A hierarchical approach to analyzing knowledge integration between two fields—a case study on medical informatics and computer science

Global hotspots and trends in research on preschool children's motor development from 2012 to 2022: a bibliometric analysis

Background

Motor development plays an important role in human development throughout the lifespans, from conception to death, and has received increasing scholarly attention in recent years. However, valuable comprehensive reviews and literature analysis on this topic are still lacking. Here, this bibliometric study aimed to identify global motor development research hotspots and trends on preschool children's motor development from 2012 to 2022.

Methods

CiteSpace 6.1.R4 was used to visualize and analyze general bibliometric characteristics, research hotspots, and trends through a review of 2,583 articles on the motor development of preschool children, which were published from 2012 to 2022 and included in the Web of Science Core Collection.

Results

Research on motor development in preschool children has been carried out into a phase of rapid development. The top five frequently occurring keywords were physical activity (n = 489), performance (n = 319), intervention (n = 222), health (n = 196), and executive function (n = 165); The top five keywords in terms of centrality are academic achievement (0.22), low birth weight (0.16), association (0.14), brain (0.13), and cerebral palsy (0.13). Thirteen keyword clusters were produced from the log-likelihood ratio (Q = 0.74, S = 0.88), and five research topics has been received focused attention in recent years. The keywords with the strongest citation bursts in the last 5 years are developing country (S = 5.92), school-aged children (S = 5.86), middle-income country (S = 3.46), efficacy (S = 5.41), readiness (S = 3.21), motor proficiency (S = 3.6), and screen time (S = 3.3), indicating newly emerging research trends.

Conclusion

The results indicated that interventions involving fundamental movement skills, cognitive function, 24-h movement behaviors, neurodevelopmental disorders, and health-related fitness were hot topics in the field of motor development over the last decade. Emerging research trends generally center on school readiness, socioeconomic status, motor proficiency, and screen time.

The popularisation of self-care: Tracing the dissemination of Orem's Self-Care Deficiency Nursing Theory into the scientific disciplines

BACKGROUND

Bibliometric methods may be used to examine research trends, and information visualisation techniques are useful in illustrating the diffusion of knowledge and how theories are applied.

OBJECTIVES

The present study aimed to illustrate how Orem's Self-Care Deficiency Nursing Theory (SCDNT) has been cited and applied in nursing science and beyond.

METHODS

A bibliometric analysis examined scientific publications that cited Orem's nursing theory. In addition, the diffusion of SCDNT was assessed using data visualisation methods and integration scores of SCDNT versions were calculated to define trends in its theoretical usage in other scientific domains.

RESULTS

The information visualisation demonstrated increased usage of SCDTN in different disciplines. Integration scores demonstrated that the scientific community still recognises and uses versions of SCDTN.

DISCUSSION

Studying citation patterns helps to identify which publications are still cited and relevant, as well as illustrating the dissemination of theory. Findings may be used in the collection weeding of SCDTN book versions.

CONCLUSIONS

Findings provided nursing scientists with a better conceptual understanding of SCDNT diffusion and development. For academic library managers, the findings identify which SCDNT should be retained for historical interest and curriculum needs.

A Multi-RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency-Based Scientific Influence Modeling

Computer science discipline includes many research fields, which mutually influence and promote each other's development. This poses two great challenges of predicting the research topics of each research field. One is how to model fine-grained topic representation of a research field. The other is how to model research topic of different fields and keep the semantic consistency of research topics when learning the scientific influence context from other related fields. Unfortunately, the existing research topic prediction approaches cannot handle these two challenges. To solve these problems, we employ multiple different Recurrent Neural Network chains which model research topics of different fields and propose a research topic prediction model based on spatial attention and semantic consistency-based scientific influence modeling. Spatial attention is employed in field topic representation which can selectively extract the attributes from the field topics to distinguish the importance of field topic attributes. Semantic consistency-based scientific influence modeling maps research topics of different fields to a unified semantic space to obtain the scientific influence context of other related fields. Extensive experiment results on five related research fields in the computer science (CS) discipline show that the proposed model is superior to the most advanced methods and achieves good topic prediction performance.

Content Characteristics of Knowledge Integration in the eHealth Field: An Analysis Based on Citation Contexts

Purpose

This study attempts to disclose the characteristics of knowledge integration in an interdisciplinary field by looking into the content aspect of knowledge.

Design/methodology/approach

The eHealth field was chosen in the case study. Associated knowledge phrases (AKPs) that are shared between citing papers and their references were extracted from the citation contexts of the eHealth papers by applying a stem-matching method. A classification schema that considers the functions of knowledge in the domain was proposed to categorize the identified AKPs. The source disciplines of each knowledge type were analyzed. Quantitative indicators and a co-occurrence analysis were applied to disclose the integration patterns of different knowledge types.

Findings

The annotated AKPs evidence the major disciplines supplying each type of knowledge. Different knowledge types have remarkably different integration patterns in terms of knowledge amount, the breadth of source disciplines, and the integration time lag. We also find several frequent co-occurrence patterns of different knowledge types.

Research limitations

The collected articles of the field are limited to the two leading open access journals. The stem-matching method to extract AKPs could not identify those phrases with the same meaning but expressed in words with different stems. The type of Research Subject dominates the recognized AKPs, which calls on an improvement of the classification schema for better knowledge integration analysis on knowledge units.

Practical implications

The methodology proposed in this paper sheds new light on knowledge integration characteristics of an interdisciplinary field from the content perspective. The findings have practical implications on the future development of research strategies in eHealth and the policies about interdisciplinary research.

Originality/value

This study proposed a new methodology to explore the content characteristics of knowledge integration in an interdisciplinary field.

Measuring interdisciplinarity of research articles: An analysis of inter-relatedness of different parameters

With evolution of knowledge disciplines and cross fertilization of ideas, research outputs reported as scientific papers are now becoming more and more interdisciplinary. An interdisciplinary research work usually involves ideas and approaches from multiple disciplines of knowledge applied to solve a specific problem. In many cases the interdisciplinary areas eventually emerge as full-fledged disciplines. In the last two decades, several approaches have been proposed to measure the Interdisciplinarity of a scientific article, such as propositions based on authorship, references, set of keywords etc. Among all these approaches, reference-set based approach is most widely used. The diversity of knowledge in the reference set has been measured with three parameters, namely variety, balance, and disparity. Different studies tried to combine these measures in one way or other to propose an aggregate measure of interdisciplinarity, called integrated diversity. However, there is a lack of understanding on inter-relations between these parameters. This paper tries to look into inter-relatedness between the three parameters by analytical study on an important interdisciplinary research area, Internet of Things (IoT). Research articles in IoT, as obtained from Web of Science for the year 2018 have been analyzed to compute the three measures and understand their inter-relatedness. Results obtained show that variety and balance are negatively correlated, variety and disparity do not show a stable relatedness and balance and disparity are negatively correlated. Further, the integrated diversity measure is negatively correlated with variety and weakly positively correlated with balance and disparity. The results imply that the composite integrated diversity measure may not be a suitably constructed composite measure of interdisciplinarity.