Validation and reliability of the Care Vulnerability Index: A study by interrater agreement and test-retest method.

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.

Usability Evaluation by Nurses of a Knowledge-Based System of Care Plans for People Hospitalized by COVID-19.

The aim of this study is to analyze the usability by nurses of the Knowledge-Based System "Diagnostics care for COVID-19." A convenience sample of 16 nurses was selected, among hospital workers and external experts. The group was divided into three subgroups intentionally to obtain different usability perspectives. Usability was evaluated by the System Usability Scale questionnaire. The participants completed the questionnaire on general usability, data inputs, and information output, after completing a minimum of 12 care plans. The first subgroup used real cases and the "think aloud" technique, the second simulated cases from the same hospital, and the third subgroup performed the external simulation. The highest scores were obtained in data inputs (94.38-97.50); and the lowest, in general usability (90.00-95.00). The subgroup of external experts scored the highest (93.13-95.63), and the first subgroup, which carried out real cases, gave the lowest score (90.00-94.38). The "think aloud" technique found an improvement in including more diagnoses and being able to carry out several plans for one person at the same time. The usability obtained was "excellent" in all subgroups and questionnaires, although the application showed limitations related to its characteristics imposed in the requirements specification.

Diagnostics of care about environment: A systematic review through nursing taxonomies.

AIM: To analyse the representation of the environment in nursing diagnostic taxonomies. DESIGN: Systematic scoping review through nursing taxonomies. METHODS: The first phase identified nursing diagnostic taxonomies by systematic review. The diagnoses were associated with the environment by analysis of terms into the diagnosis label and definition. Data analysis was quantitative with frequency measurements. The second phase mapped the identified diagnoses to establish equivalences using analysis by terms in the diagnostic labels. Finally, the findings obtained in the first phase were compared with the OMAHA System. RESULTS: The bibliographic search identified 112 studies and 16 standardized languages for diagnoses. NANDA-I and ICNP were the most frequent taxonomies; ATIC, the most recent; and OMAHA, the oldest. 2,062 diagnoses from four diagnostic taxonomies were analysed, and 361 associations corresponding to 352 environmental diagnoses were identified. All taxonomies included the environment but with different weight relative to the interpersonal and geopolitical category.

Nursing Diagnoses for Coronavirus Disease, COVID-19: Identification by Taxonomic Triangulation.

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.

Elements for an Ontology of Care in the Field of Artificial Intelligence.

An ontology of care is a formal, explicit specification of a shared conceptualization. Constructing an ontology is a process that requires four elements: knowledge object, subject that knows, knowledge operation and result. These elements configure theframework to generate ontologies that can be used in Artificial Intelligence systems for care.