目的:探究大学生学业压力与抑郁情绪的关系,为大学生心理健康提供有益参考。方法:使用具有全国代表性的2018年中国家庭追踪调查数据(CFPS2018),采用流调中心抑郁自评量表(CESD-8)测量在读大学生的抑郁情绪,并通过Logistic模型检验大学...目的:探究大学生学业压力与抑郁情绪的关系,为大学生心理健康提供有益参考。方法:使用具有全国代表性的2018年中国家庭追踪调查数据(CFPS2018),采用流调中心抑郁自评量表(CESD-8)测量在读大学生的抑郁情绪,并通过Logistic模型检验大学生学业压力与抑郁情绪的相关关系。结果:被调查大学生的抑郁检出率为18.65%。不同学习压力组的大学生抑郁情绪检出率存在显著差异(χ2 = 18.898, P = 0.001),学业压力最高和次高组中抑郁情绪检出率分别为42.11%和26.85%。大学生学业压力与情绪低落(r = 0.143, P Objective: To explore the relationship between academic stress and depression among college students and provide helpful references for their mental health. Methods: Using nationally representative data from the 2018 China Family Panel Studies (CFPS), the Center for Epidemiologic Studies Depression Scale (CESD-8) was used to measure depression among college students, and logistic regression models were used to test the relationship between academic stress and depression. Results: The detection rate of depression among college students was 18.65%. The detection rate of depression among different academic stress groups had a significant difference (χ2 = 18.898, P = 0.001), with 42.11% and 26.85% in the highest and second-highest academic stress groups, respectively. Academic stress was significantly correlated with low mood (r = 0.143, P < 0.05), difficulty in completing tasks (r = 0.165, P < 0.05), poor sleep (r = 0.135, P < 0.05), happiness (inverse indicator r = 0.114, P < 0.05), and sadness (r = 0.093, P < 0.05). Academic stress was a significant predictor of depression detection (B = 0.559;OR = 1.748, 95%CI: 1.259~2.428). Factors such as taking a nap (B = −0.550;OR = 0.577, 95%CI: 0.337~0.988), self-rated health (B = −0.345;OR = 0.708, 95%CI: 0.506~0.990), and self-rated excellence (B = −0.414;OR = 0.661, 95%CI: 0.419~1.043) were protective factors against depression among college students. Conclusion: The detection rate of depression among college students is high, and academic stress is a key predictor of depression. It is necessary to correctly relieve academic stress among college students and break the vicious cycle of academic stress leading to depression and then low academic performance.展开更多
文摘目的:探究大学生学业压力与抑郁情绪的关系,为大学生心理健康提供有益参考。方法:使用具有全国代表性的2018年中国家庭追踪调查数据(CFPS2018),采用流调中心抑郁自评量表(CESD-8)测量在读大学生的抑郁情绪,并通过Logistic模型检验大学生学业压力与抑郁情绪的相关关系。结果:被调查大学生的抑郁检出率为18.65%。不同学习压力组的大学生抑郁情绪检出率存在显著差异(χ2 = 18.898, P = 0.001),学业压力最高和次高组中抑郁情绪检出率分别为42.11%和26.85%。大学生学业压力与情绪低落(r = 0.143, P Objective: To explore the relationship between academic stress and depression among college students and provide helpful references for their mental health. Methods: Using nationally representative data from the 2018 China Family Panel Studies (CFPS), the Center for Epidemiologic Studies Depression Scale (CESD-8) was used to measure depression among college students, and logistic regression models were used to test the relationship between academic stress and depression. Results: The detection rate of depression among college students was 18.65%. The detection rate of depression among different academic stress groups had a significant difference (χ2 = 18.898, P = 0.001), with 42.11% and 26.85% in the highest and second-highest academic stress groups, respectively. Academic stress was significantly correlated with low mood (r = 0.143, P < 0.05), difficulty in completing tasks (r = 0.165, P < 0.05), poor sleep (r = 0.135, P < 0.05), happiness (inverse indicator r = 0.114, P < 0.05), and sadness (r = 0.093, P < 0.05). Academic stress was a significant predictor of depression detection (B = 0.559;OR = 1.748, 95%CI: 1.259~2.428). Factors such as taking a nap (B = −0.550;OR = 0.577, 95%CI: 0.337~0.988), self-rated health (B = −0.345;OR = 0.708, 95%CI: 0.506~0.990), and self-rated excellence (B = −0.414;OR = 0.661, 95%CI: 0.419~1.043) were protective factors against depression among college students. Conclusion: The detection rate of depression among college students is high, and academic stress is a key predictor of depression. It is necessary to correctly relieve academic stress among college students and break the vicious cycle of academic stress leading to depression and then low academic performance.
文摘本文提出了一种基于YOLOv8的元素定位方法,利用计算机视觉技术解决了传统方法的诸多局限。首先,把多梯度融合特征提取(channel-to-pixel,C2f)模块中的Bottleneck模块替换为新的动态蛇形卷积(dynamic snake convolution,DSConv)模块,其能够根据特征自适应学习感兴趣区域;其次,在主干网络增加SE通道注意力机制,能够感知主要目标图像特征;最后,使用Wise-IoU损失函数替代原先的交并比(intersection over union,IoU)损失函数,从而克服梯度消失的问题。通过消融和对比实验,与基线模型相比在精确度、F_(1)分数和mAP@0.5分别提升了6.7%、9.7%、7.8%,在应用进行元素定位时准确率均能获得明显的提升。
