Exploring Research Topics and Trends in Nursing-related Communication in Intensive Care Units Using Social Network Analysis

Topics and Trends in Neonatal Family-Centered Care: A Text Network Analysis and Topic Modeling Approach

This study used text network analysis and topic modeling to examine the knowledge structure of family-centered care in neonatal ICU nurses. Text was extracted from abstracts of 110 peer-reviewed articles published between 1995 and 2023 and analyzed by identifying keywords, topics, and changes in research topics over time. Analysis of keywords revealed significant terms including "infant," "family," "experience," "interventions," and "parent participation," highlighting family's central roles in family-centered care in neonatal ICU discourse. The research topics identified included "family-centered partnerships," "barriers to implementing family-centered care," "infant-mother attachment intervention," "family participation intervention," and "parenthood." Over time, research on family-centered care in neonatal ICUs nurses has steadily increased, with notable increases in "family-centered partnerships" and "barriers to implementing family-centered care." The findings underscore the evolving landscape of family-centered care in neonatal ICUs, emphasizing the critical role of collaborative care models in enhancing neonatal and familial outcomes. These insights provide a foundation for developing family-centered care programs that empower both nurses and families, supporting the holistic care of vulnerable infants. This study's results offer comprehensive insights into understanding family-centered care in the neonatal ICUs and could serve as a foundation for future studies to develop family-centered care programs for neonatal ICU nurses and families. Based on this study, it is recommended that nursing education programs integrate family-centered care training into their curricula, with an emphasis on communication, cultural competence, and family partnerships.

Natural Language Processing Application in Nursing Research: A Study Using Text Network Analysis and Topic Modeling

Although the potential of natural language processing and an increase in its application in nursing research is evident, there is a lack of understanding of the research trends. This study conducts text network analysis and topic modeling to uncover the underlying knowledge structures, research trends, and emergent research themes within nursing literature related to natural language processing. In addition, this study aims to provide a foundation for future scholarly inquiries and enhance the integration of natural language processing in the analysis of nursing research. We analyzed 443 literature abstracts and performed core keyword analysis and topic modeling based on frequency and centrality. The following topics emerged: (1) Term Identification and Communication; (2) Application of Machine Learning; (3) Exploration of Health Outcome Factors; (4) Intervention and Participant Experience; and (5) Disease-Related Algorithms. Nursing meta-paradigm elements were identified within the core keyword analysis, which led to understanding and expanding the meta-paradigm. Although still in its infancy in nursing research with limited topics and research volumes, natural language processing can potentially enhance research efficiency and nursing quality. The findings emphasize the possibility of integrating natural language processing in nursing-related subjects, validating nursing value, and fostering the exploration of essential paradigms in nursing science.

Research Trends in Family-Centered Care for Children With Chronic Disease: Keyword Network Analysis

Family-centered care is an approach to promote the health and well-being of children with chronic diseases and their families. This study aims to explore the knowledge components, structures, and research trends related to family-centered care for children with chronic conditions. We conducted the keyword network analysis in three stages using the keywords provided by the authors of each study: (1) search and screening of relevant studies, (2) keyword extraction and refinement, and (3) data analysis and visualization. The core keywords were child, adolescence, parent, and disabled. Four cohesive subgroups were identified through degree centrality. Research trends in the three phases of a recent decade have been changed. With the systematic understanding of the context of the knowledge structure, the future research and effective strategy establishment are suggested based on family-centered care for children with chronic disease.

Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm

Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life and expenditures. Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in nursing practice and is increasingly used for diagnosis, complications, prognosis, and recurrence prediction. This study aims to investigate hospital-acquired PI (HAPI) risk predictions in ICU based on a ML algorithm by R programming language analysis. The former evidence was gathered through PRISMA guidelines. The logical analysis was applied via an R programming language. ML algorithms based on usage rate included logistic regression (LR), Random Forest (RF), Distributed tree (DT), Artificial neural networks (ANN), SVM (Support Vector Machine), Batch normalisation (BN), GB (Gradient Boosting), expectation-maximisation (EM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Six cases were related to risk predictions of HAPI in the ICU based on an ML algorithm from seven obtained studies, and one study was associated with the Detection of PI risk. Also, the most estimated risksSerum Albumin, Lack of Activity, mechanical ventilation (MV), partial pressure of oxygen (PaO2), Surgery, Cardiovascular adequacy, ICU stay, Vasopressor, Consciousness, Skin integrity, Recovery Unit, insulin and oral antidiabetic (INS&OAD), Complete blood count (CBC), acute physiology and chronic health evaluation (APACHE) II score, Spontaneous bacterial peritonitis (SBP), Steroid, Demineralized Bone Matrix (DBM), Braden score, Faecal incontinence, Serum Creatinine (SCr) and age. In sum, HAPI prediction and PI risk detection are two significant areas for using ML in PI analysis. Also, the current data showed that the ML algorithm, including LR and RF, could be regarded as the practical platform for developing AI tools for diagnosing, prognosis, and treating PI in hospital units, especially ICU.

Social network analysis: Understanding nurses' advice-seeking interactions

AIM

This study aimed to determine advice-seeking interactions of nurses in a private hospital by using social network analysis.

DESIGN

This study was designed as a cross-sectional descriptive study.

METHODS

The study was conducted in a private hospital with 70 nurses. The data were collected with a social network analysis questionnaire. The social network analysis (SNA) focused on certain values such as network density, component, degree centrality, and betweenness centrality. The SNA was carried out using UCINET, and statistical analyses were performed with SPSS version 23.0.

RESULTS

The network density was reported to be 0.062, and it was composed of three components. It was further noted that nurse Y1 was found to have the highest scores of degree and betweenness centrality. Chi-Square Automatic Interaction Detector (CHAID) analysis indicated that the most common variables that affected degree centrality score were education, department, and position.

CONCLUSION

It was concluded that social network analysis was a useful instrument to delineate strengths and weaknesses of seeking advice relationships among nurses.

IMPLICATIONS FOR NURSING AND HEALTH POLICY

Top- and middle-level nursing managers occupy a significant position in advice-seeking networks. Nursing managers with higher education degrees will absolutely improve advice-seeking networks.

Connections and Biases in Health Equity and Culture Research: A Semantic Network Analysis

Health equity is a rather complex issue. Social context and economical disparities, are known to be determining factors. Cultural and educational constrains however, are also important contributors to the establishment and development of health inequities. As an important starting point for a comprehensive discussion, a detailed analysis of the literature corpus is thus desirable: we need to recognize what has been done, under what circumstances, even what possible sources of bias exist in our current discussion on this relevant issue. By finding these trends and biases we will be better equipped to modulate them and find avenues that may lead us to a more integrated view of health inequity, potentially enhancing our capabilities to intervene to ameliorate it. In this study, we characterized at a large scale, the social and cultural determinants most frequently reported in current global research of health inequity and the interrelationships among them in different populations under diverse contexts. We used a data/literature mining approach to the current literature followed by a semantic network analysis of the interrelationships discovered. The analyzed structured corpus consisted in circa 950 articles categorized by means of the Medical Subheadings (MeSH) content-descriptor from 2014 to 2021. Further analyses involved systematic searches in the LILACS and DOAJ databases, as additional sources. The use of data analytics techniques allowed us to find a number of non-trivial connections, pointed out to existing biases and under-represented issues and let us discuss what are the most relevant concepts that are (and are not) being discussed in the context of Health Equity and Culture.

Identifying ICU admission decision patterns in a '20-questions game' approach using network analysis

Background

The complex intensive care unit (ICU) admission decision process has numerous non-linear relationships involving multiple factors. To better describe and analyse this process, exploration of novel techniques to clearly delineate the importance and interrelationships of factors is warranted. Network analysis (NA), based on graph theory, attempts to identify patterns of connections within a network and may be useful in this regard.

Objectives

To identify patterns of ICU decision-making pertaining to patients referred for admission to ICU and to identify key factors, their distribution, connection and relative importance. The secondary aim was to compare subgroups as per decision outcomes and case labels.

Methods

NA was performed using Gephi software package as a secondary analysis on a dataset generated from a previous study on ICU admission decision-making process using a 20-questions game approach. The data were standardised and coded up to a quaternary level for this analysis.

Results

The coding process generated 31 nodes and 964 edges. Regardless of the measure used (centrality, prestige, authority and hubs), properties of the acute illness, progress of the acute illness and properties of comorbidities emerged consistently as among the most important factors and their relative rankings differed. Using different measures allowed important factors to emerge differentially. The six subgroups that emerged from the modularity measure bore little resemblance to traditional factor subgroups. Differences were noted in the subgroup comparisons of decision outcomes and case prognoses.

Conclusion

The use of NA with its various measures has facilitated a more comprehensive exploration of the ICU admission decision, allowing us to reflect on the process. Further studies with larger datasets are needed to elucidate the exact role of NA in decision-making processes.

Contributions of the study

We performed a novel analysis of a complex decision-making process that allowed for comparison with traditional analytic methods. It allowed for identification of key factors, their distribution, connection and relative importance. This may subsequently allow for reflection on difficult decision-making processes, thereby leading to more appropriate outcomes. Moreover, this may lead to new considerations in developing decision support systems such as the formulation of pro-forma data-capture tools (e.g. referral forms). Further, the way factors have been traditionally subgrouped may need to be reconsidered, with different subgroups being partitioned to better reflect their connection. This study offers a good basis for more advanced future studies in this area to use a new variety of analytical tools.

Text network analysis of research topics and trends on global health nursing literature from 1974 -2017

AIMS

The purpose of this study is to examine the relationship between keywords in existing global health nursing studies during 44 years (1974-2017) and to develop schematic diagrams of the relationship between these keywords from a macro perspective. It is to identify the trend of the literature in global health nursing field.

DESIGN

A descriptive bibliometric analysis of publications in global health nursing.

METHODS

The keywords from 7,115 articles and literatures were examined using the Text Rank Analyzer via the applied text network analysis with NetMiner 4.0.

RESULTS

As for global health nursing, keywords with the most frequent appearance and the highest networking degree in centrality were 'study', 'patient', 'nurse', and 'women'. Six central keywords were also found highly related to other keywords: 'global health nursing', 'study', 'patient', 'care', 'nurse', and 'education'. By measuring the degree of keywords connected to other keywords in centrality, six clusters were established. Then, emerging topics assessed by time periods were identified as follows: the beginning phase ('breastfeeding', 'women', and 'children'), the development phase ('quality', 'life', and 'human immunodeficiency virus'), the maturation phase ('mental health', 'depression', and 'global health'), and the expansion phase ('pregnancy', 'palliative care', and 'infectious disease').

CONCLUSION

The identified trends on this study will help nurse leaders to grasp the trends and insights for global health and to train future nurses to serve clients better in the practice fields.

IMPACT

Keywords with the highest appearance and centrality in the network were found in the global health articles. The bibliometric analysis showed various subjects according to the following phases: beginning development maturation and expansion. The awareness of the trend change in the global health helps nursing researchers and educators modify the curriculum of global health nursing and train future nurses to be equipped with the global health competencies.