five

The result of the content analysis.

收藏
Figshare2025-09-04 更新2026-04-28 收录
下载链接:
https://figshare.com/articles/dataset/The_result_of_the_content_analysis_/30054962
下载链接
链接失效反馈
官方服务:
资源简介:
BackgroundProfessional identity plays a critical role in the career development of male postgraduate nursing students, particularly in contexts where gender imbalance and social stereotypes persist.ObjectiveThis study explores how the clinical professional identity of male nursing postgraduates is perceived and constructed through social media discourse in China.DesignA qualitative study using content analysis of social media discourse, supported by sentiment classification and clustering algorithms.MethodsOnline comments related to male nursing postgraduates were extracted from Weibo and Zhihu. The data search was conducted from 2020 to 2023. This study was divided into five steps: data acquisition, data cleaning, statistical analysis, sentiment analysis, and topic analysis. Sentiment analysis was performed using a lexicon-enhanced rule-based model. Topic analysis was conducted using unsupervised machine learning.ResultsInitially, 7,483 comments were collected. After cleaning, 5,692 valid comments totaling 486,366 words were retained for analysis. The sentiment distribution showed 64.3% were negative, 21.5% neutral, and 14.2% positive. Topic modeling revealed six main themes: identity confusion, gender role conflict, lack of clinical recognition, professional value affirmation, social support, and resistance to stereotypes.ConclusionPublic discourse reflects both affirmation and marginalization of male postgraduate nurses in China. These perceptions shape their clinical professional identity and influence their sense of belonging and future career planning. Interventions in education and media strategies are necessary to promote inclusive and supportive identity development.

研究背景:专业认同在男性护理研究生的职业发展中发挥着至关重要的作用,在性别比例失衡与社会刻板印象仍普遍存在的语境下尤为如此。 研究目的:本研究旨在探讨中国语境下,男性护理研究生的临床专业认同是如何通过社交媒体话语被感知与建构的。 研究设计:本研究为质性研究,采用社交媒体话语内容分析法,并辅以情感分类与聚类算法进行支撑。 研究方法:从微博(Weibo)与知乎(Zhihu)平台爬取与男性护理研究生相关的在线评论,数据采集时段为2020年至2023年。本研究共分为五个步骤:数据获取、数据清洗、统计分析、情感分析与主题分析。其中情感分析采用基于词典增强的规则化模型完成,主题分析则借助无监督机器学习方法开展。 研究结果:初始共采集到7483条评论,经清洗后保留5692条有效评论,总字数达486366词。情感分布结果显示,64.3%的评论为负面情感,21.5%为中性情感,14.2%为正面情感。主题建模共识别出六大核心主题:认同困惑、性别角色冲突、临床认可度不足、专业价值肯定、社会支持以及对刻板印象的抵制。 研究结论:中国的公共话语既体现了对男性护理研究生的认同肯定,也反映出其被边缘化的处境。此类认知会塑造其临床专业认同,并影响其归属感与未来职业规划。因此,有必要通过教育干预与媒体策略优化,助力其形成兼具包容性与支持性的专业认同发展。
创建时间:
2025-09-04
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作