Literature DB >> 23721253

Network nursing: connections between nursing and complex network science.

Matthias Dehmer1, Werner O Hackl, Frank Emmert-Streib, Eva Schulc, Christa Them.   

Abstract

PURPOSE: Nursing processes are complex and include quite a few nonlinear interrelationships that are difficult to discover. Moreover, the formal description, visualization, and analysis of these processes are nontrivial challenges. The purpose of this paper is to establish the term network nursing as synonym for using quantitative graph theory in nursing science and to discuss how network nursing can be used for tackling such complex challenges in nursing science.
METHODS: In particular, methods from quantitative graph theory, divided into two major classes, comparative network analysis and network characterization, are employed to solve challenging problems in nursing.
FINDINGS: We demonstrate by way of example that this mathematical apparatus is feasible to tackle research questions when modeling and analyzing nursing processes. Here we use a "NANDA-I" showcase to illustrate how nursing networks can be derived from nursing data and how these networks can be used to compare different patients regarding their nursing diagnoses.
CONCLUSIONS: Nursing networks can be used to characterize patients from a nursing perspective. Especially, they allow a process-based view and are able to map relationships or dependencies. One major advantage of a networking approach is that it can be applied independently from the underlying nursing classifications or terminologies. IMPLICATIONS FOR NURSING PRACTICE: Network nursing makes it possible to formally investigate the nursing process and thus opens up a so far little-known cosmos of possibilities and methods to expand nursing knowledge.
© 2013 NANDA International.

Entities:  

Keywords:  Complex network; network complexity; nursing; quantitative graph theory

Mesh:

Year:  2013        PMID: 23721253     DOI: 10.1111/j.2047-3095.2013.01246.x

Source DB:  PubMed          Journal:  Int J Nurs Knowl        ISSN: 2047-3087            Impact factor:   1.222


  3 in total

1.  A Nursing Intelligence System to Support Secondary Use of Nursing Routine Data.

Authors:  W O Hackl; F Rauchegger; E Ammenwerth
Journal:  Appl Clin Inform       Date:  2015-06-24       Impact factor: 2.342

2.  Structural Analysis of Treatment Cycles Representing Transitions between Nursing Organizational Units Inferred from Diabetes.

Authors:  Matthias Dehmer; Zeyneb Kurt; Frank Emmert-Streib; Christa Them; Eva Schulc; Sabine Hofer
Journal:  PLoS One       Date:  2015-06-01       Impact factor: 3.240

3.  Family nursing with the assistance of network improves clinical outcome and life quality in patients underwent coronary artery bypass grafting: A consolidated standards of reporting trials-compliant randomized controlled trial.

Authors:  Liying Jin; Ruijin Pan; Lihua Huang; Haixia Zhang; Mi Jiang; Hao Zhao
Journal:  Medicine (Baltimore)       Date:  2020-12-11       Impact factor: 1.817

  3 in total

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