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.

中文翻译：

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确定有助于退伍军人退役后福祉的因素

先前的研究已经检查了人口统计学和军事特征、创伤史和应对资源对退伍军人健康的独立影响。然而，对于这些因素如何相互影响以及如何与退伍军人的健康相互作用以影响他们重新适应平民生活时更广泛的福祉，我们知之甚少。这项研究的数据来自对美国退伍军人健康和更广泛福祉的纵向调查（N = 7150) 最近刚退伍的人。机器学习分析（回归树的随机森林）用于检查军事分离后不久评估的因素如何与大约一年后退伍军人的福祉相关联。支持低抑郁、高社会支持和高心理弹性相结合的退伍军人最有可能在一年后报告高幸福感。在其他因素的背景下考虑时，人口统计学和军事特征以及创伤史都不是退伍军人幸福感的有力预测因素。尽管女性和男性的大多数预测因子相似，但抑郁症是女性幸福感的更强预测因子。结果强调了筛查和干预那些报告高度抑郁、社会支持低、退伍时心理弹性低。这些发现可以为促进退伍军人退役后福利的努力提供信息。