@文章{信息:doi/10.2196/33934，作者="Sukhera, Javeed and Ahmed, Hasan"，标题="利用机器学习了解情绪如何影响公平相关教育:半实验研究"，期刊="JMIR Med Educ"，年="2022"，月="3 "，日="30"，卷="8"，数="1"，页数="e33934"，关键词="偏见;股本;情绪分析;医学教育;情感;背景:由于偏见相关话语的情感本质，关于偏见等话题的教学和学习具有挑战性。然而，由于多种原因，情感在卫生专业教育中研究具有挑战性。随着机器学习和自然语言处理的出现，情感分析(SA)有可能弥补这一差距。目的:为了提高我们对情绪在偏见相关话语中的作用的理解，我们在卫生专业人员中开发并进行了偏见相关话语的SA。方法:采用两阶段准实验研究。 First, we developed a SA (algorithm) within an existing archive of interviews with health professionals about bias. SA refers to a mechanism of analysis that evaluates the sentiment of textual data by assigning scores to textual components and calculating and assigning a sentiment value to the text. Next, we applied our SA algorithm to an archive of social media discourse on Twitter that contained equity-related hashtags to compare sentiment among health professionals and the general population. Results: When tested on the initial archive, our SA algorithm was highly accurate compared to human scoring of sentiment. An analysis of bias-related social media discourse demonstrated that health professional tweets (n=555) were less neutral than the general population (n=6680) when discussing social issues on professionally associated accounts ($\chi$2 [2, n=555)]=35.455; P<.001), suggesting that health professionals attach more sentiment to their posts on Twitter than seen in the general population. Conclusions: The finding that health professionals are more likely to show and convey emotions regarding equity-related issues on social media has implications for teaching and learning about sensitive topics related to health professions education. Such emotions must therefore be considered in the design, delivery, and evaluation of equity and bias-related education. ", issn="2369-3762", doi="10.2196/33934", url="https://mededu.www.mybigtv.com/2022/1/e33934", url="https://doi.org/10.2196/33934", url="http://www.ncbi.nlm.nih.gov/pubmed/35353048" }