JOURNAL ARTICLE
Hope, optimism, and self‐efficacy predicting mental health and illness in a community sample exposed to Hurricane Harvey.
Published In: Journal of Community Psychology, 2023, v. 51, n. 7. P. 2774 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: D'Souza, Johann M.; Long, Laura J.; Richardson, Angela L.; Gallagher, Matthew W. 3 of 3
Abstract
In 2017, Hurricane Harvey flooded more than 300,000 buildings causing an estimated $125 billion in damages and resulting in 68 deaths (National Hurricane Center). This actual or threatened loss of life and physical harm led many to report negative effects on mental well‐being and greater mental illness. However, many individuals have been able to experience similar adverse events without a significant negative impact on their mental health and well‐being. Positive thinking factors such as hope, optimism, and self‐efficacy have been proposed as protective factors in the face of difficult life events. Hope, optimism, and self‐efficacy are related but distinct constructs that have often been studied separately, but whose unique impact on well‐being and mental illness is less clear, especially in the context of a natural hazard. The current study uses structural equation modeling to measure the unique contribution of hope, optimism, and hurricane‐coping self‐efficacy on mental well‐being and mental illness in a community sample of 300 subjects who experienced Hurricane Harvey, recruited from Mechanical Turk. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Community Psychology. 2023/09, Vol. 51, Issue 7, p2774
- Document Type:Article
- Subject Area:Earth and Atmospheric Sciences
- Publication Date:2023
- ISSN:0090-4392
- DOI:10.1002/jcop.23075
- Accession Number:169851420
- Copyright Statement:Copyright of Journal of Community Psychology is the property of Wiley-Blackwell 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.)
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