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.

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.

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.

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.

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.