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在学术界的抑郁,焦虑和倦怠:PubMed摘要的主题建模
1Learning and Skill Analytics Research Group, TIB Leibniz Information Centre for Science and Technology, Hannover, Germany.
Frontiers in research metrics and analytics
|December 13, 2023
概括
这项研究使用BERTopic分析了学术心理健康文献,揭示了学生焦虑和居民倦怠的趋势. 它强调了学术界不断发展的研究领域.
科学领域:
- 圣经计量学和科学计量学
- 心理健康研究 心理健康研究
- 学术福利 学术福利
背景情况:
- 学术界越来越多的关于心理健康挑战的文献 (抑郁症,焦虑,倦怠).
- 需要先进的文本挖掘来从众多科学出版物中提取见解.
- 以前的方法可能缺乏复杂性来捕捉微妙的主题进化.
研究的目的:
- 将 BERTopic,一种先进的主题建模技术,应用于大量学术心理健康文献.
- 在学术界识别和可视化与抑郁症,焦虑症和倦怠症相关的关键话题.
- 分析这个领域内研究主题的时间演变.
主要方法:
- 使用BERTopic,这是一个模块化主题建模方法 (嵌入,缩小维度,聚类,加权).
- 分析了1975年至2023年间发表的2846篇PubMed文章的摘要.
- 根据可解释性,连贯性,多样性和异常比例,选择了一个包含27个主题的模型.
主要成果:
- 在学术心理健康的文献中确定了27个不同的话题.
- 观察到研究重点的时间变化,最近增加了关于与大流行有关的学生焦虑和医疗住院燃烧的研究.
- 指出,对心理测量方法和与互联网有关的心理健康问题的研究需要进一步扩展.
结论:
- BERTopic是分析学术界心理健康研究趋势的有效工具.
- 该研究提供了对该领域发展的全面概述,并确定了新兴和不足研究的领域.
- 结果可以指导未来的研究重点,以解决学术心理健康支持的关键差距.
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