Nursing Students' Perceptions of AI-Driven Mental Health Support and Its Relationship with Anxiety, Depression, and Seeking Professional Psychological Help: Transitioning from Traditional Counseling to Digital Support

护理专业学生对人工智能驱动的心理健康支持的看法及其与焦虑、抑郁和寻求专业心理帮助的关系:从传统咨询过渡到数字化支持

阅读:1

Abstract

Background: The integration of artificial intelligence (AI) into mental health care is reshaping psychological support systems, particularly for digitally literate populations such as nursing students. Given the high prevalence of anxiety and depression in this group, understanding their perceptions of AI-driven mental health support is critical for effective implementation. Objectives: to evaluate nursing students' perceptions toward AI-driven mental health support and examine its relationship with anxiety, depression, and their attitudes to seeking professional psychological help. Methods: A cross-sectional survey was conducted among 176 undergraduate nursing students in northern Jordan. Results: Students reported moderately positive perceptions toward AI-driven mental health support (mean score: 36.70 ± 4.80). Multiple linear regression revealed that prior use of AI tools (β = 0.44, p < 0.0001), positive help-seeking attitudes (β = 0.41, p < 0.0001), and higher levels of psychological distress encompassing both anxiety (β = 0.29, p = 0.005) and depression (β = 0.24, p = 0.007) significantly predicted more positive perceptions. Daily AI usage was not a significant predictor (β = 0.15, p = 0.174). Logistic regression analysis further indicated that psychological distress, reflected by elevated anxiety (OR = 1.42, p = 0.002) and depression scores (OR = 1.32, p = 0.003), along with stronger help-seeking attitudes (OR = 1.35, p = 0.011), significantly increased the likelihood of using AI-based mental health support. Conclusions: AI-driven mental health tools hold promises as adjuncts to traditional counseling, particularly for nursing students experiencing psychological distress. Despite growing acceptance, concerns regarding data privacy, bias, and lack of human empathy remain. Ethical integration and blended care models are essential for effective mental health support.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。