Emotional differences based on comments on doctor-patient disputes with varying levels of severity
作者机构：School of Health Care ManagementAnhui Medical UniversityHefei 230032Anhui ProvinceChina School of ManagementHefei University of TechnologyHefei 230039Anhui ProvinceChina
出 版 物：《World Journal of Psychiatry》 (世界精神病学杂志)
年 卷 期：2024年第14卷第7期
页 面：1068-1079页
核心收录：
学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学]
基 金：Supported by the National Natural Science Foundation of China,No.72374005 Natural Science Foundation for the Higher Education Institutions of Anhui Province of China,No.2023AH050561 Cultivation Programme for Young and Middle-aged Excellent Teachers in Anhui Province,No.YQZD2023021
主 题：Doctor-patient relationship Doctor-patient dispute Comments Emotional differences Weibo TikTok
摘 要：BACKGROUND The risks associated with negative doctor-patient relationships have seriously hindered the healthy development of medical and healthcare and aroused wide-spread concern in *** number of public comments on doctor-patient relationship risk events reflects the degree to which the public pays attention to such *** incidents of doctor-patient disputes were collected from Weibo and TikTok,and 3655 related comments were *** number of comment sentiment words was extracted,and the comment sentiment value was *** Kruskal-Wallis H test was used to compare differences between each variable group at different levels of ***’s correlation analysis was used to examine associations between *** analysis was used to explore factors influencing scores of comments on *** The study results showed that public comments on media reports of doctor-patient disputes at all levels are mainly dominated by“goodand“disgustemotional *** 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 *** 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 *** 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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