PDF(Pubmed)
Abstract：
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.
OBJECTIVE: 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.
CONCLUSIONS: 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.
OBJECTIVE: 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.
CONCLUSIONS: 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.
摘要：
背景：负面医患关系带来的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：探索公众情绪差异，评论的强度，以及在不同层次的医患纠纷中所代表的立场。
方法：从微博和TikTok收集了30起医患纠纷事件，并提取了3655条相关评论。提取了评论情感词的数量，并计算了评论情绪值。使用Kruskal-WallisH检验来比较每个变量组在不同发生率水平下的差异。使用Spearman的相关分析来检查变量之间的关联。回归分析用于探索影响事件评论得分的因素。
结果：研究结果表明，公众对各级媒体报道的医患纠纷的评论主要以“好”和“厌恶”情绪状态为主。在不同程度的医患纠纷的评论之间，评论得分和部分情绪词的数量存在显着差异。评论得分与情感相关词数呈正相关,不错,和快乐)，并与与否定相关的情感词的数量负相关，愤怒,厌恶,恐惧,和悲伤。
结论：与否定相关的情感词的数量，愤怒,厌恶,恐惧,悲伤直接影响评论得分，事件级别的严重程度间接影响评论得分。
目的：探索公众情绪差异，评论的强度，以及在不同层次的医患纠纷中所代表的立场。
方法：从微博和TikTok收集了30起医患纠纷事件，并提取了3655条相关评论。提取了评论情感词的数量，并计算了评论情绪值。使用Kruskal-WallisH检验来比较每个变量组在不同发生率水平下的差异。使用Spearman的相关分析来检查变量之间的关联。回归分析用于探索影响事件评论得分的因素。
结果：研究结果表明，公众对各级媒体报道的医患纠纷的评论主要以“好”和“厌恶”情绪状态为主。在不同程度的医患纠纷的评论之间，评论得分和部分情绪词的数量存在显着差异。评论得分与情感相关词数呈正相关,不错,和快乐)，并与与否定相关的情感词的数量负相关，愤怒,厌恶,恐惧,和悲伤。
结论：与否定相关的情感词的数量，愤怒,厌恶,恐惧,悲伤直接影响评论得分，事件级别的严重程度间接影响评论得分。
