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In public libraries, especially those in rural settings, it is important that every dime of library funding is leveraged effectively into serving the community. As part of a year-long project beginning in January 2023, we are evaluating item-level cost-effectiveness for each circulating item housed at the public library in Lakeville, Indiana. Through the use of big(ish) data, some custom Python scripting, and machine learning algorithms we hope to answer: How much money is saved by library patrons through their use of the public library's physical collection? How much money is saved by the community through the operation of a public library based on the use of the circulating collection? And are there any non-obvious traits which make an item or title a more or less cost-effective circulating asset? In this column, I will describe the scripts, share initial findings, discuss challenges, and investigate next steps. [ABSTRACT FROM AUTHOR]

Purpose This study investigated the impact of an advanced analytics software solution in the operating room (OR) on tracking and evaluating controlled substance discrepancies. The authors hypothesized that the software would increase identification of these discrepancies and improve the efficiency of the preexisting manual process. Methods In this evaluation comparing data from before to after implementation of the software, data were collected using the preexisting manual process for 50 days before implementation, followed by a 25-day period for acclimation to the new software, and ending with a 49-day postimplementation review period. Data collected included the total number of medication discrepancies, time required for discrepancy review and reconciliation by an OR analyst, types of discrepancies, and number of discrepancies leading to provider audits. Results Before implementation of the analytics software, there were 7,635 OR cases with a total of 674 charting discrepancies (8.83 discrepancies per 100 total OR cases) discovered across 439 OR cases. After implementation, there were 7,454 OR cases with a total of 930 charting discrepancies (12.48 discrepancies per 100 total OR cases; P < 0.0001) discovered across 680 OR cases. While discrepancies increased by 38%, the median review time for the OR analyst per case decreased (P < 0.0001) and the percentage of incidents resolved by the OR analyst increased by 14% while the number of cases requiring additional documentation by the provider decreased by 10%. Conclusion Implementation of advanced analytics software in the OR significantly increased the number of controlled substance charting discrepancies identified compared to the preimplementation review process while increasing the efficiency of the OR analyst. [ABSTRACT FROM AUTHOR]

Background: Learning analytics (LA) research often aggregates learning process data to extract measurements indicating constructs of interest. However, the warranty that such aggregation will produce reliable measurements has not been explicitly examined. The reliability evidence of aggregate measurements has rarely been reported, leaving an implicit assumption that such measurements are free of errors. Objectives: This study addresses these gaps by investigating the psychometric pros and cons of aggregate measurements. Methods: This study proposes a framework for aggregating process data, which includes the conditions where aggregation is appropriate, and a guideline for selecting the proper reliability evidence and the computing procedure. We support and demonstrate the framework by analysing undergraduates' academic procrastination and programming proficiency in an introductory computer science course. Results and Conclusion: Aggregation over a period is acceptable and may improve measurement reliability only if the construct of interest is stable during the period. Otherwise, aggregation may mask meaningful changes in behaviours and should be avoided. While selecting the type of reliability evidence, a critical question is whether process data can be regarded as repeated measurements. Another question is whether the lengths of processes are unequal and individual events are unreliable. If the answer to the second question is no, segmenting each process into a fixed number of bins assists in computing the reliability coefficient. Major Takeaways: The proposed framework can be a general guideline for aggregating process data in LA research. Researchers should check and report the reliability evidence for aggregate measurements before the ensuing interpretation. Lay Description: What is currently known about this topic: Aggregating learning process data is common in learning analytics.The psychometric pros and cons of aggregating process data are rarely examined. What this paper adds: If the construct of interest is stable during a period, aggregation over this period is desirable.Aggregation over a long period masks meaningful changes in learning behaviours.A guideline for choosing proper reliability coefficients for aggregate measurements is proposed.Methods for computing reliability estimates when processes vary in length are provided. Implications for practice: Report reliability evidence for aggregate measurements for the sake of psychometric rigour.A short period causes unreliable action‐related indicators in learning analytics dashboards.A long period causes inaccurate indicators of learners' current state. [ABSTRACT FROM AUTHOR]

Background: With the development of computational literacy, there has been a surge in both research and practice application of text‐based and block‐based modalities within the field of computer programming education. Despite this trend, little work has actually examined how learners engaging in programming process when utilizing these two major programming modalities, especially in the context of secondary education settings. Objectives: To further compare programming effects between and within text‐based and block‐based modalities, this research conducted a quasi‐experimental research in China's secondary school. Methods: An online programming platform, Code4all, was developed to allow learners to program in text‐based and block‐based modalities. This research collected multimodal data sources, including programming platform data, process data, and performance data. This research further utilized multiple learning analytics approaches (i.e., clustering analysis, click stream analysis, lag‐sequential analysis and statistics) to compare learners' programming features, behavioural patterns and knowledge gains under two modalities. Results and Conclusions: The results indicated that learners in text‐based modality tended to write longer lines of code, encountered more syntactical errors, and took longer to attempt debugging. In contrast, learners in block‐based modality spent more time operating blocks and attempt debugging, achieving better programming knowledge performances compared to their counterparts. Further analysis of five clusters from the two modalities revealed discrepancies in programming behavioural patterns. Implications: Three major pedagogical implications were proposed based on empirical research results. Furthermore, this research contributed to the learning analytics literature by integrating process‐oriented and summative analysis to reveal learners' programming learning quality. Lay Description: What is currently known about the subject matter: Programming has the potential to improve learners' higher‐order thinking skills.Block‐based and text‐based modalities are two major instructional methods.There has been a growing interest to understand how learning occurs in two modes.Most previous work has evaluated two modalities based on learners' knowledge, skills, and attitudes. What this paper adds: Code4all allows learners to programming in text‐based and block‐based modalities.Quasi‐experimental research was conducted to examine block‐based and text‐based programming modalities.Multimodal learning analytics were used to compare programming under two modalities.Learners' programming features, behaviouralbehavioral patterns, and knowledge gains were identified under two modalities. The implications of study findings for practitioners: Instructors should integrate text‐based and block‐based modalities into programming courses.Process‐oriented assessment should be integrated with summative assessment. Adaptive, timely scaffoldings should be provided with the external support (this should be marked like above two points). [ABSTRACT FROM AUTHOR]

Background: Deep discussions play an important role in students' online learning. However, researchers have largely focused on engaging students in deep discussions in online asynchronous forums. Few studies have investigated how to promote deep discussion via mobile instant messaging (MIM). Objectives: In this study, we applied learning analytical tools (i.e., KBdeX and word clouds) to enhance students' shared epistemic agency and thereby support their deep discussions in MIM. Methods: Forty Chinese college students participated in this study and reflected on their MIM engagement by participating in the learning analytics (LA)‐augmented meta‐discourse sessions. The study used multiple data analysis methods, including content analysis, statistical analysis, epistemic network analysis and lag sequential analysis. Results: We found that LA engaged students in deep discussions and shared epistemic agency‐related discourse, such as creating shared understanding, creating knowledge objects, and projective and regulative processes. In particular, word clouds engaged students in more complete shared epistemic agency discourse trajectory which started from creating awareness of unknowns, then progressed to setting projective plans and sharing information, and ultimately, creating shared understanding. Moreover, our analysis indicated that epistemic agency discourse moves of creating shared understanding led students to a high level of deep discussion. Implications: This study contributes to research by extending the 'comparison paradigm', which focuses on comparing (a)synchronous forums with MIM, to a 'design paradigm', which mobilises design features from (a)synchronous forums to MIM and using learning analytical tools to engage students in deep online discussions by promoting their epistemic agency. Lay Description: What is known in this subject matter: Supporting students' online deep discussions is important.Prior studies have shown multiple technological and pedagogical strategies to support deep discussions in (a)synchronous discussion forums, but few studies in mobile instant messaging (MIM).MIM is a quasi‐synchronous tool. It is difficult to transform the traditional external scaffolds (i.e., scripts or teachers' facilitation) which promote deep discussions from (a)synchronous forums to MIM. What this paper adds to this: We constructed a design framework which used learning analytical tools‐augmented meta‐discourse to engage students in shared epistemic agency discourse moves to promote their deep discussion in MIM.We conducted multiple types of analyses (content analysis, statistical analysis, epistemic network analysis and lag sequential analysis) on students' WeChat message (a major MIM in China).Results indicated both learning analytical tools helped engage students in shared epistemic agency discourse moves, such as creating shared understanding, creating knowledge objects, projective and regulative, thus, to promote students' deep discussions. Word cloud especially engaged students in more complete shared epistemic agency discourse trajectory. Moreover, analysis results indicated that shared epistemic agency discourse moves of creating shared understanding would lead students to a high level of deep discussions. Implications of study findings for practitioners: Our study provides a feasible way to educational practitioners who want to promote students' deep discussions in their online learning course (both fully online and flipped classroom mode).Knowledge Building Discourse Explorer (KBDeX presents the original analysis results of students' written messages in discussion forums as socio‐cognitive networks.) could be implemented in the classroom. The objective is to encourage students to understand the importance of connecting with peers' ideas in a one‐session implementation period.If time allows, teachers could use word cloud and meta‐discourse to engage students in socio‐metacognitive conversations in class to promote their shared epistemic agency discourse in online discussions. [ABSTRACT FROM AUTHOR]

Background: Undergraduates' collective epistemic agency is critical for their productive collaborative inquiry and knowledge building (KB). However, fostering undergraduates' collective epistemic agency is challenging. Studies have demonstrated the potential of computer‐supported collaborative inquiry approaches, such as KB—the focus of this study, and reflective assessment to foster collective epistemic agency. Objectives: This study used a quasi‐experimental design to examine the impact of reflective assessment on undergraduates' collective epistemic agency and the mechanisms of this impact. Methods: An experimental class, comprising 40 undergraduates, engaged in KB inquiry enhanced by analytics‐assisted reflective assessment, while a comparison class, comprising 41 undergraduates, engaged in portfolio‐supported reflective assessment in KB inquiry. Classroom observations, audio recordings of group discussions and reflections, and student artefacts were collected in both conditions. Results and Conclusions: The experimental class demonstrated significantly higher levels of collective epistemic agency than the comparison class, as indicated by their collective knowledge advancement and epistemic and metacognitive actions. Qualitative thematic analysis revealed that reflective assessment enhanced the undergraduates' collective epistemic agency through their collective monitoring and regulation of inquiry and ideas, negotiation and synthesis of the community's ideas, conceptualization of collective theories and identification of collective inquiry trajectories with an internal reflective structure. Major Takeaways: Higher‐level metacognitive actions are critical for productive collaborative inquiry and higher‐level epistemic agency that does not often occur naturally and should be promoted by appropriate scaffolding strategies. Students need data from learning analytics and accompanying prompt sheets to help them develop the internal metacognitive structure for agency and learning. Lay Description: What is currently known about this topic?: Undergraduates' collective epistemic agency (CEA) is critical for productive KB.Analytics‐assisted reflective assessment (AARA) are promising in fostering CEA. What does this paper add?: AARA has positive influences on students' CEAAARA helps students develop CEA through the following mechanisms.AARA supports students' collective regulation and negotiation of collective ideas.AARA enhances conceptualization and deepening of collective trajectories with an internal reflective structure. Implications for practice/or policy: Higher‐level metacognitive actions are critical for collaborative inquiry and CEAAnalytics‐assisted RA can enhance students' higher‐level metacognitive actions.Students should be provided with learning data and appropriate scaffolding strategies.These data and strategies can develop students' internal metacognitive structure crucial for agency and learning. [ABSTRACT FROM AUTHOR]

Background: CARA set out to develop a data-visualisation platform to facilitate general practitioners to develop a deeper understanding of their patient population, disease management and prescribing through dashboards. To support the continued use and sustainability of the CARA dashboards, dashboard performance and user engagement have to be optimised. User research places people at the centre of the design process and aims to evaluate the needs, behaviours and attitudes of users to inform the design, development and impact of a product. Objective: To explore how different initial key messages impact the level of behavioural engagement with a CARA dashboard. Methods: Participating general practices can upload their practice data for analysis and visualisation in CARA dashboards. Practices will be randomised to one of three different initial landing pages: the full dashboard or one of two key messages: a between comparison (their practice prescribing with the average of all other practices) or within comparison (with practice data of the same month the previous year) with subsequent continuation to the full dashboard. Analysis will determine which of the three landing pages encourages user interaction, as measured by the number of 'clicks', 'viewings' and 'sessions'. Dashboard usage data will be collected through Google analytics. Discussion: This study will provide evidence of behavioural engagement and its metrics during the implementation of the CARA dashboards to optimise and sustain interaction. Trial registration: ISRCTN32783644 (Registration date: 02/01/2024). [ABSTRACT FROM AUTHOR]

A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPARδ) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various structural descriptors and docking calculations and provides predictions of the biological activity of PPARδ agonists, following the criteria of the Organization for Economic Co-operation and Development (OECD) for the development and validation of quantitative structure–activity relationship (QSAR) models. Specifically focused on small molecules, the model facilitates the identification of highly potent and selective PPARδ agonists and offers a read-across concept by providing the chemical neighbours of the compound under study. The model development process was conducted on Isalos Analytics Software (v. 0.1.17) which provides an intuitive environment for machine-learning applications. The final model was released as a user-friendly web tool and can be accessed through the Enalos Cloud platform's graphical user interface (GUI). [ABSTRACT FROM AUTHOR]

Dental sealants are effective for the prevention of caries in children at elevated risk levels, and increasing the proportion of children and adolescents who have dental sealants on 1 or more molars is a Healthy People 2030 objective. Electronic health record (EHR)-based clinical decision support systems (CDSSs) have the ability to improve patient care. A dental quality measure related to dental sealant placement for children at elevated risk of caries was targeted for improvement using a CDSS. A validated dental quality measure was adapted to assess a patient's need for dental sealant placement. A CDSS was implemented to advise care team members whether a child was at elevated risk of developing caries and had sealant-eligible first or second molars. Data on dental sealant placement at examination visits during a 5-year period were analyzed, including 32 months before CDSS implementation and 28 months after CDSS implementation. From January 1, 2018, through December 31, 2022, the authors assessed 59,047 examination visits for children at elevated risk of developing caries and with sealant-eligible teeth. With the implementation of a CDSS and training to support the clinical care team members in September 2020, the appropriate placement of dental sealants at examination visits increased from 27% through 60% (P <.00001). Integration of a CDSS into the EHR as part of a quality improvement program was effective in increasing the delivery of sealants in eligible first and second molars of children aged 5 through 15 years and considered at high risk of developing caries. An EHR–based CDSS can be implemented to improve standardization and provide timely and appropriate patient care in dental practices. [ABSTRACT FROM AUTHOR]

Data users (researchers, scientists) in healthcare institutions need access to integrated healthcare data to conduct timely analysis of diseases to serve the right population at the right time. However, preserving patient privacy and timely access to quality healthcare data is a critical challenge. Current healthcare data governance systems are largely manual. Besides, processing process data requests is extremely slow, often taking months. To address this gap, we designed an honest-broker-based healthcare application to support data users in accessing healthcare data securely and to design a comprehendible process of data governance for data users. This study applied two iterations of a user experience (UX) evaluation of an honest broker prototype. Results show that participants found the new system promising for their research prospects. Implications suggest that technological knowledge should not be a requirement for using healthcare applications to promote broader adoption in the community. This study highlights the necessity of a process to balance the control of access to sensitive data between data providers and users as well as to educate data users on data privacy. Iterative UX studies can be a fruitful approach in gradually uncovering problems and improving the design of complex systems. [ABSTRACT FROM AUTHOR]

Purpose: Hip and knee arthroplasty aims to reduce joint pain and increase functional mobility in patients with osteoarthritis; however, the degree to which arthroplasty is associated with higher physical activity is unclear. The current study sought to assess the association of hip and knee arthroplasty with objectively measured physical activity. Methods: This cross-sectional study analyzed wrist-worn accelerometer data collected in 2013-2016 from UK Biobank participants (aged 43-78 yr). Multivariable linear regression was performed to assess step count, cadence, overall acceleration, and activity behaviors between nonarthritic controls, end-stage arthritic, and postoperative cohorts, controlling for demographic and behavioral confounders. From a cohort of 94,707 participants with valid accelerometer wear time and complete self-reported data, electronic health records were used to identify 3506 participants having undergone primary or revision hip or knee arthroplasty and 68,389 nonarthritic controls. Results: End-stage hip or knee arthritis was associated with taking 1129 fewer steps per day (95% confidence interval (CI), 811-1447; P < 0.001) and having 5.8 fewer minutes per day (95% CI, 3.0-8.7; P < 0.001) of moderate-to-vigorous activity compared with nonarthritic controls. Unilateral primary hip and knee arthroplasties were associated with 877 (95% CI, 284-1471; P = 0.004) and 893 (95% CI, 232-1554; P = 0.008) more steps than end-stage osteoarthritic participants, respectively. Postoperative unilateral hip arthroplasty participants demonstrated levels of moderate-to-vigorous physical activity and daily step count equivalent to nonarthritic controls. No difference in physical activity was observed between any cohorts in terms of overall acceleration, or time spent in daily light activity, sedentary behavior, or sleep. Conclusions: Hip and knee arthroplasties are associated with higher levels of physical activity compared with participants with end-stage arthritis. Unilateral hip arthroplasty patients, in particular, demonstrate equivalence to nonarthritic peers at more than 1 yr after surgery. [ABSTRACT FROM AUTHOR]

Visual content in the form of images and videos has taken over general‐purpose social networks in a variety of ways, streamlining and enriching online communications. We are interested to understand if and to what extent the use of images is popular and helpful in social coding platforms. We mined 9 years of data from two popular software developers' platforms: the Mozilla issue tracking system, that is, Bugzilla, and the most well‐known platform for developers' Q/A, that is, Stack Overflow. We further triangulated and extended our mining results by performing a survey with 168 software developers. We observed that, between 2013 and 2022, the number of posts containing image data on Bugzilla and Stack Overflow doubled. Furthermore, we found that sharing images makes other developers engage more and faster with the content. In the majority of cases in which an image is included in a developer's post, the information in that image is complementary to the text provided. Finally, our results showed that when an image is shared, understanding the content without the information in the image is unlikely for 86.9% of the cases. Based on these observations, we discuss the importance of considering visual content when analyzing developers and designing automation tools. [ABSTRACT FROM AUTHOR]

Summary: Models are core assets in Model‐Driven Engineering (MDE). They are pervasively used throughout software development processes to leverage automation, increase cost‐effectiveness, and enhance quality factors. Model repositories have been successfully proposed to enforce reuse and elicit correlations among modeling artifacts, enabling storing operations on model‐based artifacts and collaborative modeling features. Maintaining and improving the quality of modeling artifacts is mandatory for software quality scale‐ups. Limiting the exploration of datasets and repositories of models to individual artifacts might reduce the ability to capture insights and reuse opportunities. This paper proposes AMINO, an analytics tool for model repositories supporting the discovery and quality evaluation of modeling ecosystems. [ABSTRACT FROM AUTHOR]

In this article, we offer theory‐grounded narratives of a 4‐year participatory design process of a Learning Analytics tool with K‐12 educators. We describe how we design‐in‐partnership by leveraging educators' routines, values and cultural representations into the designs of digital dashboards. We make our long‐term reasoning visible by reflecting upon how design decisions were made, discussing key tensions and analysing to what extent the developed tools were taken up in practice. Through thick design narratives, we reflect upon how cultural forms—recognizable cultural constructs that might cue and facilitate specific activities—were identified among educators and informed the design of a dashboard. We then examined the extent to which the designed tool supported coaches and teachers to engage in Generative Uncertainty, an interpretive stance in which educators manifest productive inquiries towards data. Our analysis highlights that attuning to cultural forms is a valuable first step but not enough towards designing LA tools for systems in ways that fit institutionalized practices, challenge instrumental uses and spur productive inquiry. We conclude by offering two key criteria for making culturally‐grounded design decisions in the context of long‐term partnerships. Practitioner notesWhat is already known about this topic Participatory design can invite stakeholders to directly inform the creation of LA artefacts that fit their needs, context and cultural markers.What this paper adds Cultural forms can be identified and leveraged in the design of LA tools.HCLA scholars ought to design for systems—the complex body of organizational routines, cultural practices and interactions among multiple stakeholders—and not just for users.Implications for practice and/or policy Leveraging cultural forms in LA needs to be accompanied by a critical view of which practices, behaviours, values and structures are suggested by such forms.Designing features that are easy to use, are associated with concrete tasks, and fit into existing cultural practices are three criteria for embedding cultural forms into LA design. [ABSTRACT FROM AUTHOR]

Aims: To comprehend the current research hotspots and emerging trends in big data research within the global nursing domain. Design: Bibliometric analysis. Methods: The quality articles for analysis indexed by the science core collection were obtained from the Web of Science database as of February 10, 2023.The descriptive, visual analysis and text mining were realized by CiteSpace and VOSviewer. Results: The research on big data in the nursing field has experienced steady growth over the past decade. A total of 45 core authors and 17 core journals around the world have contributed to this field. The author's keyword analysis has revealed five distinct clusters of research focus. These encompass machine/deep learning and artificial intelligence, natural language processing, big data analytics and data science, IoT and cloud computing, and the development of prediction models through data mining. Furthermore, a comparative examination was conducted with data spanning from 1980 to 2016, and an extended analysis was performed covering the years from 1980 to 2019. This bibliometric mapping comparison allowed for the identification of prevailing research trends and the pinpointing of potential future research hotspots within the field. Conclusions: The fusion of data mining and nursing research has steadily advanced and become more refined over time. Technologically, it has expanded from initial natural language processing to encompass machine learning, deep learning, artificial intelligence, and data mining approach that amalgamates multiple technologies. Professionally, it has progressed from addressing patient safety and pressure ulcers to encompassing chronic diseases, critical care, emergency response, community and nursing home settings, and specific diseases (Cardiovascular diseases, diabetes, stroke, etc.). The convergence of IoT, cloud computing, fog computing, and big data processing has opened new avenues for research in geriatric nursing management and community care. However, a global imbalance exists in utilizing big data in nursing research, emphasizing the need to enhance data science literacy among clinical staff worldwide to advance this field. Clinical Relevance: This study focused on the thematic trends and evolution of research on the big data in nursing research. Moreover, this study may contribute to the understanding of researchers, journals, and countries around the world and generate the possible collaborations of them to promote the development of big data in nursing science. [ABSTRACT FROM AUTHOR]

Context: Access to real-time data that provide accurate and timely information about the status and extent of disease spread could assist management of the COVID-19 pandemic and inform decision-making. Aim: To demonstrate our experience with regard to implementation of technical and architectural infrastructure for a near real-time electronic health record-based surveillance system for COVID-19 in Iran. Method: This COVID-19 surveillance system was developed from hospital information and electronic health record (EHR) systems available in the study hospitals in conjunction with a set of open-source solutions; and designed to integrate data from multiple resources to provide near real-time access to COVID-19 patients' data, as well as a pool of health data for analytical and decision-making purposes. Outcomes: Using this surveillance system, we were able to monitor confirmed and suspected cases of COVID-19 in our population and to automatically notify stakeholders. Based on aggregated data collected, this surveillance system was able to facilitate many activities, such as resource allocation for hospitals, including managing bed allocations, providing and distributing equipment and funding, and setting up isolation centres. Conclusion: Electronic health record systems and an integrated data analytics infrastructure are effective tools to enable policymakers to make better decisions, and for epidemiologists to conduct improved analyses regarding COVID-19. Implications: Improved quality of clinical coding for better case finding, improved quality of health information in data sources, data-sharing agreements, and increased EHR coverage in the population can empower EHR-based COVID-19 surveillance systems. [ABSTRACT FROM AUTHOR]

Propelled by the transformative power of modern information and communication technologies, digitalization of data, and the increasing affordability of high-performance computing, Big Data science has brought forth revolutionary advancement in many areas of business, industry, health, and medicine. The HIV research and care service community is no exception to the benefits from the availability and utilization of Big Data analytics. Electronic health record (EHR) data (e.g., administrative and billing data, electronic medical records, or other digital records of information pertinent to individual or population health) are an essential source of health and disease outcome data because of the large amount of real-world, comprehensive, and often longitudinal data, which provide a good opportunity for leveraging advanced Big Data analytics in addressing challenges in HIV prevention, treatment, and care. This review focuses on studies that apply Big Data analytics to EHR data with aims to synthesize the HIV-related issues that EHR data studies can tackle, identify challenges in the utilization of EHR data in HIV research and practice, and discuss future needs and directions that can realize the promising potential role of Big Data in ending the HIV epidemic. [ABSTRACT FROM AUTHOR]

Objective: Appearance focused self‐concept (i.e., overvaluing the importance of appearance for self‐definition and self‐worth) is theorized to predict dietary restraint and binge eating in the short‐term (e.g., daily life). Yet, no research has examined whether appearance focused self‐concept increases within‐persons during a day and if such increases are linked to greater dietary restraint and binge eating for that day. We addressed this gap in knowledge. Method: Sixty‐three female university students completed four items from the Beliefs About Appearance Scale as a measure of appearance focused self‐concept six times per day (9 am, 11 am, 1 pm, 3 pm, 5 pm, and 7 pm) for 14 days. Daily at 9 pm, they completed measures of dietary restraint and binge eating for that day. Results: Analyzing data from 555 days, latent growth curve analyses revealed a small‐to‐moderate linear increase in appearance focused self‐concept from 9 am to 3 pm that plateaued from 3 to 7 pm. A more rapid linear increase in appearance focused self‐concept from 9 am to 3 pm was associated with greater binge‐eating frequency during that day, but not with dietary restraint. The findings were observed when adjusting for between‐day appearance focused self‐concept at 9 am, which was positively associated with dietary restraint and binge‐eating frequency. Discussion: Findings are discussed in relation to research on appearance focused self‐concept as a risk factor for disordered eating. Findings are also discussed in relation to how sociocultural factors may increase appearance focused self‐concept over time. Future research should delve into the within‐day dynamic interplay between appearance focused self‐concept and disordered eating. Public significance: Our study revealed a noteworthy increase in the importance women attach to their appearance over the course of a day, correlating with increased binge eating during that day. Additionally, heightened appearance importance at the onset of a day was associated with more dietary restraint and binge eating during that day. These findings suggest a shorter timescale for the connection between appearance importance and disordered eating than previously understood. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been experienced as the most significant global disaster after the Spanish flue in 1918. Millions of people lost their life due to a lack of preparedness and ineffective strategies for managing humanitarian supply chains (HSC). Based on the learnings from this pandemic outbreak, different strategies for managing the effective HSC have been explored in the present context of pandemics through a systematic literature review. The findings highlight some of the major challenges faced during the COVID-19 pandemic, such as lack of planning and preparedness, extended shortages of essential lifesaving items, inadequate lab capacity, lack of transparency and visibility, inefficient distribution network, high response time, dependencies on single sourcing for the medical equipment and medicines, lack of the right information on time, and lack of awareness about the protocol for the treatment of the viral disease. Some of the significant learnings observed from this analysis are the use of multiple sourcing of essential items, joint procurement, improving collaboration among all stakeholders, applications of IoT and blockchain technologies for improving tracking and traceability of essential commodities, application of data analytics tools for accurate prediction of next possible COVID wave/disruptions and optimization of distribution network. Limited studies are focused on finding solutions to these problems in managing HSC. Therefore, as a future scope, researchers could find solutions to optimizing the distribution network in context to pandemics, improving tracing and tracking of items during sudden demand, improving trust and collaborations among different agencies involved in HSC. [ABSTRACT FROM AUTHOR]

This scoping review investigates the untapped potential of predictive analytics in healthcare social work, specifically targeting early intervention frameworks. Despite the escalating attention predictive analytics has garnered across multiple disciplines, its tailored application in social work remains notably sparse. This study endeavors to fill this lacuna by meticulously reviewing the extant literature and delineating the prospective advantages and inherent constraints of integrating predictive analytics into healthcare social work. The outcomes of this inquiry enrich the prevailing dialogue on the utility of predictive analytics in healthcare, offering indispensable perspectives for practitioners and policymakers in the social work domain. [ABSTRACT FROM AUTHOR]

Background: Technological innovations such as Learning Management Systems (LMS) are becoming more and more prevalent in the learning environments of students. Distilling and acting on knowledge gathered from these systems, the field known as learning analytics, allows educators to hone their craft and support students more effectively by providing timely interventions. Objectives: While most learning analytics studies focus on using LMS data to predict performance, this study instead predicts students' self‐reported time‐management skills using trace data from the Canvas LMS. This is done for courses at one Dutch technical university with in total 462 students. Methods: Linear regression and multi‐level regression models are constructed using both theory and findings from previous research. The predictions made by these models are compared to previously filled in questionnaire data to validate the results. Results: Our results show that models can be constructed and time‐management can be predicted for individual courses. Furthermore, there are several predictors that are significant in multiple models. However, these models and predictions are not immediately transferable to other courses. Conclusions: The study therefore emphasizes the need for further research, using multiple sources of data or more theoretically grounded predictors, to investigate the extent of the portability issues with these predictive models. Despite this we were able to predict the students' self‐reported time‐management skills in multiple different courses using Learning Analytics, and managed to identify multiple consistently predictive trace data variables. Lay Description: What is currently known: Learning analytics is mainly used to predict academic performance.Models can be constructed for single courses that are able to predict students' time‐management skills with a high accuracyIt is unclear whether or not models predicting time‐management for one course can be used for other courses as well. What do we add: We predict time‐management skills for multiple different courses.We identify multiple predictors that are consistently predictive of time‐management.We identify a possible portability problem in predictive models. What are the implications of our findings: For some courses, predictive models can be used to identify students with lower time‐management skills. These can constitute the target group for further educational support.For other courses, teachers can use predictive models to identify students who should be monitored more closely using additional means to decide whether they constitute the target group for further support.Course design needs to be taken into account when constructing a predictive model. [ABSTRACT FROM AUTHOR]

Drug shortages are a persistent and serious problem in the United States, affecting patient care and health care costs. This article canvasses factors that contribute to drug shortages, such as manufacturing complexity, price, and quality inspection records. This article further proposes an early warning system and payment, contracting, and pricing innovations to mitigate drug shortages and offers data-driven recommendations to stakeholders looking to protect the supply of quality medicines. [ABSTRACT FROM AUTHOR]

Changes in digital technology, increasing volume of data collection, and advances in methods have the potential to unleash the value of big data generated through the education of health professionals. Coupled with this potential are legitimate concerns about how data can be used or misused in ways that limit autonomy, equity, or harm stakeholders. This consensus statement is intended to address these issues by foregrounding the ethical imperatives for engaging with big data as well as the potential risks and challenges. Recognizing the wide and ever evolving scope of big data scholarship, we focus on foundational issues for framing and engaging in research. We ground our recommendations in the context of big data created through data sharing across and within the stages of the continuum of the education and training of health professionals. Ultimately, the goal of this statement is to support a culture of trust and quality for big data research to deliver on its promises for health professions education (HPE) and the health of society. Based on expert consensus and review of the literature, we report 19 recommendations in (1) framing scholarship and research through research, (2) considering unique ethical practices, (3) governance of data sharing collaborations that engage stakeholders, (4) data sharing processes best practices, (5) the importance of knowledge translation, and (6) advancing the quality of scholarship through multidisciplinary collaboration. The recommendations were modified and refined based on feedback from the 2022 Ottawa Conference attendees and subsequent public engagement. Adoption of these recommendations can help HPE scholars share data ethically and engage in high impact big data scholarship, which in turn can help the field meet the ultimate goal: high-quality education that leads to high-quality healthcare. [ABSTRACT FROM AUTHOR]

The purpose of this scoping review was to examine how mixed methods research (MMR) has been applied in adapted physical activity (APA) research about children and adolescents age 5–18 years with a disability. Six electronic databases were searched to retrieve relevant studies published between 2003 and 2020. Sixty-four studies were identified and analyzed. The findings were organized into five categories of interest: publication information, study objectives, mixed methods research design, participants' information, and data integration. Challenges related to the design and publication of MMR in APA were uncovered, and suggestions for improvement are provided. This study adds to the knowledge of MMR design, and it provides an understanding of the underlying processes and methodological strategies that have guided this approach in APA research. This article will encourage APA researchers to engage in MMR while also aligning future studies with contemporary MMR literature and publication standards. [ABSTRACT FROM AUTHOR]

More than 17.7 million people in the U.S. care for older adults. Analyzing population datasets can increase our understanding of the needs of family caregivers of older adults. We reviewed 14 U.S. population-based datasets (2003–2023) including older adults' and caregivers' data to assess inclusion and measurement of 8 caregiving science domains, with a focus on whether measures were validated and/or unique variables were used. Challenges exist related to survey design, sampling, and measurement. Findings highlight the need for consistent data collection by researchers, state, tribal, local, and federal programs, for improved utility of population-based datasets for caregiving and aging research. [ABSTRACT FROM AUTHOR]

The article offers information on the implementation of iMDsoft's Metavision as Boston Children's Hospital's anesthesia information management system (AIMS) software in 2012. It discusses the development of a "low-flow" interface for the system by 2015 to enhance teaching and patient safety during the delivery of low flow anesthesia.

Background: Twitter is rich in data for text and data analytics research, with the ability to capture trends. Objectives: This study examines Canadian tweets on marijuana legalization and terminology used. Presented as a case study, Twitter analytics will demonstrate the varied applications of how this kind of research method may be used to inform library practice. Methods: Twitter API was used to extract a subset of tweets using seven relevant hashtags. Using open‐source programming tools, the sampled tweets were analysed between September to November 2018, identifying themes, frequently used terms, sentiment, and co‐occurring hashtags. Results: More than 1,176,000 tweets were collected. The most popular hashtag co‐occurrence, two hashtags appearing together, was #cannabis and #CdnPoli. There was a high variance in the sentiment analysis of all collected tweets but most scores had neutral sentiment. Discussion: The case study presents text‐mining applications relevant to help make informed decisions in library practice through service analysis, quality analysis, and collection analysis. Conclusions: Findings from sentiment analysis may determine usage patterns from users. There are several ways in which libraries may use text mining to make evidence‐informed decisions such as examining all possible terminologies used by the public to help inform comprehensive evidence synthesis projects and build taxonomies for digital libraries and repositories. [ABSTRACT FROM AUTHOR]

Background: This article delves into an in-depth analysis of the statistically significant differences in AI support levels for project management between SMEs and large enterprises. The research was conducted based on a comprehensive survey encompassing a sample of 473 SMEs and large Slovenian enterprises. Methods: To validate the observed differences, statistical analysis, specifically the Mann–Whitney U test, was employed. Results: The results confirm the presence of statistically significant differences between SMEs and large enterprises across multiple dimensions of AI support in project management. Large enterprises exhibit on average a higher level of AI adoption across all five AI utilization dimensions. Specifically, large enterprises scored significantly higher (p < 0.05) in AI adopting strategies and in adopting AI technologies for project tasks and team creation. This study's findings also underscored the significant differences (p < 0.05) between SMEs and large enterprises in their adoption and utilization of AI technologies for project management purposes. While large enterprises scored above 4 for several dimensions, with the highest average score assessed (mean value 4.46 on 1 to 5 scale) for the usage of predictive Analytics Tools to improve the work on the project, SMEs' average levels, on the other hand, were all below 4. SMEs in particular may lag in incorporating AI into various project activities due to several factors such as resource constraints, limited access to AI expertise, or risk aversion. Conclusions: The results underscore the need for targeted strategies to enhance AI adoption in SMEs and leverage its benefits for successful project implementation and strengthen the company's competitiveness. [ABSTRACT FROM AUTHOR]

Learning analytics platforms (LAPs) have become important modes of anticipatory governance in education. Educational futures are governed by utilizing various forms of learning analytics to track student data over time, suggesting that school leaders and teachers are expected to improve school quality by engaging with digital presentations of prediction, anticipation, and decision-making. This study investigates the LAPs Conexus Engage and Insight as they unfold in school leaders' practice by drawing from actor-network theory. School leaders' interaction with the tools are examined through audio and screen recorded interviews at three lower secondary schools in Norway. The findings show how anticipation emerged in actor-networks as both fluid and stable, encouraging the problematizations and priorities of school leaders. School leaders also adapted the LAPs to their own practice. The findings further suggested that time emerges on the premises of LAPs as entangled events rather than through chronological understandings of time. [ABSTRACT FROM AUTHOR]

Traditional Chinese medicine (TCM) has been used for thousands of years and has been proven to be effective at treating many complicated illnesses with minimal side effects. The application and advancement of TCM are, however, constrained by the absence of objective measuring standards due to its relatively abstract diagnostic methods and syndrome differentiation theories. Ongoing developments in machine learning (ML) and deep learning (DL), specifically in computer vision (CV) and natural language processing (NLP), offer novel opportunities to modernize TCM by exploring the profound connotations of its theory. This review begins with an overview of the ML and DL methods employed in TCM; this is followed by practical instances of these applications. Furthermore, extensive discussions emphasize the mature integration of ML and DL in TCM, such as tongue diagnosis, pulse diagnosis, and syndrome differentiation treatment, highlighting their early successful application in the TCM field. Finally, this study validates the accomplishments and addresses the problems and challenges posed by the application and development of TCM powered by ML and DL. As ML and DL techniques continue to evolve, modern technology will spark new advances in TCM. [ABSTRACT FROM AUTHOR]

Assessing the intensity characteristics of specific soccer drills (matches, small-side game, and match-based exercises) could help practitioners to plan training sessions by providing the optimal stimulus for every player. In this paper, we propose a data analytics framework to assess the neuromuscular or metabolic characteristics of a soccer-specific exercise in relation with the expected match intensity. GPS data describing the physical tasks' external intensity during an entire season of twenty-eight semi-professional soccer players competing at the fourth Italian division were used in this study. A supervised machine-learning approach was tested in order to detect difference in playing positions in different sport-specific drills. Moreover, a non-supervised machine-learning model was used to profile the match neuromuscular and metabolic characteristics. Players' playing positions during matches and match-based exercises are characterised by specific metabolic and neuromuscular characteristics related to tactical demands, while in the small-side game these differences are not detected. Additionally, our framework permits to evaluate if the match performance request is mirrored during training drills. Practitioners could evaluate the type of stimulus performed by a player in a specific training drill in order to assess if they reflect the matches characteristics of their specific playing position. [ABSTRACT FROM AUTHOR]

The article discusses the highlights of the meeting of the 2023 American Society of Health-System Pharmacists (ASHP) Commission on Goals held on March 7, 2023. Topics mentioned include the optimization of medication therapy through advanced analytics and data-driven healthcare, the enhancement of the quality of patient care, the importance of data stewardship and governance, and ways to support comprehensive medication management.

Simple Summary: Lung cancer therapeutics have dramatically improved in recent years. Indeed, precision oncology could be exemplified by non-small cell lung cancer (NSCLC), with molecular profiling and programmed death ligand 1 (PD-L1) immunohistochemical expression representing an integral part of its tailored treatment. The present narrative review aims to highlight the promising role of artificial intelligence (AI) technologies in the optimal, patient-centered management of NSCLC, by distilling as well as interpreting big data. Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable. [ABSTRACT FROM AUTHOR]

Data-driven culture is considered to bring business-oriented cultural transformation to a firm. It is considered to provide substantial dividends to the firms' product and process innovations. Recently, several firms have been using different advanced technology-embedded business analytics (BA) tools to improve their business performance. Again, advancement of information and communication technology has helped firms to explore the option to use BA tools with artificial intelligence. This has brought radical change in the business-oriented cultural landscape of the firms to arrive at accurate decision-making to improve their innovation and performance. In this perspective, the aim of this study is to show how a firm's data-driven culture impacts its product and process innovation, which in turn improves its performance and provides better competitive advantage in the current business environment. With the help of background study, a resource-based view model and different theories, a conceptual model has been developed. The conceptual model has been validated with 456 usable responses from the employees of different firms using different business analytics tools. The study highlights that data-driven culture highly influences both product and process innovation, making the firm more competitive in the industry. In this study, leadership support and data-driven culture have been taken as moderators, whereas firm size, firm age and industry type have been taken as control variables. [ABSTRACT FROM AUTHOR]

Twitter (recently renamed X) is used by academic anesthesiology departments as a social media platform for various purposes. We hypothesized that Twitter (X) use would be prevalent among academic anesthesiology departments and that the number of tweets would vary by region, physician faculty size, and National Institutes of Health (NIH) research funding rank. We performed a descriptive study of Twitter (X) use by academic anesthesiology departments (i.e. those with a residency program) in 2022. Original tweets were collected using a Twitter (X) analytics tool. Summary statistics were reported for tweet number and content. The median number of tweets was compared after stratifying by region, physician faculty size, and NIH funding rank. Among 166 academic anesthesiology departments, there were 73 (44.0%) that had a Twitter (X) account in 2022. There were 3,578 original tweets during the study period and the median number of tweets per department was 21 (25th-75th = 0, 75) with most tweets (55.8%) announcing general departmental news and a smaller number highlighting social events (12.5%), research (11.1%), recruiting (7.1%), DEI activities (5.2%), and trainee experiences (4.1%). There was no significant difference in the median number of tweets by region (P = 0.81). The median number of tweets differed significantly by physician faculty size (P<0.001) with larger departments tweeting more and also by NIH funding rank (P = 0.005) with highly funded departments tweeting more. In 2022, we found that less than half of academic anesthesiology departments had a Twitter (X) account, and the median number of annual tweets per account was relatively low. Overall, Twitter (X) use was less common than anticipated among academic anesthesiology departments and most tweets focused on promotion of departmental activities or individual faculty. There may be opportunities for more widespread and effective use of Twitter (X) by academic anesthesiology departments including education about anesthesiology as a specialty. [ABSTRACT FROM AUTHOR]

The article focuses on a big data-driven observational study comparing the effectiveness of acute migraine therapies, with triptans identified as the most efficacious class, led by eletriptan. Topics discussed include the challenges in assessing real-world effectiveness, the study's strengths and limitations, and considerations for interpreting results, such as demographic biases and the exclusion of newer treatments like calcitonin gene-related peptide (CGRP)-targeted therapies.

Background: Australian cardiovascular disease (CVD) prevention guidelines recommend absolute CVD risk assessment, but less than half of eligible patients have the required risk factors recorded due to fragmented implementation over the last decade. Co-designed decision aids for general practitioners (GPs) and consumers have been developed that improve knowledge barriers to guideline-recommended CVD risk assessment and management. This study used a stakeholder consultation process to identify and pilot test the feasibility of implementation strategies for these decision aids in Australian primary care. Methods: This mixed methods study included: (1) stakeholder consultation to map existing implementation strategies (2018-20); (2) interviews with 29 Primary Health Network (PHN) staff from all Australian states and territories to identify new implementation opportunities (2021); (3) pilot testing the feasibility of low, medium, and high resource implementation strategies (2019-21). Framework Analysis was used for qualitative data and Google analytics provided decision support usage data over time. Results: Informal stakeholder discussions indicated a need to partner with existing programs delivered by the Heart Foundation and PHNs. PHN interviews identified the importance of linking decision aids with GP education resources, quality improvement activities, and consumer-focused prevention programs. Participants highlighted the importance of integration with general practice processes, such as business models, workflows, medical records and clinical audit software. Specific implementation strategies were identified as feasible to pilot during COVID-19: (1) low resource: adding website links to local health area guidelines for clinicians and a Heart Foundation toolkit for primary care providers; (2) medium resource: presenting at GP education conferences and integrating the resources into audit and feedback reports; (3) high resource: auto-populate the risk assessment and decision aids from patient records via clinical audit software. Conclusions: This research identified a wide range of feasible strategies to implement decision aids for CVD risk assessment and management. The findings will inform the translation of new CVD guidelines in primary care. Future research will use economic evaluation to explore the added value of higher versus lower resource implementation strategies. [ABSTRACT FROM AUTHOR]

This study delves into the intricate landscape of biomass utilization within the food and energy markets. It entails a systematic review of the existing literature with the aim of unraveling the complexities of the food and energy discourse, especially in the context of significant market factors influencing biomass use for food and energy. We leveraged the Scopus database to examine 73 pertinent scientific articles carefully selected following the PRISMA framework. The articles were analyzed using the advanced qualitative data analytics tool NVivo 12 Plus. Furthermore, we employed the Biblioshiny R-package tool to extract valuable insights from the metadata, unveiling pivotal trends and providing descriptive statistical details. The findings offer comprehensive insights into the debate on biomass utilization from 2010 to 2023, tracing the influence of the COVID-19 pandemic. We identify regions that have made notable contributions and highlight those that require increased attention. The analysis underscores the collaborative nature of this field, with 281 authors contributing to 39 different sources. Surprisingly, the observed annual growth rate of −10.93% indicates a potential decline in research output in this field. Nevertheless, the sources identified in our research provide a valuable roadmap for further research exploration of the biomass–food–energy nexus. [ABSTRACT FROM AUTHOR]

Data generated from the internet and modern applications is extensive and rapidly expanding. So, all modern applications must successfully perform tasks using this massive data. Therefore, one of the significant factors for the success of any application is understanding and extracting meaningful information using digital analytics tools to positively impact the application's performance and deal with challenges that can be encountered. On the other hand, cloud computing is simply an environment comprising a collection of high-performance services from various vendors. These services can frequently access and process massive amounts of data faster than a traditional computer. One of these services is cloud analytics, which applies cloud-based analytic algorithms against data in a private or public cloud to get the desired outcome. This paper differentiates between several analytics methodologies to create highly consistent, logical, and information-rich summaries. This research has discussed and analyzed many studies related to this field. Therefore, the results of this paper can be utilized to determine the advantages of these methods, which will help future researchers in their research for a more organized and thorough analysis when dealing with such applications. In addition, this paper attempts to discover new directions and propose new guidelines in this evolving field. [ABSTRACT FROM AUTHOR]

The recent coronavirus disease (COVID-19) pandemic has severely impacted nations across the globe. Not only has it created economic shocks, but also long-term impacts on the social and psychological behaviors of the public. This can be attributed to the severity of the pandemic and because of the preventive and control measures such as global lockdowns, social distancing, and self-isolation that the governments imposed. Previous studies have reported significant changes in human emotions and behaviors are used to measure public sentiments about certain phenomena (such as the recent pandemic). The present study aims to study the public's sentiments during the COVID-19 outbreak based on an analytics review of public tweets highlighting changes in emotions. A data set of 58,320 tweets extracted from Twitter and 61 academic articles was explored to analyze behavioral and emotional changes during previous and current pandemic situations. We chose the research process approach--COVID-19 (RPA-COV) approach, which was combined with the literature-based thematic analysis (LBTA) and the COVID-19 Twitter analytics (COVTA). The sentiments' analysis results were coupled with word-tree analysis and highlighted that the public showed more highly neutral, positive, and mixed emotions than negative ones. The analysis pointed that people may react differently on Twitter as compared to real-life circumstances. The present study makes a significant contribution toward understanding how the public expresses their sentiments in pandemic situations. [ABSTRACT FROM AUTHOR]

Big data and artificial intelligence are buzzwords that everyone is talking about and yet always provide a touch of science fiction to the scenery. What is the status of these topics in anesthesia? Are the first robots already rolling through the corridors while doctors are getting bored as all the work has been done? Spoiler alert! We are still far away from achieving this. Initially, paper charts and analogue notes stand in the way of comprehensive digitization. Source systems need to be merged and data standardized, harmonized and validated. Therefore, the friendly android that is rolling towards us, waving and holding a freshly brewed cup of coffee in our thoughts will have to wait; however, a glimpse of the future is already evident in some clinics and the first promising developments are already showing what could be the standard tomorrow. Learning algorithms calculate the length of stay individually for each patient in the intensive care unit (ICU), reducing negative consequences such as readmission and mortality. The field of ultrasound technology for regional anesthesia and closed-loop anesthesia systems is also demonstrating the benefits of artificial intelligence (AI)-assisted technologies in practice. The efforts are diverse and ambitious but they repeatedly collide with privacy challenges and significant capital expenditure, which weigh heavily on an already financially strained healthcare system; however, anyone who listens carefully to the medical staff knows that robots are not what they would expect and the buzzwords big data and artificial intelligence might be less science fiction than initially assumed. [ABSTRACT FROM AUTHOR]

To inform and optimise a cochlear implant (CI) fitting software design through an analysis of big data to define array-specific comfort (C) level profiles, frequently-used MAP parameters, and the minimum number of Neural Response Telemetry thresholds (tNRT) needed to create an accurate profile. To evaluate the software's ease of use and completion time for AutoNRT®s. MAPs analysis. Clinical study evaluating software use in creating MAPs, addressing sound-quality issues and setting patient goals. MAPs (N = 39,885); CI recipients (N = 47) and clinicians (N = 19). Distinct C-level profiles were observed for lateral-wall, contour, and slim-modiolar electrode arrays. Default settings were used for most MAP parameters (13/16) except for Pulse Width, Rate, and Maxima. Nine tNRT measurements were required for an accurate C-level profile. Measurement-time of nine tNRTs via the new algorithm was comparable to five tNRTs using the previous algorithm. Nearly all (99%) clinical tasks were completed by clinicians with the first use of the software. Most CI recipients (79.5%) rated goal-setting as valuable. Custom Sound Pro fitting software developed based on big data analysis incorporates a guided fitting workflow and expected fitting ranges. It helps to improve clinical efficiency, is easy to use and supports patient-centred care. [ABSTRACT FROM AUTHOR]

Background: Traditional methods for benchmarking dietitian productivity are time‐consuming and fail to accurately measure the total time spent providing nutrition care. An electronic health record (EHR)‐based tool that allows for daily tracking of both face‐to‐face and patient care coordination time for dietitians was created. We assessed whether it provided consistent, continuous measurement of time and productivity. Methods: This tool was created in an independent paediatric academic healthcare system in the USA. Time spent by dietitians in face‐to‐face settings and care coordination were tracked. Changes in time spent between the years 2013–2016 versus 2018–2019 were also analysed. Results: The outpatient dietitian spent a mean total of 66.4 min per patient (37.8 ± 6.0 min in face‐to‐face care and 28.6 ± 5.2 min in care coordination). The total times and fractions spent on face‐to‐face and care coordination time varied by specialty. Comparison of the two periods of time revealed 75% more productivity on average of dietitians in different outpatient settings after including care coordination tracking. In addition, dietitians were more likely to document time spent in 5‐min increments after the institution of this methodology as opposed to 15‐min increments. Conclusions: An EHR‐based tool that facilitates the documentation of both face‐to‐face time and patient care coordination time is feasible and enables consistent, continuous measurement of time and productivity. The real‐time data from this tool can be used to support adequate dietitian staffing and be used to create a multicentre database to measure the actual time dietitians need to provide care and generate consistent staffing benchmarks. Key points: An electronic health record (EHR)‐based tool that facilitates the documentation of both face‐to‐face time and patient care coordination time is feasible and enables consistent, continuous measurement of time and productivity.Outpatient dietitians spend varying amounts of time on face‐to‐face care versus and care coordination depending on the specialty. [ABSTRACT FROM AUTHOR]

Despite having worked in higher education for over twenty years, I am still, first and foremost, a practicing nurse. My employer requires me to be a nurse and my regulator regards what I do as nursing. My practice is regulated by the Code and informed by nursing ethics. If I am nurse, practicing nursing, does that mean that my students are my patients? This paper considers how the relationship that I have with my students can be informed by the ethics of the nurse/patient relationship. After some initial theoretical preparation concerning argument from analogy, the paper identifies some areas for comparison between the two relationships. Areas of similarity and difference identify two areas of concern: Nurse education and educators regularly engage in coercion and surveillance in an attempt to increase student success, both of which would be considered outside nursing ethics. It is concluded that these coercive practices are not conducive to an environment where character is cultivated. Despite current financial and workforce pressures, nurse lecturers and more especially their managers would do well to return to the professional ethics of nursing to question and guide their practice. [ABSTRACT FROM AUTHOR]

Simple Summary: TCGAnalyzeR provides a novel web site integrating 123 pre-computed pan-cancer cohorts (i.e., microsatellite instability, immune, metastasis, PAM50, Triple Negative breast cancer, idh1-mutated glioblastoma, etc.), along with our own iCluster+ subcohorts, computed based on pre-processed single-nucleotide variations, copy number variations, differential expression, miRNA, methylation, and clinical data. TCGAnalyzeR interface provides an optimized and fully customizable experience to each user, enabling the selection of their own "My patients" or "My genes" to a clipboard. Several use cases of the web site are presented as Help documents. For humans, the parallel processing capability of visual recognition allows for faster comprehension of complex scenes and patterns. This is essential, especially for clinicians interpreting big data for whom the visualization tools play an even more vital role in transforming raw big data into clinical decision making by managing the inherent complexity and monitoring patterns interactively in real time. The Cancer Genome Atlas (TCGA) database's size and data variety challenge the effective utilization of this valuable resource by clinicians and biologists. We re-analyzed the five molecular data types, i.e., mutation, transcriptome profile, copy number variation, miRNA, and methylation data, of ~11,000 cancer patients with all 33 cancer types and integrated the existing TCGA patient cohorts from the literature into a free and efficient web application: TCGAnalyzeR. TCGAnalyzeR provides an integrative visualization of pre-analyzed TCGA data with several novel modules: (i) simple nucleotide variations with driver prediction; (ii) recurrent copy number alterations; (iii) differential expression in tumor versus normal, with pathway and the survival analysis; (iv) TCGA clinical data including metastasis and survival analysis; (v) external subcohorts from the literature, curatedTCGAData, and BiocOncoTK R packages; (vi) internal patient clusters determined using an iClusterPlus R package or signature-based expression analysis of five molecular data types. TCGAnalyzeR integrated the multi-omics, pan-cancer TCGA with ~120 subcohorts from the literature along with clipboard panels, thus allowing users to create their own subcohorts, compare against existing external subcohorts (MSI, Immune, PAM50, Triple Negative, IDH1, miRNA, metastasis, etc.) along with our internal patient clusters, and visualize cohort-centric or gene-centric results interactively using TCGAnalyzeR. [ABSTRACT FROM AUTHOR]

Research in social sciences has been questioned in the past decade due to the failure to replicate earlier work. To improve the trust in results, best practices that reduce data‐analytical flexibility have been proposed, such as preregistration. Another more recent such development regards the multiverse approach. The current study sought to demonstrate the benefits of applying the multiverse approach within developmental psychology. Using the relation between self‐esteem and peer victimization in adolescence as an example, we examined how both the concurrent as well as longitudinal association was impacted by data‐analytical decisions, such as the operationalization of variables, treatment of outliers, inclusion of covariates, and paths estimated in the model. Indeed, through the multiverse approach, we showed that and how these decisions matter for the model fit and estimates of the associations. We were thus able to illustrate that the multiverse approach increases transparency, makes it possible to examine the robustness of effects and can help advance theory building. Limitations of this approach as well as recommendations for other researchers are discussed. [ABSTRACT FROM AUTHOR]

Background: Using third‐party resources to manage remote monitoring (RM) data from implantable cardiac electronic devices (CIEDs) can assist in device clinic workflows. However, each hospital‐acquired data is not used for further analysis as big data. Methods and results: We developed a real‐time and automatically centralized system of CIED information from multiple hospitals. If the extensive data‐based analysis suggests individual problems, it can be returned to each hospital. To show its feasibility, we prospectively analyzed data from six hospitals. For example, unexpected abnormal battery levels were easily illustrated without recall information. Conclusions: The centralized RM system could be a new platform that promotes the utilization of device data as big data, and that information could be used for each patient's practice. [ABSTRACT FROM AUTHOR]

Big data analytics has attracted significant scholarly attention over the past few years due to its critical role in directing organizational decision making. Big data analytics capabilities (BDAC) are increasingly being suggested to be of great help in providing competitive advantages to the firms across industrial settings. Although, prior research has examined the influence of BDAC from a technological perspective; there is a lack of a research from a broader strategic perspective. Correspondingly, this study builds on dynamic capabilities view to propose a conceptual model that investigates the direct and indirect effects between three dimensions of BDAC on environmental scanning and organization's strategic intent. This study adopted the questionnaire-based survey method to collect the data from 179 senior managers of pharmaceutical companies in Egypt. The results revealed that only the technological dimension of BDAC significantly affected the strategic intent only through supporting data-driven culture and organizational learning. The results also reveal that BDAC is positively associated with environmental scanning, which consequently affects strategic intent. Our results further reveal that environmental scanning mediates the relationship between BDAC and organization's strategic intent. The paper concludes by offering some valuable theoretical and practical implications. [ABSTRACT FROM AUTHOR]

Knowledge explosion is associated with the exponential growth of research literature production, triggering the need for new approaches to structure and synthesize knowledge. Traditional knowledge synthesis approaches have weaknesses like requiring many resources, being time and labor consuming, and being executed manually. To overcome these weaknesses, we developed a new semi-automated synthesis approach, synthetic knowledge synthesis (SKS). The new approach is a triangulation of bibliometrics, bibliometric mapping, and content analysis and can be used by hospital librarians to advance their role as partners in health research. In this paper, we present SKS and demonstrate its use. [ABSTRACT FROM AUTHOR]