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Monitoring real-time junior doctor sentiment from comments on a public social media platform: a retrospective observational study.
Sirkis, Tamir; Maitland, Stuart.
  • Sirkis T; College of Medicine and Health, University of Exeter, Exeter, UK.
  • Maitland S; Translational Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK.
Postgrad Med J ; 99(1171): 423-427, 2023 Jun 08.
Article in English | MEDLINE | ID: covidwho-20243037
ABSTRACT

OBJECTIVES:

To investigate whether sentiment analysis and topic modelling can be used to monitor the sentiment and opinions of junior doctors.

DESIGN:

Retrospective observational study based on comments on a social media website.

SETTING:

Every publicly available comment in r/JuniorDoctorsUK on Reddit from 1 January 2018 to 31 December 2021.

PARTICIPANTS:

7707 Reddit users who commented in the r/JuniorDoctorsUK subreddit. MAIN OUTCOME

MEASURE:

Sentiment (scored -1 to +1) of comments compared with results of surveys conducted by the General Medical Council.

RESULTS:

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%).

CONCLUSION:

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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Coronavirus Infections / Social Media Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: Postgrad Med J Year: 2023 Document Type: Article Affiliation country: Pmj-2022-142080

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Coronavirus Infections / Social Media Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: Postgrad Med J Year: 2023 Document Type: Article Affiliation country: Pmj-2022-142080