Author
Listed:
- Qi Zhou
- Tao Zhou
- Jing Huang
- Yuling Lei
- Guangxin Xu
- Yuting Yang
- Yanbing Shen
- Yueli Zhu
Abstract
Background: Professional identity plays a critical role in the career development of male postgraduate nursing students, particularly in contexts where gender imbalance and social stereotypes persist. Objective: This study explores how the clinical professional identity of male nursing postgraduates is perceived and constructed through social media discourse in China. Design: A qualitative study using content analysis of social media discourse, supported by sentiment classification and clustering algorithms. Methods: Online 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. Results: Initially, 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. Conclusion: Public 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.
Suggested Citation
Qi Zhou & Tao Zhou & Jing Huang & Yuling Lei & Guangxin Xu & Yuting Yang & Yanbing Shen & Yueli Zhu, 2025.
"The public’s perceptions and attitudes of male nursing postgraduate professional identity on Chinese social media: Qualitative study based on machine learning,"
PLOS ONE, Public Library of Science, vol. 20(9), pages 1-14, September.
Handle:
RePEc:plo:pone00:0331379
DOI: 10.1371/journal.pone.0331379
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