PURPOSE: To identify the nursing care problems related to the clinical process of disease by COVID-19. METHOD: The study applied the taxonomic triangulation technique on a clinical management guide to coronavirus disease, COVID-19, from the World Health Organization. The technique is divided into the phases: extraction of knowledge in natural language about assessment, planning and intervention, translation into standard language NOC and NIC, linking to NANDA-I diagnoses, triangulation looking for diagnostic matches in the three sets, and, finally, validation by a panel of experts from a hospital and a university. FINDINGS: The extraction identified 159 terms in natural language that were translated into 173 variables: 34 NOC for assessment, 19 NOC for planning, and 120 NIC for intervention. The relationships to NANDA-I diagnoses recorded 2,182 links and the triangulation returned 109 diagnoses, 54 of them for a critical situation. The panel of experts unanimously validated the 29 diagnoses with the highest number of links. CONCLUSION: Coronavirus disease, COVID-19, involves a complex situation with multiple associated care problems that can be identified using the taxonomic triangulation technique. IMPLICATIONS FOR NURSING PRACTICE: The links between taxonomies and the taxonomic triangulation technique are an important tool for generating knowledge. The results of this study may guide the diagnosis and treatment of coronavirus disease, COVID-19, as well as similar processes that occur with acute respiratory distress syndrome.

Aim: The aim of this study is to determine the validity and reliability of the Care Vulnerability Index (CVI) as a tool to estimate the need and competence of care. Design: A cross-sectional survey including a longitudinal component. Methods: Content validity ratio (CVR) was calculated by interrater agreement of a group of 11 experts in two rounds. The test-retest analysis was measured in an urban population of Colombia with 96 participants through two statistical tests: Pearson's correlation coefficient and the difference in means. Results: Care Vulnerability Index turned out to be valid with a CVR of 0.879. Reliability by Pearson correlation between test-retest was 0.912 (CI95: 0.872-0.941; p-value <.01) and there was no significant mean difference between test and retest in global score and in clustered groups of variables. Validating CVI will make it possible to prioritize healthcare resources in the population and identify people susceptible to care problems.

Background: The COVID-19 pandemic made people face the fact that we are all vulnerable. This vulnerability can be measured through the Basic Variables of Care (BVC) using the Care Vulnerability Index (CVI). Health systems work with indicators that nurses can consult to understand the care and health situation of their population. These indicators provide valuable information on the vulnerability of the population. Objective: to determine the level of community vulnerability of a population group using health indicators from the computerized clinical records of Primary Care. Methods: observational, retrospective study from March 2023 to January 2024, with a sample of 2106 people assigned to a nurse at the Meco Health Centre (Madrid, Spain). Phases: selection of dashboard indicators, linkage to BVC, score assignment, population adjustment and calculation of the CVI. Results: The sample of indicators selects 18 out of 376 included in the Specific Dashboard; each indicator is related to 6 to 10 BCVs, with different rank values. Each score is adjusted by a Vulnerability Correction Factor according to the number of people included in the indicator. Finally, the population-adjusted CVI scores 1.95 points (percentile 37.90). Conclusions: community vulnerability is an essential tool in community health diagnostics and can be measured through health indicators that reflect the care situation of a population group at a given time, including changes in the situation in the face of health crises such as the COVID-19 pandemic.

Background: Knowledge management in global health is essential in response to aging populations, increasing morbidity, and rising expectations of care. The Knowledge Model about Person Care promotes health systems organized around individuals rather than diseases. Within this framework, vulnerability—understood as the risk of physical or moral harm—can be assessed through basic care variables (BCV) that determine individuals’ need and capacity for self-care. Primary care health information systems provide an opportunity to operationalize these variables at the population level. Methods: This study applies deductive methodology to extrapolate community-level health indicator data to population-level vulnerability measures. Using the electronic primary care objective monitoring tool (e-SOAP) from the Community of Madrid, we analyzed health and social care indicators derived from primary care clinical information systems. The mathematical architecture of selected indicators was used as an approximation to model-based systems engineering. Results: Primary care indicators enabled the identification and aggregation of community-level data reflecting BCVs. The system supports multi-level analysis (regional, managerial, institutional, and professional), facilitating grouped and anonymized data extraction for future vulnerability assessment. Conclusions: A minimum set of primary care indicators can effectively estimate community vulnerability, supporting person-centered health system management and informed decision-making.

Background: Knowledge management in global health is essential in response to ageing populations, increasing morbidity, and rising expectations of care. The Knowledge Model about Person Care promotes health systems organized around individuals rather than diseases. Within this framework, vulnerability—understood as the risk of physical or moral harm—can be assessed through Basic Care Variables (BVC) that determine individuals’ need and capacity for self-care. Primary care health information systems provide an opportunity to operationalize these variables at the population level. Methods: This study applies Deductive Methodology to extrapolate community-level health indicator data to population-level vulnerability measures. Using the electronic Primary Care Objective Monitoring tool (e-SOAP) from the Community of Madrid, we analyzed health and social care indicators derived from primary care clinical information systems. The mathematical architecture of selected indicators was used as an approximation to Model-Based Systems Engineering. Results: Primary care indicators enabled the identification and aggregation of community-level data reflecting BCV. The system supports multi-level analysis (regional, managerial, institutional, and professional), facilitating grouped and anonymized data extraction for future vulnerability assessment. Conclusion: A minimum set of primary care indicators can effectively estimate community vulnerability, supporting person-centred health system management and informed decision-making.

Gamification and game-based learning provide the opportunity to acquire knowledge and skills on a given subject in a practical and interactive way. They are an innovative teaching methodology that could be used for competence acquisition in a variety of fields. This study focuses on two domains: technology (including robotics) and care. This study evaluates the usability of RobotCareMaker^®, a board game designed to teach care robotics, a branch of robotics oriented towards the study of human care. RobotCareMaker^® consists of 106 elements. The playing cards are the engine of the game and the element of interaction between players. A convenience sample was selected. Usability was evaluated by the System Usability Scale (SUS) questionnaire modified for the game, and three questions about the game experience were used. Using a modified SUS questionnaire, 21 participants rated it with an excellent score of 80.36. Over 90% found the instructions clear and error-free. RobotCareMaker^® allows teachers, professionals, and nursing students to integrate curricular competencies in novel topics such as care robotics. The result suggests that RobotCareMaker^® enhances learning in assistive robotics, improving competencies in education and healthcare.