学术相关的压力因素预测研究生抑郁症状:一项机器学习研究
Aline F Bastos1, Orlando Fernandes-Jr2, Suzana P Liberal3
1Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil.
Behavioural brain research
|November 9, 2024
概括
在COVID-19大流行期间,研究生经历了抑郁症的增加. 机器学习确定了学术压力因素,如表现恐惧和财务问题,作为抑郁症状的关键预测因素.
科学领域:
- 心理健康 心理健康
- 机器学习 机器学习
- 公共卫生 公共卫生
背景情况:
- 在全球范围内,研究生报告了高抑郁率,这种情况因COVID-19大流行而恶化.
- 与学术相关的压力因素显著影响研究生心理健康.
研究的目的:
- 用学术相关压力因素预测研究生抑郁症状.
- 研究学术压力因素在预测心理健康结果中的作用.
主要方法:
- 使用了一种机器学习方法,具体来说是epsilon不敏感的支持向量回归 (Ɛ-SVR).
- 通过在线问卷评估学术压力因素和抑郁症状 (PHQ-9) 来收集来自巴西172名研究生的数据.
- 采用k倍 (k=5) 交叉验证策略,将性别,年龄和种族/种族作为共同变量.
主要成果:
- 机器学习模型成功地预测了抑郁症状,准确度很高 (r=0.51,R2=0.26,p=0.001).
- 导致预测的关键学术压力因素包括"对学业成绩的恐惧和担忧"",金融困难"",对学业进步和计划的恐惧和担忧"",对学术截止日期的恐惧和担忧".
结论:
- 研究生特别容易受到来自学术压力因素的抑郁症状的影响,特别是在流行病背景下.
- 这些发现支持使用人工智能来理解和应对这一群体的心理健康挑战.
- 结果可以为研究生制定有针对性的干预计划和公共卫生政策提供信息.
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