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模拟实践中护生非语言沟通模式的分析:一项初步研究。

Analysis of Nursing Students' Nonverbal Communication Patterns during Simulation Practice: A Pilot Study.

作者信息

Jin Eunju, Kang Hyunju, Lee Kunyoung, Lee Seung Gun, Lee Eui Chul

机构信息

Department of Nursing, Gangneung Yeongdong University, Gangneung-si 25521, Republic of Korea.

College of Nursing, Kangwon National University, Chuncheon-si 24341, Republic of Korea.

出版信息

Healthcare (Basel). 2023 Aug 18;11(16):2335. doi: 10.3390/healthcare11162335.

DOI:10.3390/healthcare11162335
PMID:37628532
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10454223/
Abstract

Therapeutic communication, of which nonverbal communication is a vital component, is an essential skill for professional nurses. The aim of this study is to assess the possibility of incorporating computer analysis programs into nursing education programs to improve the nonverbal communication skills of those preparing to become professional nurses. In this pilot observational study, the research team developed a computer program for nonverbal communication analysis including facial expressions and poses. The video clip data captured during nursing simulation practice by 10 3rd- and 4th-grade nursing students at a university in South Korea involved two scenarios of communication with a child's mother regarding the child's pre- and post-catheterization care. The dominant facial expressions varied, with sadness (30.73%), surprise (30.14%), and fear (24.11%) being the most prevalent, while happiness (7.96%) and disgust (6.79%) were less common. The participants generally made eye contact with the mother, but there were no instances of light touch by hand and the physical distance for nonverbal communication situations was outside the typical range. These results confirm the potential use of facial expression and pose analysis programs for communication education in nursing practice.

摘要

治疗性沟通是非语言沟通的重要组成部分,是专业护士必备的技能。本研究旨在评估将计算机分析程序纳入护理教育项目的可能性,以提高准专业护士的非语言沟通技能。在这项试点观察性研究中,研究团队开发了一个用于非语言沟通分析的计算机程序,包括面部表情和姿势。韩国一所大学的10名大三和大四护理专业学生在护理模拟实践中拍摄的视频片段数据,涉及与一名儿童母亲就该儿童导尿前后护理进行沟通的两种场景。主要面部表情各不相同,悲伤(30.73%)、惊讶(30.14%)和恐惧(24.11%)最为普遍,而快乐(7.96%)和厌恶(6.79%)则较少见。参与者通常与母亲有眼神交流,但没有手部轻触的情况,非语言沟通情境中的身体距离超出了典型范围。这些结果证实了面部表情和姿势分析程序在护理实践沟通教育中的潜在用途。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10e4/10454223/a4d3b7ea03ac/healthcare-11-02335-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10e4/10454223/408be1a5a1ca/healthcare-11-02335-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10e4/10454223/a4d3b7ea03ac/healthcare-11-02335-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10e4/10454223/408be1a5a1ca/healthcare-11-02335-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10e4/10454223/a4d3b7ea03ac/healthcare-11-02335-g002.jpg

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本文引用的文献

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Healthcare (Basel). 2022 Feb 7;10(2):311. doi: 10.3390/healthcare10020311.
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Facial expression recognition based on deep learning.基于深度学习的面部表情识别。
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