Published on in Vol 23, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30217, first published .
The State of Mind of Health Care Professionals in Light of the COVID-19 Pandemic: Text Analysis Study of Twitter Discourses

The State of Mind of Health Care Professionals in Light of the COVID-19 Pandemic: Text Analysis Study of Twitter Discourses

The State of Mind of Health Care Professionals in Light of the COVID-19 Pandemic: Text Analysis Study of Twitter Discourses

Journals

  1. Rapaport E, Poese I, Zilberman P, Holschke O, Puzis R. Spillover Today? Predicting Traffic Overflows on Private Peering of Major Content Providers. IEEE Transactions on Network and Service Management 2021;18(4):4169 View
  2. Mele B, Holroyd-Leduc J, Harasym P, Dumanski S, Fiest K, Graham I, Nerenberg K, Norris C, Parsons Leigh J, Pilote L, Pruden H, Raparelli V, Rabi D, Ruzycki S, Somayaji R, Stelfox H, Ahmed S. Healthcare workers’ perception of gender and work roles during the COVID-19 pandemic: a mixed-methods study. BMJ Open 2021;11(12):e056434 View
  3. Xavier T, Lambert J. Sentiment and emotion trends in nurses' tweets about the COVID‐19 pandemic. Journal of Nursing Scholarship 2022;54(5):613 View
  4. Rossettini G, Peressutti V, Visintini E, Fontanini R, Caruzzo D, Longhini J, Palese A. Italian nurses’ experiences of the COVID-19 pandemic through social media: A longitudinal mixed methods study of Internet posts. DIGITAL HEALTH 2022;8:205520762211290 View
  5. Bouchacourt L, Henson-García M, Sussman K, Mandell D, Wilcox G, Mackert M. Web-Based Conversations Regarding Fathers Before and During the COVID-19 Pandemic: Qualitative Content Analysis. JMIR Pediatrics and Parenting 2023;6:e40371 View
  6. Slobodin O, Plochotnikov I, Cohen I, Elyashar A, Cohen O, Puzis R. Global and Local Trends Affecting the Experience of US and UK Healthcare Professionals during COVID-19: Twitter Text Analysis. International Journal of Environmental Research and Public Health 2022;19(11):6895 View
  7. Axelrad-Levy T, Schwartz Tayri T, Achdut N, Sarid O. The Perceived Job Performance of Child Welfare Workers During the COVID-19 Pandemic. Clinical Social Work Journal 2023;51(2):175 View
  8. Leung P, Li S, Holroyd E, Tsang C, Wong W. Online social media poses opportunities and risks in autistic youth: implications for services from a qualitative study. Frontiers in Psychiatry 2023;14 View
  9. Weerasinghe S, Oyebode O, Orji R, Matwin S. Dynamics of emotion trends in Canadian Twitter users during COVID-19 confinement in relation to caseloads: Artificial intelligence-based emotion detection approach. DIGITAL HEALTH 2023;9:205520762311714 View
  10. Zhang Y, Chen F, Suk J, Yue Z. WordPPR: A Researcher-Driven Computational Keyword Selection Method for Text Data Retrieval from Digital Media. Communication Methods and Measures 2023:1 View
  11. Li W, Tang L, Montayre J, Harris C, West S, Antoniou M. Investigating Health and Well-Being Challenges Faced by an Aging Workforce in the Construction and Nursing Industries: Computational Linguistic Analysis of Twitter Data. Journal of Medical Internet Research 2024;26:e49450 View
  12. Li Y, Chen M, Lee H. Health communication on social media at the early stage of the pandemic: Examining health professionals’ COVID-19 related tweets. Social Science & Medicine 2024;347:116748 View
  13. Jessiman-Perreault G, Boucher J, Kim S, Frenette N, Badami A, Smith H, Allen Scott L. The Role of Scientific Research in Human Papillomavirus Vaccine Discussions on Twitter: Social Network Analysis. JMIR Infodemiology 2024;4:e50551 View
  14. Paradise-Vit A, Elyashar A, Aronson Y. Automated photo filtering for tourism domain using deep and active learning: the case of Israeli and worldwide cities on instagram. Information Technology & Tourism 2024;26(3):553 View
  15. Levin C, Naimi E, Saban M. Evaluating GenAI systems to combat mental health issues in healthcare workers: An integrative literature review. International Journal of Medical Informatics 2024;191:105566 View
  16. Paradise Vit A, Magid A. Differences in Fear and Negativity Levels Between Formal and Informal Health-Related Websites: Analysis of Sentiments and Emotions. Journal of Medical Internet Research 2024;26:e55151 View
  17. Deiner M, Honcharov V, Li J, Mackey T, Porco T, Sarkar U. Large Language Models Can Enable Inductive Thematic Analysis of a Social Media Corpus in a Single Prompt: Human Validation Study. JMIR Infodemiology 2024;4:e59641 View