs, involved in caring for patients during the pandemic in two periods (1st period, May–Jun 2021, and 2nd period, Sep-Oct 2021). Data were distributed using electronic questionnaires. BOS was defined if respondents exhibited a high level of at least one domain in the Maslach Burnout Inventory criteria. The primary outcome was prevalence of BOS. Results. Altogether, 2,027 and 1,146 respondents were enrolled in the 1st and 2nd periods, respectively. Most respondents were female (73.3, 68.2%). The top three job positions were physicians (49.2, 58.9%), nurses (41.2, 30.6%), and nursing assistants (4.8, 6.5%), respectively. No difference was found in overall prevalence of Burnout syndrome during the 1st and 2nd periods (73 vs. 73.5%, p = 0.80). Using multivariate analysis, significant risk factors for Burnout syndrome in both periods were (1) living with family (odds ratio (OR) 1.3 and 1.5), (2) tertiary care hospital (OR 1.92 and 2.13), (3) nurse (OR 1.38 and 2.29), (4) nursing assistant (OR 0.92 and 4.81), (5) salary ≤40,000 THB (OR 1.53 and 1.53), (6) >20 patients per shift (OR 1.55 and 1.88), (7) >6 shifts after hours monthly (OR 1.26 and 1.49), and (8) ≤1 rest day weekly (OR 1.3 and 1.4). Conclusion. We found a high prevalence of Burnout syndrome among Thai HCPs during the pandemic. Knowing those risk factors may provide a strategy to BOS during the pandemic. [ABSTRACT FROM AUTHOR]

• Immunization pre-service training studies are done mostly in high-income countries. • Studies targeted professional medical students but largely missed nurses. • No studies described pre-service training for workforce in non-academic settings. • Applied learning increased confidence in vaccine knowledge and practices. • Immunization pre-service training methods varied across population and region. The World Health Organization's Global Strategy on Human Resources for Health: Workforce 2030 sets policy recommendations and targets for in-service and pre-service training programs to improve workforce competency. To date, comprehensive reviews on immunization training have mainly focused on in-service trainings. This systematic review aimed to synthesize current literature on pre-service immunization training, including primary immunization competencies covered, methods used, outcomes on improving competencies and behavior change for immunization service delivery, and student readiness for immunization practice, in both low-resource and high-income settings. A systematic search of seven scholarly databases identified published literature on pre-service training on immunization published between January 2001 and November 2021. It included all geographic regions and languages, study designs, and individuals preparing to enter the immunization workforce. Additional search methods included reviewing references of retrieved articles, scanning journals, and engaging pre-service training experts for unpublished reports. Search results yielded 5,611 articles; 39 articles met the inclusion criteria. Five articles were identified through other search methods. Studies took place mostly in high-income countries (35/44), targeted professional (medical, nursing, and pharmacy) students and tutors at health training institutions. Eight of the ten recommended immunization competencies were included in the curricula and methods used to deliver pre-service training varied. Teaching techniques and applied learning strategies using realistic situations increased students' knowledge, attitudes, and awareness of vaccine benefits; built confidence to administer vaccines and communicate with hesitant patients; and increased the likelihood of recommending vaccines. This review was the first step to understanding pre-service training on immunization. Further research is needed to inform pre-service training programs in low- and middle-income countries, particularly for nurses, vaccinators with low-level educational backgrounds, and other healthcare professional students. Prioritizing essential audiences, designing and delivering practical training, and evaluating results will help prepare students for the immunization challenges of tomorrow. [ABSTRACT FROM AUTHOR]

The article discusses research which examined the validity of transformational leadership in improving the quality of nursing documentation self-efficacy in government hospitals in Indonesia. Results show the distribution and frequency of transformational leadership style of head nurses, and the relationship between gender, nursing documentation certificate, transformational leadership style and self-efficacy. The importance of mentoring to improving self-efficacy is discussed.

Purpose of the study: The objective of the study was to identify the main strategic domains and their characteristics that enable the proposition of a theoretical-analytical model for implementation and analysis of Digital Transformation (DT) in organizations. Relevance/originality: Proposition of a theoretical-analytical model for implementing and analyzing Digital Transformation (DT) in organizations, based on the 'state of the art' of the topic (national and international literature from 2017 to 2021), composed of eight strategy domains (or dimensions). Methodology/approach: Systematic Literature Review, using the Scopus and Web of Science databases as a source of consultation and data collection, between 2017 and 2021, whose selection was based on reading the titles and abstracts of 296 articles, 26 of which were selected for this work. Main results: Identification of the main strategy domains inherent to DT and its characteristics - customers, data, competition, innovation, value generation, competencies, culture, and agility, which enabled the structuring and proposition of a theoretical-analytical model for implementation and analysis of (DT) in organizations. Theoretical/methodological contributions: From a theoretical-academic point of view, by extending investigative and analytical research on the topic of Digital Transformation into the interdisciplinary and multidisciplinary field of Information Science, Business Administration, and Computing, and the consequent generation of new knowledge for such fields. Social/management contributions: From the organizational point of view, by proposing a framework that is not only theoretical, but also applied, allowing the implementation and analysis of DT, and its consequent generation of value for the various types and levels of users, besides the business itself. [ABSTRACT FROM AUTHOR]

This article is a natural continuation of its first part, published in the journal “Mathematics and Informatics”, where the terminology related to game-based learning and the role of tasks in learning the learning content, as well as example tasks and methodical implementation for non-profiled training in programming for high school students through the creation of games, are presented using Python‘s turtle graph. This article follows the structure of its first part and focuses on example tasks implemented with the Python programming language, the Pygame library, and the Mu programming environment. The sample tasks are supported by methodical instructions. Guidelines are also given for their use in training. In the conclusion, the possibilities for game-based programming learning in profiled and non-profiled high schools are discussed using the Pygame Python library. [ABSTRACT FROM AUTHOR]

In this article, we will analyze the needs, interests and realization of the students of The Faculty of mathematics and computer science of Konstantin Preslavsky University of Shumen, preparing to become teachers of mathematics, informatics, computer modeling and information technologies. The group we are considering are the students of Bachelor’s and Master’s degree. The data for students who choose to graduate with thesis defense are being studied. The aim is to track the trends and the reflection of current methodological directions in the interests of students. A classification is proposed in the main areas in which students choose to work. A content analysis of the defended graduation theses in the period 2014 – 2022 was made. The realization of the students was tracked. Ideas that are to be realized in diploma thesis in the future are marked. [ABSTRACT FROM AUTHOR]