Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classification. In this context, the B Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (h[p://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to 6 users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others.

Analytical Procedure Development and Proposed Established Conditions: A Case Study of a Mass Spectrometry Based NDSRI Analytical Procedure.

With the finalization of the ICH Q14 Analytical Procedure Development guideline, how to apply enhanced approaches (such as analytical quality by design (AQbD)) to develop an analytical procedure, and to propose Established Conditions (ECs) and corresponding reporting categories, is increasingly being discussed. To gain practical experience in applying an enhanced approach for method development and identifying ECs, we developed, validated, and implemented an analytical procedure for a nitrosamine drug substance-related impurity (NDSRI). Here, as an example of the application of Q12 Lifecycle Management guideline principles in regards to analytical procedures, we briefly elaborate how: 1) the principles documented in the ICH Q14 guideline for analytical procedure development were applied, with the focus on identifying an Analytical Target Profile (ATP), knowledge management and risk assessment; 2) analytical procedure robustness according to the recommendations in ICH Q2(R2) Validation of Analytical Procedure guideline and Q14, were evaluated; and 3) mass spectrometry ECs and associated proposed reporting categories were proposed.

Scanning resources to build an international nursing knowledge network.

This paper reports the gathered information from an international environmental scan of university-baseline information for the creation of a Nursing Knowledge Network. An online environment scan of organizational context (February to October 2021) explored the identification of research areas, existing resources, expected benefits, innovations in teaching research, barriers to knowledge dissemination, and prospective contributions of the Network. Target informants were 200 nursing faculty affiliated with 63 universities located in 13 countries, as well as nursing networks in the Ibero-American context. One informant per university was asked to respond to the questionnaire. The participation rate was nearly 70% (42/63). The informants' universities per country included Brazil (n = 21), Canada (n = 4), Portugal and Spain (n = 3 each), Colombia, Mexico, Peru and USA (n = 2 each), Chile, Italy and Paraguay (n = 1 each). Nursing faculty provided rich information and shared knowledge confirming a strong commitment to global co-creation of innovations and research partnership capacities through collaboration, cooperation, and knowledge exchange among nursing higher education institutions. Seldom researched areas are a potential focus for the Network to generate appropriate evidence to inform local scientific practices. The gathered information will inform further review of nursing and governmental policies and programs related to the application and dissemination of nursing evidence across local, regional, and global levels.

Integrated Perspectives on Clinical Decision Support: A Comparative Analysis of Knowledge Management Approaches.

We analyze five approaches to knowledge management in clinical decision support (CDS) systems: pattern recognition based on annotated imaging data, mining of stored structured medical data, text mining of published texts, computable knowledge design, and general or specific text corpora for large language models. Each method's strengths and limitations in automating clinical knowledge management while striving for a zero-error policy are evaluated, offering insights into their roles in enhancing healthcare through intelligent decision support. The study aims to inform decisions in the development of effective, transparent CDS systems in clinical and patient care settings.

Sustainability risk management: Exploring the role of artificial intelligence capabilities through an information-processing lens.

The global sustainability movement is reshaping the operational requirements and managerial approaches of maritime firms, resulting in the emergence of unprecedented and complex risks in the sector. This has driven maritime firms to leverage digital tools, such as artificial intelligence (AI) capabilities, to enhance their sustainability risk management (SRM) endeavors. Drawing on the organizational information-processing theory (OIPT), this study proposes four AI capabilities: customer value proposition, key process optimization, key resource optimization, and societal good. It examines their influence on sustainability-related knowledge management capabilities (SKMC), stakeholder engagement, and SRM. A survey questionnaire was used to gather responses from 157 maritime professionals across various sectors of the industry, providing empirical data for analysis. Employing structural equation modeling, the findings reveal that AI capabilities can improve SKMC. These findings enhance existing literature by using OIPT concepts to investigate the interplay among the constructs that lead to better SRM in maritime firms. Furthermore, the study offers managerial guidance by providing insights into AI capabilities that maritime firms should incorporate into their operations, fostering best practices to effectively manage sustainability risks and ensure the firm's long-term survival.

Proposal of a Knowledge Management Model for Complex Systems: Case of the Supervision and Control Subsystem of the Colombian Health System.

BACKGROUND: Considering regulatory, supervision, and control health policy, an innovative knowledge management model is proposed for the Colombian health system, which is recognized as a complex system. METHODS: A model is constructed through a comparative analysis of various theoretical and conceptual frameworks, and an original methodology is proposed based on an analysis of the macroprocesses of the Supervision and Control System (SSC) of the Colombian General Social Security System in Health (SGSSS). After formulating hypotheses and conceptual references, information errors are determined within the different macroprocesses of the SGSSS, including those of governance and the SSC. RESULTS: The risks of generating duplicate, wrong, hidden, or non-existent information arise when the associated regulations need more specificity to be applied in all cases, thus leading to the risk of different interpretations by some actors. In this way, it is possible to hinder the generation of unified information, as there is no clarity as to who is responsible for the generation or creation of certain data. CONCLUSIONS: The proposed model is characterized by its flexibility and adaptability, integrating several processes that can be executed simultaneously or cyclically (depending on the system's needs) and allowing for the generation and feedback of knowledge at different stages, with some processes simultaneously executed to complement each other.

COVID-19 Pandemic: From Population Need to Professional Nursing Competence.

Nursing has proven to be an essential healthcare profession, especially in the face of the COVID-19 pandemic crisis. In this chapter, it shows the essential aspects of the discipline of care and its application in the face of the pandemic from an Informatics Nursing approach. The conceptual bases include the conception of care and its historical evolution. Thus, the Personal Care Knowledge Model, the clinical care sequence and its standardized languages allow Taxonomic Triangulation to be developed. Taxonomic Triangulation is a technique created by nurses that allows managing information and that served to extract knowledge from documents and clinical experiences. The application of this vision of care and its knowledge management models have been tested in different situations: from the identification of care diagnoses in a World Health Organization clinical guide to the design of a care plan manual in a hospital. On the other hand, a secondary result is the resilience shown by the nurses. A resilience based on theoretical models centered on the person and on a language that can represent life from care. In addition, nursing includes a comprehensive perspective that addresses the emotional and spiritual area. In conclusion, nurses and their specialization with skills in knowledge management allow giving visibility to care. A professional care whose purpose is to improve health systems through solutions based on care so that people can achieve their best health situation.

Negative impact of nurses' fear of COVID-19: the moderating role of implementation of knowledge management.

Background: During the COVID-19 pandemic, nurses encountered substantial infection risks and psychological strain, which severely affected their emotional well-being, professional attitudes, and job performance. This study investigated the impact of nurses' fear of COVID-19 on their intention to leave the occupation and emotional labor as well as the moderating role of the implementation of knowledge management on these primary variables. Methods: To mitigate common method bias, this research adopted a two-phase questionnaire approach, targeting nurses at a medical center in central Taiwan. In the first phase, 300 copies of questionnaire were distributed for participants to complete self-assessment surveys covering fear of COVID-19, knowledge management implementation, and demographic information. After 1 month, the participants were invited to complete a follow-up questionnaire, focusing on the intention to leave the occupation and emotional labor. The questionnaire was conducted from June to July 2022. Through this two-phase distribution method, after exclusion of invalid responses, a total of 288 valid responses were collected, resulting in a response rate of 96%. The proposed hypotheses were verified using hierarchical regression conducted with SPSS version 25.0. Results: The findings indicated that nurses' fear of COVID-19 was significantly and positively associated with their intention to leave the occupation and surface acting, but negatively associated with their deep acting. Moreover, the implementation of knowledge management significantly moderated the positive relationship among fear of COVID-19, intention to leave the occupation, and surface acting. A robust knowledge management system weakened the positive association among fear of COVID-19, intention to leave the occupation, and surface acting. Conclusion: In summary, nurses' fear of COVID-19 may increase their tendency to leave the nursing profession and engage in more surface acting and less deep acting. However, effective knowledge management practices can mitigate these adverse effects. Hospitals can thus establish and employ comprehensive knowledge management systems to enhance nurses' resilience and help alleviate their fear of future pandemics and their potential negative repercussions.

The Procedures for Documenting Organizational Knowledge and Experiences: A Scoping Review.

Background: The present study was motivated by issues with earlier studies on documenting knowledge and experiences. This scoping review investigates and maps the procedures for documenting organizational knowledge and experiences. Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for Scoping Reviews (PRISMA-ScR) guidelines, a scoping review was conducted. Data were obtained by searching PubMed, Web of Science, Scopus, ProQuest, Embase, and Emerald Insight databases and Persian databases, such as Magiran, Noormags, and Ensani. The selected terms were searched using the Boolean AND/OR operators, phrases, parentheses, and truncations in the title, abstract, keywords, and text word fields. The inclusion criteria were resources relevant to the research question, studies in English and Persian, original research articles, and resources published between 2011 and 2022. Finally, 8 related papers were selected as the research population after screening records. Results: The review of the selected studies indicates that there have been different steps for documenting knowledge and experiences according to the subject's scope and the goals of the studies. The included articles revealed numerous steps for documentation-including planning, acquisition, registration, evaluation, submission, maintenance, publication, application, payment, and compensation. Conclusion: Although a systematic mechanism for documenting knowledge and experience is essential, many processes and phases are offered for documentation. Therefore, a complete review that synthesizes and integrates past study findings must still be included. Several shortcomings in past research on documenting knowledge and expertise prompted the present study. The results of the present study can be of great use to managers and employees of various organizations in topics such as the creation of standards for documenting knowledge and experiences, organizational-structural planning in this field, and training on different documentation methods.

Perception of Knowledge Transfer from Clinical Simulations to the Care Practice in Nursing Students.

Objective: This work sought to assess the perception of knowledge transfer from clinical simulations to the care practice in nursing students through effective debriefing. Methods: An observational, descriptive, and cross-sectional study was conducted with a sample of 281 students during the 2020-2021 course, through una ad hoc survey from the Debriefing Assessment for Simulation in Healthcare (DASH) in Spanish, to assess competence areas that undergraduate students must reach to complete their studies. Results: The survey conducted after each simulation showed that the students valued positively the debriefing sessions conducted by experts, with a mean score of 6.61 over 7 [6.56%-6.65%] based on 675 surveys analyzed, given that each student conducted more than one simulation within the academic course. It was observed in 221 completed answers that what was learned in the simulation was transferred to the practice in 89.23% [86.39%-92.06%], specifically in areas of Communication, Patient safety, Teamwork, and Leadership. Conclusion: In the perception by the participating students, the use of effective debriefing in clinical simulation enabled knowledge transfer to the care practice, proving to be a crucial tool that helps to improve the formation of the future nurses.

Knowledge infrastructure for integrated data management and analysis supporting new approach methods in predictive toxicology and risk assessment.

The EU-ToxRisk project (2016-2021) was a large European project working towards shifting toxicological testing away from animal tests, towards a toxicological assessment based on comprehensive mechanistic understanding of cause-consequence relationships of chemical adverse effects. More than 40 partners from scientific institutions, industry and regulators coordinated their work towards this goal in a six-year long programme. The breadth and variety of data and knowledge generated, presented a challenging data management landscape. Here, we describe our approach to data management as developed under EU-ToxRisk. The main building blocks of the data infrastructure are: 1) An easy-to-use, extensible data and metadata format; 2) A flexible system with protocols for data capture and sharing from the entire consortium; 3) A methods database for describing and reviewing data generation and processing protocols; 4) Data archiving using a sustainable resource; 5) Data transformation from the archive to the system that provides granular access; 6) Application Programming Interface (API) for access to individual data points; 7) Data exploration and analysis modules, based on a «web notebook¼ approach to executable data processing documentation; and 8) Knowledge portal that ties together all of the above and provides a collaboration space for information exchange across the consortium. This knowledge infrastructure is being extended and refined for the support of follow-up projects (RISK-HUNT3R, ASPIS cluster, European Open Science Cloud (2021-2026)).

An innovative referral system: Bridging healthcare providers and community supports for pregnant and parenting people.

The connection between community healthcare providers and available community-based programs is often weak. For community members to be connected with services, a strong referral link between providers and programs is paramount. The Referral Express and Central Hub (REACH) project was born out of this need for Nurse-Family Partnership (NFP) program sites in the Denver Metro area of Colorado to be better connected and coordinated with local referral sources. The objective of the REACH pilot was to create a centralized referral system and determine its feasibility in practice. After a 12-month pilot, this innovative referral system supported the increase in a number of referral partners to Denver Metro area sites, an increase in clients served across the Denver Metro area, as well as an increase in the "refer to enroll" rate which increased by 37%. The success of the pilot of REACH led the developers to test the process on a second program, Child First, which was also successful. REACH has proved to be sustainable and continues to operate for both programs. Additionally, REACH acts as a consultant to NFP sites outside the Denver Metro area, in the state of Colorado, as each site works to increase its relationships with referral partners.

A Case Study in Application of the Risk Knowledge Infinity Cycle.

The Risk Knowledge Infinity (RKI) Cycle Framework was featured as part of the ICH-sanctioned training materials supporting the recent issuance of ICH Q9(R1) Quality Risk Management To support ICH Q9(R1) understanding and adoption, this paper presents a case study on the application of the RKI Cycle, based on an underlying out-of-specification investigation. This case study provides a stepwise walk-through of the cycle to illustrate how key concepts within the ICH Q9(R1) revision can be achieved through better connecting quality risk management and knowledge management with a framework such as the RKI Cycle.

A theory based study on diabetes self-management in older Chinese adults.

Approximately 1 in 3 (or 80 million) Chinese age 60 years or older are living with type 2 diabetes in China. New perspectives are needed to understand the intricate phenomenon of diabetes self-management (DSM) in older Chinese adults. Guided by the expanded Tripartite Model of Self-Management, this study aimed to identify the inter-relationships between the tripartite components simultaneously and their influencing factors. This cross-sectional study included a stratified random sample of 98 community-dwelling adults age 60 or older with type 2 diabetes. Findings revealed distinct predictors for knowledge about DSM, DSM behaviors and coping. There were significant inter-relationships among the tripartite components. The theoretical model was a good fit for the data. This study provides valuable insights into the complex relationships between knowledge about DSM, DSM behaviors, and coping strategies, offering direct implications for improving the health outcomes of older adults with diabetes.

The knowledge and holistic management indicators to measure the sustainability of area-based infrastructure project (AIP).

For determining the effectiveness of area-based infrastructure management, a comprehensive measurement to implement and develop an infrastructure project would need to be integrated in holistic and knowledge management. The main objective of this study was to identify the keys to measurement the sustainability of area-based infrastructure project (AIP) management. First, the key performance indicators were reviewed and selected. Next, the interviews with project developers and managers of the pilot site were conducted to investigate the real context of significant keys. The discussion with five experts who have relevant experience in area-based infrastructure projects management then arose to validate possible key performance indicators. Based on the expert comments, the questionnaire was revised for its validity and clarity. Then, the data collection using a questionnaire was sent out to representative samples across Thailand. Later, there was the analysis to interpret the survey results. The results revealed that there are two groups of keys to measure the sustainability of AIP management. One was the "Holistic Management (HM)", which consisted of seventeen components. The other was the "Knowledge Management (KM)", which involved six similarity items. It was found that the aforementioned factors can explain 70.024% of the total cumulative variance in the entire datasets compiled for the assessment of keys to measurement the sustainability of AIP management. The main outcomes from this study can be beneficial for the development of effective and good governance strategies for achieving sustainability patterns of area-based infrastructure project management. This theory offers the understanding of how the effectiveness of key measurement of AIP management can drive sustainable development project s, which can help in project manager and developer research on identity-related transitions.

Health and social care managers' self-assessed competence in knowledge management: A descriptive cross-sectional study.

AIM: To describe health and social care managers' self-assessed competence in knowledge management and the factors associated with management competence. BACKGROUND: It has been shown that the performance of an organization is as good as the competence of its managers, so health and social care managers' competence in knowledge management should be assessed to improve organizational performance. DESGIN: A descriptive cross-sectional design. METHODS: A total of 116 managers participated from six Finnish public health and social care organizations. The data were collected in February and August 2022 using the managers' competence in knowledge management (MCKM) instrument and analysed using descriptive statistical methods. RESULTS: Health and social care managers rated their self-reported total competence in knowledge management as good. Among the dimensions of knowledge management competence, managing a culture of competence received the highest rating, while planning competence development and cooperation was perceived as the weakest dimension. The results indicate that background factors such as the healthcare setting, the number of units managed and the number of direct staff had a statistically significant association with the health and social care managers' self-assessed competence in planning competence development and cooperation. CONCLUSION: Even though the health and social care managers' total self-assessed competence level in knowledge management was rated as good, the results underscore the significance of continuous competence development among these managers in all dimensions of knowledge management. IMPACT: By enhancing and clarifying managers' tasks and competence in knowledge management, managers can increase staff retention, attractiveness and work well-being. IMPLICATIONS: The results can be utilized to identify managers' strengths and weaknesses in knowledge management and, thus, effectively target their limited competence development resources. REPORTING METHOD: The STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) checklist. PATIENT OR PUBLIC CONTRIBUTION: There is no patient or public contribution.

Factors influencing knowledge sharing between scientific specialists in knowledge networks and communities of practice: A systematic literature review.

Knowledge sharing is a competitive advantage and necessity for the success of any organization. Meanwhile, knowledge networks have been introduced as a way to enhance knowledge sharing between individuals and as an effective tool to facilitate knowledge exchange in clinical, educational, and commercial fields. The purpose of this paper is to identify the factors that can affect the level of knowledge sharing and exchange between academic and scientific specialists in knowledge networks and Communities of Practice (COP). A systematic literature review was conducted using the PRISMA guidelines. Four databases were searched, including Scopus, Web of Science, PubMed, and ProQuest. Google Scholar search was conducted to complete the search and ensure the tracking of the gray literature. Also, relevant sources, references, and reference lists of the related articles were reviewed. The studies were searched from April until August 2022 and finally the content analysis of the findings was done. Two reviewers independently assessed the quality of included studies. Data were extracted using the Joanna Briggs Institute (JBI) checklist tool. Of the 1439 records, 13 studies met the inclusion criteria. This study identified three main categories of factors affecting knowledge sharing in knowledge networks and COPs as individual factors, organizational, and structural. The results showed that knowledge networks provide opportunities to overcome professional barriers and complex systemic challenges and lead to knowledge sharing and exchange among scientific specialists. This article has important implications for managers, health policymakers, and academics who wish to expand knowledge sharing of scientific specialists through knowledge networks and CoPs in knowledge-based organizations.

Artificial Intelligence Algorithms for Expert Identification in Medical Domains: A Scoping Review.

With abundant information and interconnectedness among people, identifying knowledgeable individuals in specific domains has become crucial for organizations. Artificial intelligence (AI) algorithms have been employed to evaluate the knowledge and locate experts in specific areas, alleviating the manual burden of expert profiling and identification. However, there is a limited body of research exploring the application of AI algorithms for expert finding in the medical and biomedical fields. This study aims to conduct a scoping review of existing literature on utilizing AI algorithms for expert identification in medical domains. We systematically searched five platforms using a customized search string, and 21 studies were identified through other sources. The search spanned studies up to 2023, and study eligibility and selection adhered to the PRISMA 2020 statement. A total of 571 studies were assessed from the search. Out of these, we included six studies conducted between 2014 and 2020 that met our review criteria. Four studies used a machine learning algorithm as their model, while two utilized natural language processing. One study combined both approaches. All six studies demonstrated significant success in expert retrieval compared to baseline algorithms, as measured by various scoring metrics. AI enhances expert finding accuracy and effectiveness. However, more work is needed in intelligent medical expert retrieval.