疫情后期青少年的抑郁和焦虑变迁趋势及其潜在因果：一项追踪研究

doi:10.3724/SP.J.1041.2024.00482

摘要/Abstract

摘要：

在新冠疫情初期, 青少年抑郁、焦虑问题急剧恶化, 而疫情后期青少年抑郁、焦虑的发展状况尚不清晰。结合我国疫情发展趋势, 本研究采用流调中心抑郁量表、焦虑自评量表、心理韧性量表、亲社会倾向量表收集了疫情后期我国1170名中学生三次数据(T1 = 2021年5月; T2 = 2021年11月; T3 = 2022年5月), 并借助潜在增长曲线模型对疫情后期青少年抑郁、焦虑的变化轨迹、性别差异及其潜在因果进行探讨。结果表明, 疫情后期青少年的抑郁、焦虑水平逐渐下降, 且男生和女生的变化轨迹无显著差异; 心理韧性对抑郁、焦虑的初始水平及其变化轨迹均有显著的预测作用, 即T1的心理韧性水平越高, T1的抑郁与焦虑水平越低, 此后抑郁和焦虑的下降速度也越快。此外, 青少年抑郁的初始水平及其变化趋势均能够负向预测T3的亲社会倾向, 即T1抑郁水平越高, T3亲社会倾向越低; 抑郁下降速度越快, T3亲社会倾向越高。总之, 在疫情后期, 我国青少年心理健康变化呈向好趋势, 这一发现可为青少年的心理健康服务体系提供经验证据。

关键词: 青少年, 抑郁, 焦虑, 心理韧性, 亲社会倾向

Abstract:

During the COVID-19 pandemic, all age groups’ mental health has substantially declined. Compared to other age groups, the impact of the COVID-19 pandemic on adolescent mental health problems was more salient, particularly on depression and anxiety. Previous studies, which were primarily conducted during the phase of COVID-19 pandemic, have mainly covered the first and second phases of COVID-19-related prevention and containment in China, without adequate attention being paid to the third and fourth phases of COVID-19-related prevention and containment in China. Currently, there are three competing hypotheses for the longitudinal trajectories of depression and anxiety among Chinese adolescents in the post-COVID era, making the construction of the mental health monitoring system and the mental health service system for adolescents in China “blind spots”. Based on developmental contextualism and three-dimensional theoretical framework of mental health change, the present study thus investigated the developmental trajectories of depression and anxiety among adolescents, as well as the potential antecedent (psychological resilience) and outcome (prosocial tendency) of such trajectories in the late phase of the COVID-19 pandemic in China.

In this study, 1170 adolescents in Shandong province were assessed at three time points with a six-month interval (Time 1 = May 2021; Time 2 = November 2021; Time 3 = May 2022) from the normalization period of the pandemic through self-report questionnaires. The adolescents completed Center for Epidemiologic Studies Depression Scale and Self-Rating Anxiety Scale during the three measurements. Furthermore, all adolescents completed the Connor-Davidson Resilience Scale at Time 1 and Prosocial Tendencies Measure at Time 3. Attrition analyses, common method bias analyses, and descriptive statistics were analyzed by SPSS software. The trajectories of depression and anxiety, as well as their associated antecedent (i.e., psychological resilience) and outcome (i.e., prosocial tendency) were further analyzed via latent growth curve models using Mplus software.

According to the results of the latent growth curve models, adolescents’ depression and anxiety levels tended to decline. The higher the level of depression and anxiety at Time 1, the slower the decline over time. Furthermore, psychological resilience at Time 1 negatively predicted initial levels of depression and anxiety but positively predicted the decreasing speed of depression and anxiety trajectories. Concerning the outcomes, we found that the initial level and speed of depression trajectories significantly and negatively predicted the levels of prosocial tendency at Time 3. Regarding the anxiety trajectory, its intercept factor negatively predicted prosocial tendency at Time 3, but its slope factor’s predictive effect was non-significant. Finally, multi-group analyses revealed that both boys and girls showed declining trends in depression and anxiety over time, while girls scored significantly higher on depression and anxiety initially than boys.

In conclusion, this research found that the changes in mental health (particularly depression and anxiety problems) among Chinese adolescents in the late phase of the COVID-19 pandemic were developing well, which provides empirical evidence for better construction of adolescent mental health service systems in China.

Key words: adolescents, depression, anxiety, psychological resilience, prosocial tendency

中图分类号:

R395

杨之旭, 彭海云, 辛素飞. (2024). 疫情后期青少年的抑郁和焦虑变迁趋势及其潜在因果：一项追踪研究. 心理学报, 56(4), 482-496.

YANG Zhixu, PENG Haiyun, XIN Sufei. (2024). A longitudinal study on depression and anxiety among Chinese adolescents in the late phase of the COVID-19 pandemic: The trajectories, antecedents, and outcomes. Acta Psychologica Sinica, 56(4), 482-496.

图/表 6

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变量	人数	百分比(%)	变量	人数	百分比(%)
性别			大专	110	9.4
男生	558	47.7	本科	77	6.6
女生	612	52.3	研究生	8	0.7
年级			母亲学历
初一	682	58.3	高中及以下	984	84.5
初二	381	32.6	大专	117	10.0
高一	107	9.1	本科	57	4.9
居住地			研究生	7	0.6
城市	921	80.5	家庭月收入
农村	223	19.5	5000元以下	328	28.2
父亲学历			5000~10000元	656	56.5
高中及以下	972	83.3	10000元以上	178	15.3

变量	人数	百分比(%)	变量	人数	百分比(%)
性别			大专	110	9.4
男生	558	47.7	本科	77	6.6
女生	612	52.3	研究生	8	0.7
年级			母亲学历
初一	682	58.3	高中及以下	984	84.5
初二	381	32.6	大专	117	10.0
高一	107	9.1	本科	57	4.9
居住地			研究生	7	0.6
城市	921	80.5	家庭月收入
农村	223	19.5	5000元以下	328	28.2
父亲学历			5000~10000元	656	56.5
高中及以下	972	83.3	10000元以上	178	15.3

变量	1	2	3	4	5	6	7	8
1. 抑郁(T1)	—
2. 抑郁(T2)	0.63	—
3. 抑郁(T3)	0.53	0.69	—
4. 焦虑(T1)	0.80	0.60	0.50	—
5. 焦虑(T2)	0.57	0.84	0.63	0.61	—
6. 焦虑(T3)	0.50	0.65	0.81	0.52	0.68	—
7. 心理韧性(T1)	−0.51	−0.31	−0.29	−0.40	−0.26	−0.23	—
8. 亲社会倾向(T3)	−0.18	−0.19	−0.22	−0.17	−0.15	−0.18	0.30	—
M	1.85	1.76	1.65	1.84	1.76	1.66	3.47	3.45
SD	0.55	0.60	0.58	0.47	0.47	0.44	0.63	0.78

变量	1	2	3	4	5	6	7	8
1. 抑郁(T1)	—
2. 抑郁(T2)	0.63	—
3. 抑郁(T3)	0.53	0.69	—
4. 焦虑(T1)	0.80	0.60	0.50	—
5. 焦虑(T2)	0.57	0.84	0.63	0.61	—
6. 焦虑(T3)	0.50	0.65	0.81	0.52	0.68	—
7. 心理韧性(T1)	−0.51	−0.31	−0.29	−0.40	−0.26	−0.23	—
8. 亲社会倾向(T3)	−0.18	−0.19	−0.22	−0.17	−0.15	−0.18	0.30	—
M	1.85	1.76	1.65	1.84	1.76	1.66	3.47	3.45
SD	0.55	0.60	0.58	0.47	0.47	0.44	0.63	0.78

轨迹	全样本	男生	女生
抑郁
截距均值	1.86^***[1.83, 1.89]	1.80^***[1.76, 1.84]	1.91^***[1.86, 1.95]
斜率均值	−0.10^***[−0.12, −0.09]	−0.12^***[−0.14, −0.09]	−0.09^***[−0.11, −0.07]
截距和斜率的相关	−0.34^*** [−0.43, −0.25]	−0.28^***[−0.43, −0.13]	−0.38^***[−0.50, −0.27]
焦虑
截距均值	1.84^***[1.82, 1.87]	1.79^***[1.75, 1.83]	1.89^***[1.85, 1.92]
斜率均值	−0.09^***[−0.10, −0.08]	−0.10^***[−0.12, −0.08]	−0.08^***[−0.10, −0.06]
截距和斜率的相关	−0.37^***[−0.47, −0.27]	−0.39^***[−0.53, −0.25]	−0.36^***[−0.51, −0.21]