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Created
January 02, 2025 13:06
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meh302
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Updated
January 02, 2025 13:06
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[unknown user]
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Added Creator Dawne Vogt
January 02, 2025 13:06
by
meh302
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Added Creator Matthew W. King
January 02, 2025 13:06
by
meh302
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Added Creator Shelby Borowski
January 02, 2025 13:06
by
meh302
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Added Creator Erin P. Finley
January 02, 2025 13:06
by
meh302
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Added Creator Daniel F. Perkins
January 02, 2025 13:06
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meh302
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Added Creator Laurel A. Copeland
January 02, 2025 13:06
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meh302
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Updated
Work Title, Keyword, Publisher, and 3 more
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January 02, 2025 13:06
by
meh302
Work Title
Identifying factors that contribute to military veterans' post-military well-being
- Identifying factors that contribute to military veterans’ post-military well-being
Keyword
- Veterans, Military Veterans, Well Being, Military, World Health Organization, Coping Behavior, Trauma History, Veteran Well Being, Strong Predictor, Veterans Health, Demography, Wounds And Injuries, Social Support, Psychological Resilience, Resilience, Military Service, Trauma, Military Separation, Random Forest, Regression Tree, Longitudinal Investigation, Coping Resources, Machine Learning Analysis, Civilian Life, Women's Depression, Us Veterans, Forests, Military Health, Health, Machine Learning, Learning, Resources, Regression, Coping
Publisher
- Applied Psychology: Health and Well-Being
Publisher Identifier (DOI)
Description
- <p>Prior research has examined the independent effects of demographic and military characteristics, trauma history, and coping resources on military veterans’ health. However, there is limited knowledge of how these factors intersect with one another and with veterans’ health to impact their broader well-being as they readjust to civilian life. Data for this study were drawn from a longitudinal investigation of the health and broader well-being of U.S. veterans (N = 7150) who had recently left military service. Machine learning analyses (random forests of regression trees) were used to examine how factors assessed shortly after military separation were associated with veterans’ well-being approximately a year later. Veterans who endorsed the combination of low depression, high social support, and high psychological resilience were most likely to report high well-being a year later. Neither demographic and military characteristics nor trauma history emerged as strong predictors of veterans’ well-being when considered in the context of other factors. Although most predictors were similar for women and men, depression was a stronger predictor of women's well-being. Results highlight the importance of screening for and intervening with veterans who report high depression, low social support, and low psychological resilience when leaving military service. These findings can inform efforts to promote veterans’ post-military well-being.</p>
Publication Date
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Updated
January 02, 2025 13:06
by
meh302
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January 02, 2025 13:06
by
meh302
Description
<p>Prior research has examined the independent effects of demographic and military characteristics, trauma history, and coping resources on military veterans’ health. However, there is limited knowledge of how these factors intersect with one another and with veterans’ health to impact their broader well-being as they readjust to civilian life. Data for this study were drawn from a longitudinal investigation of the health and broader well-being of U.S. veterans (N = 7150) who had recently left military service. Machine learning analyses (random forests of regression trees) were used to examine how factors assessed shortly after military separation were associated with veterans’ well-being approximately a year later. Veterans who endorsed the combination of low depression, high social support, and high psychological resilience were most likely to report high well-being a year later. Neither demographic and military characteristics nor trauma history emerged as strong predictors of veterans’ well-being when considered in the context of other factors. Although most predictors were similar for women and men, depression was a stronger predictor of women's well-being. Results highlight the importance of screening for and intervening with veterans who report high depression, low social support, and low psychological resilience when leaving military service. These findings can inform efforts to promote veterans’ post-military well-being.</p>
- Prior research has examined the independent effects of demographic and military characteristics, trauma history, and coping resources on military veterans’ health. However, there is limited knowledge of how these factors intersect with one another and with veterans’ health to impact their broader well-being as they readjust to civilian life. Data for this study were drawn from a longitudinal investigation of the health and broader well-being of U.S. veterans (N = 7150) who had recently left military service. Machine learning analyses (random forests of regression trees) were used to examine how factors assessed shortly after military separation were associated with veterans’ well-being approximately a year later. Veterans who endorsed the combination of low depression, high social support, and high psychological resilience were most likely to report high well-being a year later. Neither demographic and military characteristics nor trauma history emerged as strong predictors of veterans’ well-being when considered in the context of other factors. Although most predictors were similar for women and men, depression was a stronger predictor of women's well-being. Results highlight the importance of screening for and intervening with veterans who report high depression, low social support, and low psychological resilience when leaving military service. These findings can inform efforts to promote veterans’ post-military well-being.
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Updated Creator Dawne Vogt
January 02, 2025 13:07
by
meh302
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Updated Creator Matthew W. King
January 02, 2025 13:07
by
meh302
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Updated Creator Shelby Borowski
January 02, 2025 13:07
by
meh302
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Updated Creator Erin P. Finley
January 02, 2025 13:07
by
meh302
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Updated Creator Daniel F. Perkins
January 02, 2025 13:07
by
meh302
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Updated Creator Laurel A. Copeland
January 02, 2025 13:07
by
meh302
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Added
identify factors13May2021.pdf
January 02, 2025 13:07
by
meh302
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January 02, 2025 13:07
by
meh302
License
- https://creativecommons.org/licenses/by/4.0/
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Published
January 02, 2025 13:07
by
meh302
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Updated
January 02, 2025 21:04
by
[unknown user]