Dell Nathaniel A, Mancini Michael, Vaughn Michael G, Maynard Brandy R, Huang Jin
is assistant professor, Department of Psychiatry, School of Medicine, Washington University in St. Louis, 660 South Euclid Avenue, Saint Louis, MO 63110, USA.
is associate professor, School of Social Work, Saint Louis University, Saint Louis, MO, USA.
Soc Work Res. 2025 Apr 3;49(2):119-130. doi: 10.1093/swr/svaf004. eCollection 2025 Jun.
This study distinguishes clinically and theoretically meaningful subgroups of people experiencing homelessness based on their endorsement of personality difficulties, using nationally representative data of the civilian, noninstitutionalized population of the United States, inclusive of those reporting past-year homelessness ( = 704). A bias-adjusted three-step latent class analysis was used to estimate latent class measurement models; classify cases into the optimal class solution; and, using a maximum likelihood method, test the association between demographic and behavioral health covariates with class membership. Results show that the four-class solution was optimal. The largest class (35.44%) had high probability of endorsing each personality difficulty and had high rates of behavioral health disorders. The second class (26.51%) had higher levels of antisocial traits and greater probability of endorsing substance use disorders relative to third and fourth classes. The third-largest class showed minimal personality difficulties (24.40%) and had the lowest probability of meeting criteria for each behavioral health disorder considered. The final class showed high levels of relational instability and identity diffusion (13.65%) and had higher levels of mood and anxiety disorders and suicide attempt relative to second and third classes. In conclusion, personality difficulties are commonly endorsed by adults experiencing homelessness and show differential relationships to behavioral health conditions.
本研究基于对人格障碍的认可程度，区分了无家可归者在临床和理论上有意义的亚组，使用了美国平民非机构化人口的全国代表性数据，包括那些报告过去一年无家可归经历的人（=704）。采用偏差调整的三步潜在类别分析来估计潜在类别测量模型；将案例分类为最优类别解决方案；并使用最大似然法检验人口统计学和行为健康协变量与类别成员之间的关联。结果表明，四类解决方案是最优的。最大的类别（35.44%）认可每种人格障碍的可能性很高，且行为健康障碍发生率很高。第二类（26.51%）相对于第三类和第四类，具有更高水平的反社会特质，认可物质使用障碍的可能性更大。第三大类别表现出最小的人格障碍（24.40%），且符合所考虑的每种行为健康障碍标准的可能性最低。最后一类表现出高度的关系不稳定和身份扩散（13.65%），相对于第二类和第三类，具有更高水平的情绪和焦虑障碍以及自杀未遂情况。总之，人格障碍在无家可归的成年人中普遍存在，并且与行为健康状况存在不同的关系。
