干预(咨询)
压力源
心理学
压力(语言学)
焦虑
心理干预
脆弱性(计算)
临床心理学
发展心理学
精神科
计算机科学
计算机安全
语言学
哲学
作者
David Scott Yeager,Christopher J. Bryan,James J. Gross,Jared S. Murray,Danielle Krettek Cobb,Pedro Henrique Flores dos Santos,Hannah Gravelding,Meghann Johnson,Jeremy P. Jamieson
出处
期刊:Nature
[Nature Portfolio]
日期:2022-07-06
卷期号:607 (7919): 512-520
被引量:282
标识
DOI:10.1038/s41586-022-04907-7
摘要
Social stress poses a major threat to adolescent health via its effects on internalizing symptoms, such as anxiety and depression. Available interventions to help adolescents improve their stress responses, however, have not been effective in rigorous evaluation studies, or they have been difficult to administer widely. Here we show that replicable improvements in adolescent stress responses can be achieved with a short (~30-minute), scalable synergistic mindsets intervention. This intervention, which is a self-administered online training module, targets both growth mindsets (the idea that people's intelligence can be developed in response to challenge) and stress-can-be-enhancing mindsets (the idea that people's stress responses can be fuel for optimal performance). Its goal is to promote positive engagement with stressful events (e.g., learning from failure on a quiz or a conflict with a peer) and to encourage adolescents to use their responses to stressful events and even their bodily symptoms (e.g. racing heart, sweaty palms, butterflies in their stomach) to their advantage. In five double-blind, randomized, controlled trials (total N = 4,091 adolescents), the new synergistic mindsets intervention improved stress-related cognitions (Studies 1-2), cardiovascular reactivity (Study 3), daily internalizing symptoms and cortisol levels (Study 4), and generalized anxiety symptoms during the 2020 COVID-19 lockdowns (Study 5). Effects on downstream outcomes (in Studies 3-5) were stronger among individuals who, at baseline, held the two negative mindsets targeted by the intervention, providing evidence for the proposed mechanisms. Confidence in this conclusion comes from a conservative, Bayesian machine-learning method for detecting heterogeneity.
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