Lu Jing-Ru, Wei Yu-Han, Wang Xin, Zhang Yu-Qing, Shao Jia-Yi, Sun Jiang-Jie
School of Health Care Management, Anhui Medical University, Hefei 230032, Anhui Province, China.
School of Management, Hefei University of Technology, Hefei 230039, Anhui Province, China.
World J Psychiatry. 2024 Jul 19;14(7):1068-1079. doi: 10.5498/wjp.v14.i7.1068.
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.
To explore public emotional differences, the intensity of comments, and the positions represented at different levels of doctor-patient disputes.
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 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.
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.
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.
医患关系负面化所带来的风险严重阻碍了医疗卫生事业的健康发展，并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这类事件的关注程度。
探讨公众在不同层级医患纠纷中的情绪差异、评论强度及所代表的立场。
从微博和抖音收集30起医患纠纷事件，并提取3655条相关评论。提取评论情感词数量，计算评论情感值。采用Kruskal-Wallis检验比较不同发生率水平下各变量组之间的差异。采用Spearman相关分析检验变量之间的关联。采用回归分析探讨影响事件评论得分的因素。
研究结果表明，公众对各级医患纠纷媒体报道的评论主要以“好”和“厌恶”情绪状态为主。不同严重程度的医患纠纷评论在评论得分和部分情感词数量上存在显著差异。评论得分与积极、美好和开心相关情感词数量呈正相关，与消极、愤怒、厌恶、恐惧和悲伤相关情感词数量呈负相关。
与消极、愤怒、厌恶、恐惧和悲伤相关的情感词数量直接影响评论得分，事件层级的严重程度间接影响评论得分。
