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Content Analysis of Feedback Journals for New Nurses From Preceptor Nurses Using Text Network Analysis

This study aimed to identify keywords, core topic areas, and subthemes by analyzing feedback journals written by preceptor nurses to new nurses during the preceptorship period and to derive implications through word clustering. A total of 143 preceptor nurses' feedback journals for new nurses f...

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Detalles Bibliográficos
Autores principales: Ahn, Shin Hye, Jeong, Hye Won
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10581421/
https://www.ncbi.nlm.nih.gov/pubmed/37326509
http://dx.doi.org/10.1097/CIN.0000000000001040
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author Ahn, Shin Hye
Jeong, Hye Won
author_facet Ahn, Shin Hye
Jeong, Hye Won
author_sort Ahn, Shin Hye
collection PubMed
description This study aimed to identify keywords, core topic areas, and subthemes by analyzing feedback journals written by preceptor nurses to new nurses during the preceptorship period and to derive implications through word clustering. A total of 143 preceptor nurses' feedback journals for new nurses from March 2020 to January 2021 were converted into a database using Microsoft Office Excel. Text network analysis was performed using the NetMiner 4.4.3 program. After data preprocessing, simple frequency, degree centrality, closeness centrality, betweenness centrality, and community modularity were analyzed. In the feedback journals, the most central words were “study,” “medication,” “practice,” “nursing,” “method,” “need,” and “effort,” whereas frustration, “new nurses” had low centrality. Five subthemes were derived: (1) learning necessity to strengthen new nurses' competency, (2) independence of new nurses, (3) emphasis on accuracy in nursing skills, (4) difficulties in understanding the nursing tasks expected of new nurses, and (5) basic competency of new nurses. The results of this study highlighted the experiences of new nurses and allowed for an assessment of journal feedback content provided by preceptor nurses. As such, the study provides basic data to develop a standardized education and competency empowerment program for preceptor nurses.
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spelling pubmed-105814212023-10-18 Content Analysis of Feedback Journals for New Nurses From Preceptor Nurses Using Text Network Analysis Ahn, Shin Hye Jeong, Hye Won Comput Inform Nurs Features This study aimed to identify keywords, core topic areas, and subthemes by analyzing feedback journals written by preceptor nurses to new nurses during the preceptorship period and to derive implications through word clustering. A total of 143 preceptor nurses' feedback journals for new nurses from March 2020 to January 2021 were converted into a database using Microsoft Office Excel. Text network analysis was performed using the NetMiner 4.4.3 program. After data preprocessing, simple frequency, degree centrality, closeness centrality, betweenness centrality, and community modularity were analyzed. In the feedback journals, the most central words were “study,” “medication,” “practice,” “nursing,” “method,” “need,” and “effort,” whereas frustration, “new nurses” had low centrality. Five subthemes were derived: (1) learning necessity to strengthen new nurses' competency, (2) independence of new nurses, (3) emphasis on accuracy in nursing skills, (4) difficulties in understanding the nursing tasks expected of new nurses, and (5) basic competency of new nurses. The results of this study highlighted the experiences of new nurses and allowed for an assessment of journal feedback content provided by preceptor nurses. As such, the study provides basic data to develop a standardized education and competency empowerment program for preceptor nurses. Lippincott Williams & Wilkins 2023-07-20 /pmc/articles/PMC10581421/ /pubmed/37326509 http://dx.doi.org/10.1097/CIN.0000000000001040 Text en Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.
spellingShingle Features
Ahn, Shin Hye
Jeong, Hye Won
Content Analysis of Feedback Journals for New Nurses From Preceptor Nurses Using Text Network Analysis
title Content Analysis of Feedback Journals for New Nurses From Preceptor Nurses Using Text Network Analysis
title_full Content Analysis of Feedback Journals for New Nurses From Preceptor Nurses Using Text Network Analysis
title_fullStr Content Analysis of Feedback Journals for New Nurses From Preceptor Nurses Using Text Network Analysis
title_full_unstemmed Content Analysis of Feedback Journals for New Nurses From Preceptor Nurses Using Text Network Analysis
title_short Content Analysis of Feedback Journals for New Nurses From Preceptor Nurses Using Text Network Analysis
title_sort content analysis of feedback journals for new nurses from preceptor nurses using text network analysis
topic Features
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10581421/
https://www.ncbi.nlm.nih.gov/pubmed/37326509
http://dx.doi.org/10.1097/CIN.0000000000001040
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