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• W2891656095 abstract "Although misuse of social networking sites, particularly Twitter, has occurred, little is known about the prevalence, content, and characteristics of uncivil tweets posted by nurses and nursing students. The aim of this study was to describe the characteristics of tweets posted by nurses and nursing students on Twitter with a focus on cyberincivility. A cross-sectional, data-mining study was held from February through April 2017. Using a data-mining tool, we extracted quantitative and qualitative data from a sample of 163 self-identified nurses and nursing students on Twitter. The analysis of 8934 tweets was performed by a combination of SAS 9.4 for descriptive and inferential statistics including logistic regression and NVivo 11 to derive descriptive patterns of unstructured textual data. We categorized 413 tweets (4.62%, n = 8934) as uncivil. Of these, 240 (58%) were related to nursing and the other 173 (42%) to personal life. Of the 163 unique users, 60 (36.8%) generated those 413 uncivil posts, tweeting inappropriately at least once over a period of six weeks. Most uncivil tweets contained profanity (n = 135, 32.7%), sexually explicit or suggestive material (n = 37, 9.0%), name-calling (n = 14, 3.4%), and discriminatory remarks against minorities (n = 9, 2.2%). Other uncivil content included product promotion, demeaning comments toward patients, aggression toward health professionals, and HIPAA violations. Nurses and nursing students share uncivil tweets that could tarnish the image of the profession and violate codes of ethics. Individual, interpersonal, and institutional efforts should be made to foster a culture of cybercivility." @default.
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• W2891656095 date "2019-01-01" @default.
• W2891656095 modified "2023-10-13" @default.
• W2891656095 title "Uncovering cyberincivility among nurses and nursing students on Twitter: A data mining study" @default.
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• W2891656095 doi "https://doi.org/10.1016/j.ijnurstu.2018.09.009" @default.
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