Liu Xin-Qiao, Guo Yu-Xin, Zhang Wen-Jie, Gao Wen-Juan
School of Education, Tianjin University, Tianjin 300350, China.
Graduate School of Education, Peking University, Beijing 100871, China.
World J Psychiatry. 2022 Jul 19;12(7):860-873. doi: 10.5498/wjp.v12.i7.860.
The high prevalence of depression among college students has a strong negative impact on individual physical and mental health, academic development, and interpersonal communication. This paper reviewed the extant literature by identifying nonpathological factors related to college students' depression, investigating the methods of predicting depression, and exploring nonpharmaceutical interventions for college students' depression. The influencing factors of college students' depression mainly fell into four categories: biological factors, personality and psychological state, college experience, and lifestyle. The outbreak of coronavirus disease 2019 has exacerbated the severity of depression among college students worldwide and poses grave challenges to the prevention and treatment of depression, given that the coronavirus has spread quickly with high infection rates, and the pandemic has changed the daily routines of college life. To predict and measure mental health, more advanced methods, such as machine algorithms and artificial intelligence, have emerged in recent years apart from the traditional commonly used psychological scales. Regarding nonpharmaceutical prevention measures, both general measures and professional measures for the prevention and treatment of college students' depression were examined in this study. Students who experience depressive disorders need family support and personalized interventions at college, which should also be supplemented by professional interventions such as cognitive behavioral therapy and online therapy. Through this literature review, we insist that the technology of identification, prediction, and prevention of depression among college students based on big data platforms will be extensively used in the future. Higher education institutions should understand the potential risk factors related to college students' depression and make more accurate screening and prevention available with the help of advanced technologies.
大学生抑郁症的高患病率对个体身心健康、学业发展和人际交往产生了强烈的负面影响。本文通过识别与大学生抑郁症相关的非病理因素、研究预测抑郁症的方法以及探索针对大学生抑郁症的非药物干预措施，对现有文献进行了综述。大学生抑郁症的影响因素主要分为四类：生物学因素、人格与心理状态、大学经历和生活方式。2019年冠状病毒病的爆发加剧了全球大学生抑郁症的严重程度，鉴于冠状病毒传播迅速、感染率高，且疫情改变了大学生活的日常规律，这对抑郁症的预防和治疗构成了严峻挑战。为了预测和测量心理健康状况，近年来除了传统常用的心理量表外，还出现了更先进的方法，如机器算法和人工智能。关于非药物预防措施，本研究考察了大学生抑郁症预防和治疗的一般措施和专业措施。患有抑郁症的学生在大学需要家庭支持和个性化干预，还应辅以认知行为疗法和在线治疗等专业干预。通过这篇文献综述，我们坚持认为基于大数据平台的大学生抑郁症识别、预测和预防技术在未来将得到广泛应用。高等教育机构应了解与大学生抑郁症相关的潜在风险因素，并借助先进技术进行更准确的筛查和预防。
