Latent Profile Analysis in Child Bereavement

项目摘要 父母死亡是最深刻的童年经历，具有长期和负面的心理健康 结果，包括抑郁症和自杀的风险增加，与一般人群相比。早期 青少年特别容易受到父母死亡的影响。这一年龄组 发展和适应他们的认知，动机，社会行为和环境的变化，每一个 从而塑造他们的互动和关系。早期的压力经历，如父母去世， 通过引发情感危机来影响孩子的发展这些危机可能有助于发展 精神健康障碍并持续到青年期，即精神疾病发病的高峰年龄。 为了减轻这些影响，丧亲干预措施针对这一人群，以加强适应性应对 和情感表达。然而，评估这些干预措施有效性的实证研究 这些方案的数量有限，方法严谨，关于其长期有效性的数据很少。 此外，这些研究使用以变量为中心的方法，假设所有的悲伤反应都是一样的， 而不是以人为中心的方法来解释经验和个人特征的变化。 此应用程序将通过利用以人为中心的分析（即，潜在配置文件 分析）以检验：基线时个体寻求支持应对和情绪表达特征 (i.e.,近时间的损失），心理健康结果在儿童和青年成人的基础上，这些个人 特征，以及基线时的个体差异是否预测干预反应。此应用程序与 与NICHD和NIMH优先加强纵向发展和心理健康的理解 通过确定风险因素、行为指标和干预措施，评估丧亲儿童的结局。 这些目标将通过以人为本的分析来实现，这些分析利用了丰富而独特的数据集， 在244名失去父母的孩子的样本中，五波数据跨越儿童到年轻的成年期。的 具体目标是：（1）根据应对和情绪模式， 表达，（2）确定纵向抑郁和自杀结果的异质性作为儿童的功能 应对和情绪表达模式，以及（3）检查干预效果不同的方式，基于 个人资料会员资格。以人为中心的分析对于理解个体差异是必要的， 有助于心理健康结果和干预反应的异质性，并考虑了动态 悲伤的本质这项研究是一个及时的公共卫生优先事项，考虑到父母损失的急剧增加， 新冠肺炎。在这个NRSA F31博士前奖学金申请概述的研究和培训将装备 我的技能和支持需要追求一个成功的职业生涯作为一个独立的研究科学家， 将增加我对相关理论模型及其在预防中的应用的知识，以及 先进的统计方法，用于纵向研究、科学写作和传播。

PROJECT SUMMARY Parental death is the most profound childhood experience, with long-term and negative mental health consequences, including elevated risk for depression and suicide, compared to the general population. Early adolescents are particularly vulnerable to the resultant consequences of a parental death. This age group is developing and adjusting to shifts in their cognition, motivation, social behaviors, and environments, each of which shapes their interactions and relationships. Early stress experiences, such as a parental death, influence the child’s development by provoking emotional crises. These crises may contribute to the development of mental health disorders and persist into young adulthood, the peak age for mental illness onset. To mitigate these effects, bereavement interventions have targeted this population to enhance adaptive coping and emotional expression. However, empirical studies evaluating the effectiveness of these interventions are limited in number and methodological rigor, and there is little data regarding their long-term effectiveness. Moreover, these studies use variable-centered approaches, which assume all grief responses are the same, rather than person-centered approaches to account for variation in experience and individual characteristics. This application will address these limitations by leveraging a person-centered analysis (i.e., Latent Profile Analysis) to examine: individual support-seeking coping and emotional expression characteristics at baseline (i.e., near time of loss), mental health outcomes across child- and young adulthood based on those individual characteristics, and if individual differences at baseline predict intervention response. This application aligns with NICHD and NIMH priorities of enhancing understanding of longitudinal developmental and mental health outcomes of bereaved children by identifying risk factors, behavioral indicators, and intervention responses. These aims will be addressed through person-centered analyses that leverage a rich and unique dataset with five waves of data spanning child- to young adulthood in a sample of 244 parentally-bereaved children. The specific aims are: (1) to identify subgroups of bereaved children based on patterns of coping and emotional expression, (2) identify heterogeneity in longitudinal depression and suicide outcomes as a function of child coping and emotional expression patterns, and (3) examine ways in which intervention effects differ based on profile membership. Person-centered analyses are necessary for understanding the individual differences that contribute to heterogeneity in mental health outcomes and intervention response, and considers the dynamic nature of grief. This research is a timely public health priority, given the stark increase of parental loss due to COVID-19. The research and training outlined in this NRSA F31 predoctoral fellowship application will equip me with the skills and support needed to pursue a successful career as an independent research scientist, and will increase my knowledge of relevant theoretical models and their application to prevention, competencies in advanced statistical methods for longitudinal research, and scientific writing, and dissemination.