Validation of the Health Index in the Postoperative Period: Use of the Nursing Outcome Classification to Determine the Health Level.

BACKGROUND: The postoperative period is the recovery time after surgery and is defined as an individual process whose purpose is to return the person to the state of normality and integrity that they had prior to surgery. AIM: Demonstrate the modification of the level of health of people in the early postoperative period through the development and validation of the Health Index Instrument, which is built from the Nursing Outcomes Classification (NOC) standardized language. DESIGN: The design used a mixed method, which involved a first phase of instrument development and a second phase of instrument validation. METHODS: The methods was based on focus group techniques with text analysis techniques, internal validation with a group of care language experts, external validation with a group of clinical nursing experts and a clinical validation with quantitative and qualitative analysis. A panel of experts in Language of Care evaluated the (NOC) labels and their correlation with the 11 Health Variables to construct the instrument. The instrument developed was subjected to external validation with a panel of clinical nurse experts in post-anesthesia care. The clinical validation included a cross-sectional descriptive study in a postoperative unit. The final sample of the cross-sectional descriptive study was 139 cases. RESULTS: Of the 89 NOCs proposed in the preliminary construction phase of the instrument, 36 passed through the first round. Of those 36 NOCs, 25 passed through to the second round with a review performance and 11 directly as approved. The total number of approved NOCs were 4. The results of the research show that there are changes in the global score of the health level and in each health variable. It is observed that there was a significant increase in the scores of the health variables at admission and discharge (p < 0.001). CONCLUSIONS: The results of the data analysis show that six groups present a similar pattern of evolution of the health variables. A correlation was found between the time of stay in the unit with the scores obtained in the health variables, the physical functioning, comfort status and the presence of symptoms being particularly significant.

Deductive Care Methodology: Describing and testing modes of care research.

OBJECTIVE: Define the modes of procedure of the Deductive Care Methodology (DCM) in the generation of knowledge about person's health care. METHODOLOGY: Design and test of the DCM modes based on three phases: mapping of the DCM, generation of models from this methodology and testing of the models through studies in a clinical context. RESULTS: The DCM presents five levels of abstraction with three modes broken down to 16 types. The modes are: Philosophical Mode to conceptualize and obtain generalities about reality, Mathematical Mode to operate with generalities, and Physical Mode to operationally verify, validating the results and the predictive capacity of the model. This MDC allows the creation of three models: Knowledge Model about Person Care, an ontology of care, Vulnerability Model about the person and Taxonomic Triangulation Model for knowledge management. All models generate products for computational knowledge management. In addition, the models are applied in teaching and generate research with more than a hundred participations in conferences and journals, of which five impact publications (from 2008 to 2022) classified in the categories of Nursing and Informatics are analysed. CONCLUSIONS: The DCM collects prior knowledge to work with certainties, evidence and applying inferences that do not depend on the number of cases or inductive designs. This research presents a formal structure of the DCM with an interdisciplinary orientation between Health Sciences and Computer Sciences.

Competencies in the Robotics of Care for Nursing Robotics: A Scoping Review.

In parallel with the development and design of different technological advances, competencies in nursing have advanced. With the development of robotics, it is expected that nursing robotic competencies will also increase. The aim of this study is to review the competencies in nursing robotics. A review was conducted between January 2017 and December 2023. The search strategy was carried out in the MEDLINE database (through PubMed). This review explores the developmental competencies in nursing robotics and informatics. The data extraction in this review included an intentional search for competencies and learning outcomes in engineering and robotic programs. A total of 340 competencies and program outcomes were reviewed. The synthesis of the data established a total of 17 developmental competencies in nursing robotics based on this knowledge extraction, which we organized into five categories: assessment, diagnosis, planning, intervention (implementation) and evaluation. This review suggests that nursing robotic competencies for the development of care robotics are still scarce, and there is an opportunity for the development of competencies and the definition of new roles in the area of nursing informatics in order to adapt to the new health care demands of society.

Active and Healthy Confinement: Care Recommendations on Activity, Sleep and Relationships.

Persons that lived through periods of confinement suffered an impact on their physical and mental health. The adaptation of the lifestyle in relation to activity, sleep and social relationships is key to facing these periods of confinement. The aim is to validate a series of care recommendations aimed at being able to maintain an active and healthy confinement, which serves to prepare the population for future health crises. This study is part of a general strategy based on a care recommendation guide for COVID-19. The validation was carried out by a group of experts using the Delphi technique through a questionnaire that uses the Content Validity Index (CVI) and considers high validation those with a score >0.80. A total of 75 care recommendations are proposed: 30 on activity-exercise (CVI = 0.82), 14 on sleep-rest (CVI = 0.83) and 31 on roles-relationships (CVI = 0.83). Additionally, 49 recommendations achieve high validation. The care recommendations integrate a person-centred model, which addresses individual characteristics (age, health status, professional role). An active and healthy confinement requires respecting social distance measures, maintaining a balance between physical activity and sleep, and using technologies to promote social contact, which promote well-being and avoid depression and anxiety.

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.

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.

The Dimensions of Care: Big Data Analysis in Health Information Systems.

The axioms of care postulate energy and time as underlying entities that allow us to study the dimensions of the person as care: the sorge. Logic allows to formalize the deductive study of sorge knowledge in order to determine what are the dimensions of care. These dimensions will be the guiding framework in the analysis of big data contained in health information systems.