Revisiting online academic networks within the COVID-19 pandemic – From the intellectual capital of knowledge networks towards institutional knowledge capitalization

Purpose: The study sets out to explore the mediating role of intellectual capital (IC) dimensions (i.e. human, structural and relational) between scholars' affiliation to online academic networks and institutional knowledge capitalization. Online academic networks are tackled through the lens of knowledge networks which have been of primary importance for new relevant knowledge acquisition during the COVID-19 pandemic. Design/methodology/approach: A questionnaire-based survey of 305 academics from 35 different countries was conducted from July to December 2021, employing a partial least squares structural equation modelling technique. The database was initially filtered to ensure the adequacy of the sample, and data were analyzed using the statistics software package SmartPLS 3.0. Findings: Evidence was brought forward that the proposed conceptual model accounted for 52.5% of the variance in institutional knowledge capitalization, the structural and relational capital availed by knowledge networks exerting strong positive influence on the dependent variable. Research limitations/implications: The study has both research and managerial implications in that it approaches a topical phenomenon, namely the capitalization of online academic networks in the COVID-19 context, which has dramatically altered the way that research and teaching are conducted worldwide. Originality/value: The most important contribution of the paper resides in the comprehensive research model advanced which covers individual, organizational and network multifaced layers, starting with the personal and institutional motives to join a specialized network, continuing with the opportunities provided by knowledge networks in terms of intellectual capital harnessing, and ending with its influence on higher education organizations. [ FROM AUTHOR] Copyright of Journal of Intellectual Capital is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Revisiting online academic networks within the COVID-19 pandemic – From the intellectual capital of knowledge networks towards institutional knowledge capitalization

Purpose The study sets out to explore the mediating role of intellectual capital (IC) dimensions (i.e. human, structural and relational) between scholars' affiliation to online academic networks and institutional knowledge capitalization. Online academic networks are tackled through the lens of knowledge networks which have been of primary importance for new relevant knowledge acquisition during the COVID-19 pandemic. Design/methodology/approach A questionnaire-based survey of 305 academics from 35 different countries was conducted from July to December 2021, employing a partial least squares structural equation modelling technique. The database was initially filtered to ensure the adequacy of the sample, and data were analyzed using the statistics software package SmartPLS 3.0. Findings Evidence was brought forward that the proposed conceptual model accounted for 52.5% of the variance in institutional knowledge capitalization, the structural and relational capital availed by knowledge networks exerting strong positive influence on the dependent variable. Research limitations/implications The study has both research and managerial implications in that it approaches a topical phenomenon, namely the capitalization of online academic networks in the COVID-19 context, which has dramatically altered the way that research and teaching are conducted worldwide. Originality/value The most important contribution of the paper resides in the comprehensive research model advanced which covers individual, organizational and network multifaced layers, starting with the personal and institutional motives to join a specialized network, continuing with the opportunities provided by knowledge networks in terms of intellectual capital harnessing, and ending with its influence on higher education organizations.

KBART Phase III: Unresolved questions

During the “NISO update” session at the NISO Plus 2021 conference, which took place online due to the COVID-19 pandemic, members of the KBART (Knowledge Base and Related Tools) Standing Committee presented their plans and work toward KBART Phase III, a revision of the KBART Recommended Practice. In an interactive breakout session, they sought input from attendees on how KBART is being used and what new content types it should support. Presenters from the KBART Standing Committee were Noah Levin (Independent Professional), Stephanie Doellinger (OCLC, Inc.), Robert Heaton (Utah State University), and Andrée Rathemacher (University of Rhode Island). Assisting them in preparing the presentation were Jason Friedman (Canadian Research Knowledge Network), Sheri Meares (EBSCO Information Services), Benjamin Johnson (ProQuest), Elif Eryilmaz-Sigwarth (Springer Nature), and Nettie Lagace (NISO). © 2021 - The authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC 4.0).

2AI&7D Model of Resistomics to Counter the Accelerating Antibiotic Resistance and the Medical Climate Crisis

The antimicrobial resistance (AMR) crisis is referred to as ‘Medical Climate Crisis’. Inappropriate use of antimicrobial drugs is driving the resistance evolution in pathogenic microorganisms. In 2014 it was estimated that by 2050 more people will die due to antimicrobial resistance compared to cancer. It will cause a reduction of 2% to 3.5% in Gross Domestic Product (GDP) and cost the world up to 100 trillion USD. The indiscriminate use of antibiotics for COVID-19 patients has accelerated the resistance rate. COVID-19 reduced the window of opportunity for the fight against AMR. This man-made crisis can only be averted through accurate actionable antibiotic knowledge, usage, and a knowledge driven Resistomics. In this paper, we present the 2AI (Artificial Intelligence and Augmented Intelligence) and 7D (right Diagnosis, right Disease-causing-agent, right Drug, right Dose, right Duration, right Documentation, and De-escalation) model of antibiotic stewardship. The resistance related integrated knowledge of resistomics is stored as a knowledge graph in a Neo4j properties graph database for 24 × 7 access. This actionable knowledge is made available through smartphones and the Web as a Progressive Web Applications (PWA). The 2AI&7D Model delivers the right knowledge at the right time to the specialists and non-specialist alike at the point-of-action (Stewardship committee, Smart Clinic, and Smart Hospital) and then delivers the actionable accurate knowledge to the healthcare provider at the point-of-care in realtime. © 2021, Springer Nature Switzerland AG.

Counterfactual inference to predict causal knowledge graph for relational transfer learning by assimilating expert knowledge --Relational feature transfer learning algorithm

Transfer learning (TL) is a machine learning (ML) method in which knowledge is transferred from the existing models of related problems to the model for solving the problem at hand. Relational TL enables the ML models to transfer the relationship networks from one domain to another. However, it has two critical issues. One is determining the proper way of extracting and expressing relationships among data features in the source domain such that the relationships can be transferred to the target domain. The other is how to do the transfer procedure. Knowledge graphs (KGs) are knowledge bases that use data and logic to graph-structured information;they are helpful tools for dealing with the first issue. The proposed relational feature transfer learning algorithm (RF-TL) embodies an extended structural equation modelling (SEM) as a method for constructing KGs. Additionally, in fields such as medicine, economics, and law related to people’s lives and property safety and security, the knowledge of domain experts is a gold standard. This paper introduces the causal analysis and counterfactual inference in the TL domain that directs the transfer procedure. Different from traditional feature-based TL algorithms like transfer component analysis (TCA) and CORelation Alignment (CORAL), RF-TL not only considers relations between feature items but also utilizes causality knowledge, enabling it to perform well in practical cases. The algorithm was tested on two different healthcare-related datasets — sleep apnea questionnaire study data and COVID-19 case data on ICU admission — and compared its performance with TCA and CORAL. The experimental results show that RF-TL can generate better transferred models that give more accurate predictions with fewer input features.

Recommendations for coastal planning and beach management in Caribbean insular states during and after the COVID-19 pandemic.

The COVID-19 pandemic has implications for coastal planning and management. Rules for isolation and physical distancing, among other measures for human life protection, have led to the closure of most beaches around the world. The present critical situation has raised the following question: How can some recommendations be designed in sun, sea, and sand tourism-dependent-insular countries to face "the COVID-19 new normality?" We used the content analysis technique to analyze representative publications on a global level to ascertain information on best management practices. A survey of 58 experts provided additional information. We used inferential statistics for sample selection and produced a list of 43 practices and beach planning and management actions to face the COVID-19 pandemic. This led to 27 new recommendations designed for beach planning and management within insular contexts, some of which were tested in the Republic of Cuba. Recommendations aim to guarantee a culture of safety and improvement within the field of beach or coastal planning and management. These recommendations should prove useful for other insular countries, during the COVID-19 period, in the new normality that follows, and in other post-pandemic scenarios.

Nimble, efficient and evolving: the rapid response of the National Collaborating Centres to COVID-19 in Canada. / Agiles, efficients et en évolution : la réponse rapide des Centres de collaboration nationale à la COVID-19 au Canada.

Since December 2019, there has been a global explosion of research on COVID-19. In Canada, the six National Collaborating Centres (NCCs) for Public Health form one of the central pillars supporting evidence-informed decision making by gathering, synthesizing and translating emerging findings. Funded by the Public Health Agency of Canada and located across Canada, the six NCCs promote and support the use of scientific research and other knowledges to strengthen public health practice, programs and policies. This paper offers an overview of the NCCs as an example of public health knowledge mobilization in Canada and showcases the NCCs' contribution to the COVID-19 response while reflecting on the numerous challenges encountered.

The explosion of research on COVID-19 in Canada and around the world called for an improved capacity to support evidence-informed decision making (EIDM). Canada is fostering various mechanisms to achieve this goal; the National Collaborating Centres (NCCs) for Public Health are central to supporting EIDM during the pandemic. The NCCs, a network of networks anchored on six unique knowledge hubs, are well connected to provincial, territorial, local and international partners. In response to COVID-19, the NCCs are making an important contribution to building knowledge, skills and capacity in the public health sector, and to supporting public health professionals in synthesizing and using evidence-informed knowledge in policy and practice.

L'explosion de la recherche menée sur la COVID-19 au Canada et ailleurs dans le monde a nécessité une augmentation de la capacité à soutenir la prise de décisions informées par les données probantes. Parmi les divers mécanismes préconisés par le Canada pour atteindre cet objectif, les Centres de collaboration nationale (CCN) en santé publique jouent un rôle essentiel pendant la pandémie pour soutenir la prise de décisions informées par les données probantes. Les CCN, qui constituent un réseau de réseaux s'appuyant sur six centres de connaissances, ont des liens étroits avec plusieurs partenaires provinciaux, territoriaux, locaux et internationaux. Pour lutter contre la COVID-19, les CCN renforcent de façon significative les connaissances, les compétences et les capacités en santé publique et soutiennent les professionnels en santé publique en synthétisant des connaissances fondées sur des données probantes pour leur intégration aux politiques et aux pratiques.