基于不同严重程度的医患纠纷评论的情感差异

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Jing-Ru Lu, Yu-Han Wei, Xin Wang, Yu-Qing Zhang, Jia-Yi Shao, Jiang-Jie Sun
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引用次数: 0

摘要

背景 负面医患关系风险严重阻碍了医疗卫生事业的健康发展,引起了社会的广泛关注。公众对医患关系风险事件的评论数量反映了公众对此类事件的关注程度。目的 探讨医患纠纷中公众的情绪差异、评论强度以及不同层面所代表的立场。方法 从微博和嘀嗒中收集 30 起医患纠纷事件,提取相关评论 3655 条。提取评论情感词数,计算评论情感值。采用 Kruskal-Wallis H 检验比较各变量组在不同发生率水平下的差异。斯皮尔曼相关分析用于研究变量之间的关联。回归分析用于探讨影响事件评论得分的因素。结果 研究结果显示,公众对各级媒体医患纠纷报道的评论主要以 "好 "和 "反感 "两种情绪状态为主。不同严重程度医患纠纷的评论在评论得分和部分情绪词数量上存在明显差异。评论得分与正面、好的和开心的情绪词数量呈正相关,与负面、愤怒、厌恶、恐惧和悲伤的情绪词数量呈负相关。结论 与负面、愤怒、厌恶、恐惧和悲伤相关的情绪词数量直接影响评论得分,事件的严重程度间接影响评论得分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emotional differences based on comments on doctor-patient disputes with varying levels of severity
BACKGROUND The risks associated with negative doctor-patient relationships have seriously hindered the healthy development of medical and healthcare and aroused widespread concern in society. The number of public comments on doctor-patient relationship risk events reflects the degree to which the public pays attention to such events. AIM To explore public emotional differences, the intensity of comments, and the positions represented at different levels of doctor-patient disputes. METHODS Thirty incidents of doctor-patient disputes were collected from Weibo and TikTok, and 3655 related comments were extracted. The number of comment sentiment words was extracted, and the comment sentiment value was calculated. The Kruskal-Wallis H test was used to compare differences between each variable group at different levels of incidence. Spearman’s correlation analysis was used to examine associations between variables. Regression analysis was used to explore factors influencing scores of comments on incidents. RESULTS The study results showed that public comments on media reports of doctor-patient disputes at all levels are mainly dominated by “good” and “disgust” emotional states. There was a significant difference in the comment scores and the number of partial emotion words between comments on varying levels of severity of doctor-patient disputes. The comment score was positively correlated with the number of emotion words related to positive, good, and happy) and negatively correlated with the number of emotion words related to negative, anger, disgust, fear, and sadness. CONCLUSION The number of emotion words related to negative, anger, disgust, fear, and sadness directly influences comment scores, and the severity of the incident level indirectly influences comment scores.
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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