Search

We researchers have taken searching for information for granted for far too long. The COVID-19 pandemic shows us the boundaries of academic searching capabilities, both in terms of our know-how and of the systems we have. With hundreds of studies published daily on COVID-19, for example, we struggle to find, stay up-to-date, and synthesize information--all hampering evidence-informed decision making. This COVID-19 "information crisis" is indicative of the broader problem of information overloaded academic research. To improve our finding capabilities, we urgently need to improve how we search and the systems we use. We respond to Klopfenstein and Dampier ("Res Syn Meth." 2020) who commented on our 2020 paper and proposed a way of improving PubMed's and Google Scholar's search functionalities. Our response puts their commentary in a larger frame and suggests how we can improve academic searching altogether. We urge that researchers need to understand that search skills require dedicated education and training. Better and more efficient searching requires an initial understanding of the different goals that define the way searching needs to be conducted. We explain the main types of searching that we academics routinely engage in; distinguishing lookup, exploratory, and systematic searching. These three types must be conducted using different search methods (heuristics) and using search systems with specific capabilities. To improve academic searching, we introduce the "Search Triangle" model emphasizing the importance of matching goals, heuristics, and systems. Further, we suggest an urgently needed agenda toward search literacy as the norm in academic research and fit-for-purpose search systems. [For Klopfenstein and Dampier's "Commentary to Gusenbauer and Haddaway 2020: Evaluating Retrieval Qualities of Google Scholar and PubMed," see EJ1288342.]

Our paper focuses on delineating and scoping international Women's Sheds, a movement that has emerged within the past decade, mainly in Australia, Ireland and the UK. It addresses two main research questions. Firstly, what is the origin, distribution, nature and intent of Women's Sheds internationally to March 2021? Secondly, how might Women's Sheds be located within a typology inclusive of Men's Sheds and a range of community development models? We employed a systematic search via the internet in 2020-21, followed up by attempted email or phone contact to publicly reported Women's Sheds and like organisations internationally. In the process, we created a publicly shareable blog including a database of 122 existing, previously active, developing or planned Women's Sheds and like organisations to 13 March 2021. We identify four nations where self-identified Women's Sheds have operated or commenced within the past decade: Australia (61), the UK (30), Ireland (28) and New Zealand (3), particularly during the five years between 2014 and 2019. The COVID-19 pandemic seriously curtailed this previous momentum and development after March 2020. We identify some similarities but also important differences between Women's and Men's Sheds. We propose a typology that accounts not only for the different ways in which Women's Sheds operate and women participate within their communities but also the different ways in which they locally collaborate (or not) with Men's Sheds in different countries. We conclude that Women's Sheds have largely been created by women in order to claim the shed as a positive female gendered space, in order to create an alternative community of women's hands-on practice. Sheds have operated or commenced within the past decade: Australia (61), the UK (30), Ireland (28) and New Zealand (3), particularly during the five years between 2014 and 2019. The COVID-19 pandemic seriously curtailed this previous momentum and development after March 2020. We identify some similarities but also important differences between Women's and Men's Sheds. We propose a typology that accounts not only for the different ways in which Women's Sheds operate and women participate within their communities but also the different ways in which they locally collaborate (or not) with Men's Sheds in different countries. We conclude that Women's Sheds have largely been created by women in order to claim the shed as a positive female gendered space, in order to create an alternative community of women's hands-on practice.

Internationalization in higher education seems to be an unavoidable process, albeit temporarily limited by the COVID-19 (coronavirus disease of 2019) pandemic. Specifically, internationalization of the curriculum in the context of higher education is a dimension of this internationalization that is less valued in published studies. This paper, through critical reflection, sought to contribute to a deeper understanding of internationalization of the curriculum in higher education. The methodology used consisted of a bibliographic search in international databases, and the selected documents were analyzed using the content analysis technique. This analysis allowed concluding that internationalization of the curriculum in higher education is a complex process and involves several actors, with various challenges to be considered. For this process to be successful, it involves the ability to be attentive to the cultural multiplicity that will be experienced in classes where this internationalization of the curriculum exists.

In March 2020, the Center on Reinventing Public Education (CRPE) began tracking U.S. districts' plans for navigating school closures and distance learning. The database, which is available on CRPE's website (www.crpe.org), draws on publicly available plans from the 30 largest districts in the country, members of the Council of the Great City Schools, smaller districts that participate in CRPE's rural studies, and at least one district from otherwise unrepresented states. Since March, the center has been analyzing the plans and presenting the trends in a series of research papers and commentaries. "The Learning Professional" spoke with CRPE director Robin Lake about how the database took shape, what she and her colleagues have learned from it, and the implications for professional learning. The interview with Robin Lake is presented in this article.

Purpose: The study aims to examine the factors that determine the success of graduates in the labour market. This success is measured in several ways, namely, above-average wages in the economy, employment in accordance with the field of study and job satisfaction. The research explores the employment outcomes of graduates from Jagiellonian University, which is the oldest and one of the best in Poland. Not only does the analysis focus on degree-related predictors such as discipline or faculty, but it is also extended to include activities undertaken during studies and family background. Design/methodology/approach: The analysis is based on data collected in the graduates' career survey, which is conducted six months after graduation. For this article, a database of over 6,000 graduates from four subsequent editions of the survey was compiled. In order to identify the predictors of graduates' success, a logistic regression model was applied. Findings: The study reveals that graduates' success in the labour market is influenced not only by their discipline, but also by the activities undertaken during their studies, the type of studies and demographic features. Studies in natural sciences give chance for higher earnings, job matching and satisfaction. The humanities and social sciences increase only graduates' group satisfaction. The impact of activities during studies is diversified -- those matched to studies increase the job matching and those unmatched lower the earnings, job matching and satisfaction. Parents' level of education resulted in salary increase. The results also confirm the premises of behavioural theory. Research limitations/implications: Firstly, the main research limitation is that data of one particular university's graduates are used, so the results should be interpreted in the context of Poland only. Secondly, the authors propose a precise concept of success; however, it could be refined in any future study. This limitation could be mitigated by the flexibility of the model, which guarantees that other predictors of success could be involved. Furthermore, the article was based on data collected before the COVID-19 pandemic, when the labour market was completely different, so it also could be revised now. Practical implications: The results of the analysis can be used when preparing the University's educational offer. Since various predictors are implemented in the model, the information could be useful not only for future students, but also for current students, so they can make a more data-driven decision about their future careers. Additionally, University careers' advisors are provided with knowledge on the determinants of graduates' success. Taking a broader view, the research findings are also informative for labour market institutions. Originality/value: The paper offers an advanced analysis in a field that is under-researched in many transition countries, including Poland, i.e. graduates' entry into the workplace. Although the study provides insight into the factors that determine the labour market success of one particular university's graduates, this modelling can be applied to the empirical data of another higher education institution's (HEI's) graduates.

Background The National Institutes of Health (NIH) mobilized more than $4 billion in extramural funding for the COVID-19 pandemic. Assessing the research output from this effort is crucial to understanding how the scientific community leveraged federal funding and responded to this public health crisis. Methods NIH-funded COVID-19 grants awarded between January 2020 and December 2021 were identified from NIH Research Portfolio Online Reporting Tools Expenditures and Results using the "COVID-19 Response" filter. PubMed identifications of publications under these grants were collected and the NIH iCite tool was used to determine citation counts and focus (eg, clinical, animal). iCite and the NIH's LitCOVID database were used to identify publications directly related to COVID-19. Publication titles and Medical Subject Heading terms were used as inputs to a machine learning–based model built to identify common topics/themes within the publications. Results and Conclusions We evaluated 2401 grants that resulted in 14 654 publications. The majority of these papers were published in peer-reviewed journals, though 483 were published to preprint servers. In total, 2764 (19%) papers were directly related to COVID-19 and generated 252 029 citations. These papers were mostly clinically focused (62%), followed by cell/molecular (32%), and animal focused (6%). Roughly 60% of preprint publications were cell/molecular-focused, compared with 26% of nonpreprint publications. The machine learning–based model identified the top 3 research topics to be clinical trials and outcomes research (8.5% of papers), coronavirus-related heart and lung damage (7.3%), and COVID-19 transmission/epidemiology (7.2%). This study provides key insights regarding how researchers leveraged federal funding to study the COVID-19 pandemic during its initial phase. [ABSTRACT FROM AUTHOR]

Background: As evidence related to the COVID-19 pandemic surged, databases, platforms, and repositories evolved with features and functions to assist users in promptly finding the most relevant evidence. In response, research synthesis teams adopted novel searching strategies to sift through the vast amount of evidence to synthesize and disseminate the most up-to-date evidence. This paper explores the key database features that facilitated systematic searching for rapid evidence synthesis during the COVID-19 pandemic to inform knowledge management infrastructure during future global health emergencies. Methods: This paper outlines the features and functions of previously existing and newly created evidence sources routinely searched as part of the NCCMT's Rapid Evidence Service methods, including databases, platforms, and repositories. Specific functions of each evidence source were assessed as they pertain to searching in the context of a public health emergency, including the topics of indexed citations, the level of evidence of indexed citations, and specific usability features of each evidence source. Results: Thirteen evidence sources were assessed, of which four were newly created and nine were either pre-existing or adapted from previously existing resources. Evidence sources varied in topics indexed, level of evidence indexed, and specific searching functions. Conclusion: This paper offers insights into which features enabled systematic searching for the completion of rapid reviews to inform decision makers within 5–10 days. These findings provide guidance for knowledge management strategies and evidence infrastructures during future public health emergencies. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019(COVID-19) has caused widespread and significant harm globally. In order to address the urgent demand for a rapid and reliable diagnostic approach to mitigate transmission, the application of deep learning stands as a viable solution. The impracticality of many existing models is attributed to excessively large parameters, significantly limiting their utility. Additionally, the classification accuracy of the model with few parameters falls short of desirable levels. Motivated by this observation, the present study employs the lightweight network MobileNetV3 as the underlying architecture. This paper incorporates the dense block to capture intricate spatial information in images, as well as the transition layer designed to reduce the size and channel number of the feature map. Furthermore, this paper employs label smoothing loss to address the inter-class similarity effects and uses class weighting to tackle the problem of data imbalance. Additionally, this study applies the pruning technique to eliminate unnecessary structures and further reduce the number of parameters. As a result, this improved model achieves an impressive 98.71% accuracy on an openly accessible database, while utilizing only 5.94 million parameters. Compared to the previous method, this maximum improvement reaches 5.41%. Moreover, this research successfully reduces the parameter count by up to 24 times, showcasing the efficacy of our approach. This demonstrates the significant benefits in regions with limited availability of medical resources. [ABSTRACT FROM AUTHOR]

The new coronavirus SARS-CoV-2 cuases the known disease as COVID -19 extended around the world, increasing the infected and deceased cases. It was declared pandemic by OMS in March of this year. It is reason to actívate the scientific community in the production and dissemination of scientific articles. This paper analyzes and predicts scientific publications related with COVID 19, monthly and for country, proceeded and registered daily by CTS Observatory based on PubMed. USA highlights as the main country who publishes with 7,974 (27.73 %) results, followed by China with 4,202 (14.61 %). In Iberoamerican level, Spain and Brazil highlight with 4.26% y 2.37%, respectively. An increase of publications related with COVID-19 of 2,236 monthly publications is predicted in the mentioned database. [ABSTRACT FROM AUTHOR]

This paper analyses COVID-19 research papers in international collaboration by Indian researchers indexed in the web of Science core collection database through co-authorship. Indian researchers have published 290 publications out of which 128 (44.14%) were in international collaboration. The papers in international collaboration have received 63.19% of total citations as compared to 36.80% received by non-international collaborative papers. The impact of publication in international collaboration is 2.56 while non-international collaborative papers have 1.15 citations per publication. Dr D Y Patil Vidyapeeth, Pune is the most productive organization whereas Hainan Medical University China is the most preferred collaboration organizations. Among the countries, Peoples Republic of China is the most preferred country for collaboration followed by USA, Thailand and England. [ABSTRACT FROM AUTHOR]

Background: We aimed to assess the adherence to five transparency practices (data availability, code availability, protocol registration and conflicts of interest (COI), and funding disclosures) from open access Coronavirus disease 2019 (COVID-19) related articles. Methods: We searched and exported all open access COVID-19-related articles from PubMed-indexed journals in the Europe PubMed Central database published from January 2020 to June 9, 2022. With a validated and automated tool, we detected transparent practices of three paper types: research articles, randomized controlled trials (RCTs), and reviews. Basic journal- and article-related information were retrieved from the database. We used R for the descriptive analyses. Results: The total number of articles was 258,678, of which we were able to retrieve full texts of 186,157 (72%) articles from the database Over half of the papers (55.7%, n = 103,732) were research articles, 10.9% (n = 20,229) were review articles, and less than one percent (n = 1,202) were RCTs. Approximately nine-tenths of articles (in all three paper types) had a statement to disclose COI. Funding disclosure (83.9%, confidence interval (CI): 81.7–85.8 95%) and protocol registration (53.5%, 95% CI: 50.7–56.3) were more frequent in RCTs than in reviews or research articles. Reviews shared data (2.5%, 95% CI: 2.3–2.8) and code (0.4%, 95% CI: 0.4–0.5) less frequently than RCTs or research articles. Articles published in 2022 had the highest adherence to all five transparency practices. Most of the reviews (62%) and research articles (58%) adhered to two transparency practices, whereas almost half of the RCTs (47%) adhered to three practices. There were journal- and publisher-related differences in all five practices, and articles that did not adhere to transparency practices were more likely published in lowest impact journals and were less likely cited. Conclusion: While most articles were freely available and had a COI disclosure, adherence to other transparent practices was far from acceptable. A much stronger commitment to open science practices, particularly to protocol registration, data and code sharing, is needed from all stakeholders. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology to improve territorial cohesion in clustering processes where many variables from different topics are considered. Clustering techniques provide added value to identify typologies, but there are still unsolved challenges when data contain an unbalanced number of variables from different topics. The territorial feature selection method (TFSM) is presented as a method to select the representative variable of each topic such that the interpretability of resulting clusters is preserved and the geographical cohesion is improved with respect to classical approaches. This paper also introduces the thermometer as a new knowledge acquisition tool that allows experts to transfer semantics to the data mining process. TFSM proposes the index of potential explainability ( E k ) as the criteria to select the most promising variables for clustering. E k is based on the combination of inferential testing and metrics such as support. The proposal is applied with the INSESS-COVID19 database, where territorial groups of vulnerable populations were found. A set of 195 variables with 21 unbalanced thematic blocks is used to compare the results with a traditional multiview clustering analysis with promising results from both the geographical and the thematic point of view and the capacity to support further decision making. [ABSTRACT FROM AUTHOR]

Objective. Aging is a growing public health concern for people, organizations, and governments. The current study was undertaken to provide insights into the global research output on geriatric nursing. Methods. A bibliometric study was implemented using the WoS database for the period from 1900 to 2020. Various tools and measures were used to analyze and visualized. Results. The search strategy found 4923 papers. The oldest paper was written by Beverly C. Andre in 1953. As team size increases, so does the number of citations. The USA was the active country and the highest number of coauthors. New York University was an active institution. Stig Karlsson was the most active author in Geriatric Nursing with 28 articles from Sweden, followed by Koen Milisen and Sandman, with 26 articles each from Sweden and Belgium. The most frequent words in this field were depression, malnutrition, education, Alzheimer's disease, and dementia. The latest research themes in this field were COVID-19, interprofessional locomotive syndrome, emergency nursing, and public health. The most influential papers were specified. Journal of the American Geriatrics Society was the most active journal. Conclusions. Geriatric nursing is a rooted field and has received special attention in the last decade. Policymakers, especially in developing countries, should pay attention to geriatric nursing as a specialty of nursing to solve aging issues they would face considering the increasing elderly population. [ABSTRACT FROM AUTHOR]

The pandemic context has presented new challenges for education. In a short time, higher education institutions (HEIs) adapted their students, staff, technology, and infrastructures for a fast migration to distance learning. This change brought new challenges but also new opportunities that justify more contributions. The purpose of this paper is to study distance education (DE) methods and approaches, with a focus on management studies that are understudied. The research adopted a descriptive quantitative approach. Based on a sample of 400 documents using the Scopus database, a bibliometric analysis was carried out, aiming to identify the most prominent keywords, authors, sources, and countries for DE in general, with a focus on management courses in HEIs. The results suggest that innovation can be an asset for HEIs and can be seen as an ally for both teachers and students in learning new practices that involve digitalization. The bibliometric analysis highlights that motivation and collaboration are very important aspects and should be considered, especially when students are at the center of the teaching process. The main limitation regards the dominance of papers about DE, mostly based on the education sciences, and just a small number focusing on management courses. This limitation can influence the results achieved, but also allows this research to contribute for a better understanding of this field. This is one of few studies that has innovated by identifying the main subjects in DE literature, with a special emphasis on management courses, thus complementing previous scarce research in the field. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to review the role of green training and green work life balance (GWLB) on sustainable organizational performance (SOP) with a moderating variable "Emotional Intelligence" (EI). Design/methodology/approach: For the development of the construct of the present study, a Scopus database was selected and research papers published in indexed journals were considered. Relevant keywords were selected and literature was searched on green training, EI, SOP, GWLB. The literature was reviewed to find out the linkage and possibility of development of integrated model. The main focus was on highlighting the relevance of green training on GWLB and its influence on SOP. Findings: SOP can be achieved with the intervention of EI and GWLB; further green training is one of the influential practices of human resource development (HRD) which helps to develop the green behavior. Research limitations/implications: It can give new insight to the organization for application of green human resource practices for SOP. Development and designing the cohesive environmental work culture and willingness to protect environment through green training can be implemented by HRD. Perhaps, the application of green training encourages GWLB. Practical implications: Quantitative research and cross sectional study is required to find out the intervening role of EI and work–life balance between green training and SOP across a broader range of sectors. Originality/value: This research extends the literature review and developed a new integrated model which shows the link between green training and SOP. [ABSTRACT FROM AUTHOR]

Recognizing patient activity in real‐time from video or images collected by a CCTV camera available in the hospital during a Covid‐19 situation has proven challenging. The dilemma of patient activity recognition is identifying and recognizing a patient's various actions in a series of videos. The process presented in our paper needs to achieve unrestricted, generic behavior in videos. Detecting events in any video is often difficult because we use Bidirectional ConvLSTM to create a robust patient in the sense behaviors (PSB) framework capable of eliminating certain barriers. To begin this paper by proposing a new Bidirectional ConvLSTM for establishing a stable PSB scheme. Our proposed model is capable of accurately predicting patient's behaviors like seated, standing, and so on. Using Bidirectional ConvLSTM, learning information from a pre‐trained model is an excellent place to start for rapidly developing a new PSB system using a current PSB database, as both the source and target datasets are critical. All parameters are frozen in a pre‐trained PSB device. Then, using the UCI and HMDB51 dataset to train the model, variables and local relations are progressively fixed. A novel PSB framework is developed using the target dataset. Relevant tests are conducted using commonly used research indices to assess prediction precision accuracy. They acknowledge six patient's behavior with a weighted accuracy rate of 92%. For recognizing novel activity, laying, the precision of a corresponding prediction is the best, 91%, of all six test results. The proposed work uses bidirectional ConvLSTM with modified activation layers to sense the patients' behavior. This article may be a patient activity recognition system to identify an individual. It takes a clip of COVID‐19 patients as input and looks for matches inside the hold‐on images. [ABSTRACT FROM AUTHOR]

SARS-COV2 or the coronavirus disease even after two years of identification is still a problem because of its varying mutations. The prediction and classification of the virus are one of the most important tasks for controlling the spread of the pandemic. So immediate development of some strong techniques with artificial intelligence implementation to predict COVID-19 is required. The paper's main aim is to develop an accurate, efficient, and time-saving model for detecting COVID-19 from Chest X-Ray (CXR) images. This study utilises a dataset from the Kaggle database containing CXR images of COVID-19, Viral Pneumonia, and Normal Healthy lung images. Here, some Deep Learning (DL) models like GoogleNet, SqueezeNet, and ResNet-18 are utilised for the detection of the virus. The overall accuracy achieved from the models like GoogleNet is 92.46%, for SqueezeNet is 92.57%, and ResNet-18 is 97.38%. From the obtained outcomes it is concluded that ResNet-18 provides accurate results as compared with the other three models. The achieved outcomes could help medical experts in the future to predict and classify COVID-19 and can very quickly diagnose cases with effective models. [ABSTRACT FROM AUTHOR]

South Africa shared with the world the warning of a new strain of SARS-CoV2, Omicron, in November 2021. As a result, many high-income countries (HICs) instituted complete travel bans on persons leaving South Africa and other neighbouring countries. These bans were unnecessary from a scientific standpoint, and they ran counter to the International Health Regulations. In short, South Africa was penalized for sharing data. Data sharing during pandemics is commonly justified by appeals to solidarity. In this paper, we argue that solidarity is, at best, an aspirational ideal to work toward but that it cannot ground an obligation to share data. Instead, low-and-middle income countries (LIMCs) should be guided by the principle of reciprocity, which states that we ought to return good for good received. Reciprocity is necessarily a conditional principle. LMICs, we argue, should only share data during future pandemics on the condition that HICs provide enforceable assurances that the benefits of data sharing will be equitably distributed and that LMICs won't be penalized for sharing information. [ABSTRACT FROM AUTHOR]