College of Medicine and Health, University of Exeter, Exeter, UK.
Translational Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
Postgrad Med J. 2023 Jun 8;99(1171):423-427. doi: 10.1136/pmj-2022-142080.
To investigate whether sentiment analysis and topic modelling can be used to monitor the sentiment and opinions of junior doctors.
Retrospective observational study based on comments on a social media website.
Every publicly available comment in r/JuniorDoctorsUK on Reddit from 1 January 2018 to 31 December 2021.
7707 Reddit users who commented in the r/JuniorDoctorsUK subreddit.
Sentiment (scored -1 to +1) of comments compared with results of surveys conducted by the General Medical Council.
Average comment sentiment was positive but varied significantly during the study period. Fourteen topics of discussion were identified, each associated with a different pattern of sentiment. The topic with the highest proportion of negative comments was the role of a doctor (38%), and the topic with the most positive sentiment was hospital reviews (72%).
Some topics discussed in social media are comparable to those queried in traditional questionnaires, whereas other topics are distinctive and offer insight into what themes junior doctors care about. Events during the coronavirus pandemic may explain the sentiment trends in the junior doctor community. Natural language processing shows significant potential in generating insights into junior doctors' opinions and sentiment.
探讨情感分析和主题建模是否可用于监测初级医生的情绪和观点。
基于社交媒体网站评论的回顾性观察研究。
Reddit 上 r/JuniorDoctorsUK 中从 2018 年 1 月 1 日至 2021 年 12 月 31 日的所有公开评论。
在 r/JuniorDoctorsUK 子版块发表评论的 7707 名 Reddit 用户。
评论的情绪(评分为-1 到+1)与医学总会进行的调查结果进行比较。
平均评论情绪为阳性,但在研究期间变化显著。确定了 14 个讨论主题,每个主题都与不同的情绪模式相关。负面评论比例最高的主题是医生的角色(38%),而情绪最积极的主题是医院评价(72%)。
社交媒体中讨论的一些主题与传统问卷中查询的主题相似,而其他主题则独具特色,深入了解初级医生关注的主题。冠状病毒大流行期间的事件可能解释了初级医生群体中的情绪趋势。自然语言处理在生成初级医生意见和情绪的见解方面具有巨大潜力。