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Co-word analysis in nursing science
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Ehsan Geraei    , Razieh Farshid * , Fatemeh Faraji |
| PhD student of Knowledge and Information Science, Faculty of Psychology and Education, Kharazmi University, Tehran, Iran , razieh.farshid@gmail.com |
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Abstract: (3947 Views) |
Background and aim: Co-word analysis is a tool for depicting the scientific structure of outputs. Therefore, the aim of this study was to evaluate the co-word analysis in nursing science by examining the keywords of Master's and Doctoral dissertations in the Faculty of Nursing and Midwifery of Shahid Beheshti University of Medical Sciences.
Materials and methods: In this scientometric study used co-word analysis method, the research population included 473 master's and doctoral dissertations in the field of nursing in the Faculty of Nursing and Midwifery of Shahid Beheshti University of Medical Sciences. Data were analyzed using Ravar, Gephi, UCINET and SPSS.
Findings: The results showed that 902 keywords were used in the dissertations that the highest frequency of words in the nursing dissertations of Shahid Beheshti University of Medical Sciences was related to the nurse (117), neonate (58), quality (53), respectively. Results associated with hierarchical clustering by Ward’s method led to the formation of seven clusters in this field: "Nursing Management, Community Health Nursing, Neonatal Nursing, Neonatal Intensive Care Units, Nursing Research Methodology, Community Health Nursing" and "Nursing Emergencies" so that cluster 6 (Community Health Nursing) and cluster 1 (Nursing Management) had the highest and lowest centrality among different clusters, respectively. Moreover, cluster 7 (Nursing Emergencies) and cluster 6 (Community Health Nursing) had the highest and lowest density among other clusters, respectively.
Conclusion: In nursing research, the issues of quality, health and self-care play a central role, but the issues of stress, anxiety and mortality have not been considered in this field. |
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| Keywords: Scientific structure, Co-word analysis, Hierarchical clustering, Shahid Beheshti University of Medical Sciences, Nursing |
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Full-Text [PDF 858 kb]
(2092 Downloads)
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Type of Study: Orginal |
Subject:
Scientometrics
Received: 2020/11/28 | Revised: 2021/07/10 | Accepted: 2021/05/18 | ePublished: 2021/09/1
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