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Trait anxiety predicts the emotional valence of meaning-making in life stories: A 10-year prospective study.

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Title: Trait anxiety predicts the emotional valence of meaning-making in life stories: A 10-year prospective study.
Authors: Thomsen, Dorthe Kirkegaard1,2 dorthe.thomsen@psy.au.dk, Matthiesen, Signe1,3, Frederiksen, Yoon1,3, Ingerslev, Hans Jakob4, Zachariae, Robert1,3, Mehlsen, Mimi Yung1,3
Source: Personality & Individual Differences. Nov2016, Vol. 102, p51-55. 5p.
Subject Terms: *Emotions, *Anxiety, *Mental depression, *Longitudinal method, Well-being, Path analysis (Statistics)
Abstract: Individuals interpret events in positive and negative ways, creating positive and negative meaning in their life stories. We hypothesized that higher trait anxiety measured 10 years earlier would predict more negative meaning-making in life stories and that negative meaning-making would be related to more concurrent depressive symptoms. Participants were 272 women who had undergone fertility treatment and completed measures of trait anxiety 10 years earlier. In the present study, they described five life story events and rated these on emotional tone and meaning-making. A path analysis showed that higher trait anxiety predicted more negative meaning-making 10 years later and that negative meaning-making predicted more concurrent depressive symptoms after adjusting for baseline depressive symptoms, trait anxiety, and education. The results suggest that life stories are important to well-being. [ABSTRACT FROM AUTHOR]
Copyright of Personality & Individual Differences is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Trait anxiety predicts the emotional valence of meaning-making in life stories: A 10-year prospective study.
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  Data: *<searchLink fieldCode="DE" term="%22Emotions%22">Emotions</searchLink><br />*<searchLink fieldCode="DE" term="%22Anxiety%22">Anxiety</searchLink><br />*<searchLink fieldCode="DE" term="%22Mental+depression%22">Mental depression</searchLink><br />*<searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Well-being%22">Well-being</searchLink><br /><searchLink fieldCode="DE" term="%22Path+analysis+%28Statistics%29%22">Path analysis (Statistics)</searchLink>
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  Data: Individuals interpret events in positive and negative ways, creating positive and negative meaning in their life stories. We hypothesized that higher trait anxiety measured 10 years earlier would predict more negative meaning-making in life stories and that negative meaning-making would be related to more concurrent depressive symptoms. Participants were 272 women who had undergone fertility treatment and completed measures of trait anxiety 10 years earlier. In the present study, they described five life story events and rated these on emotional tone and meaning-making. A path analysis showed that higher trait anxiety predicted more negative meaning-making 10 years later and that negative meaning-making predicted more concurrent depressive symptoms after adjusting for baseline depressive symptoms, trait anxiety, and education. The results suggest that life stories are important to well-being. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Personality & Individual Differences is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.paid.2016.06.059
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