课题基金 / 基金详情

Predicting Substance Use among Military Veterans with a Positive MST Screen: A Machine Learning Approach

Predicting Substance Use among Military Veterans with a Positive MST Screen: A Machine Learning Approach
通过积极的 MST 筛选来预测退伍军人的药物使用情况:一种机器学习方法
批准号:
10404488
负责人:
SHANNON FORKUS
金额:
$0.72万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-05-24 至 2022-07-31

项目摘要

项目成果

SHANNON FORKUS的其他基金

相似基金

相关文献

中文摘要
翻译
项目摘要/摘要 军人性创伤(MST)是军人中一个严重而普遍的问题,影响 大约16%的军事人员和退伍军人[1]。物质使用对个人的影响不成比例 有MST病史。有(与没有)MST病史的人滥用物质的可能性是前者的两倍 [2-4]。在军事样本中使用物质与更高的负面后果发生率有关 几个领域(如健康、职业、法律[5、6]),包括死亡(如服药过量[7]、交通事故 [8]、自杀[7、8])。此外,尽管在有MST病史的个体中研究不足,但阴性 在创伤暴露人群中,物质使用后果已被证明更为严重,包括 临床表现越严重,治疗预后越差[9,10]。这些发现强调了 澄清军事人群中MST和物质使用之间的联系的重要性。 尽管在军事人群中使用药物具有临床相关性和公共卫生意义, 这一领域的研究几乎完全依赖于横截面设计。此外,绝大多数 这一领域的研究利用了传统的统计方法,这些方法在范围和能力上都受到限制。 这些限制具有重要的临床意义,因为它们限制了我们指定确切的性质和 MST和物质使用之间关系的方向性,从而影响研究结果的翻译方式 用于预防和干预工作。拟议的研究旨在通过利用 陆军STARRS部署前/部署后研究,一个大型的、预期的军事数据集:(1)阐明方向 使用纵向数据集的MST和物质使用之间的关系,以及(2)使用机器学习 方法开发一种算法来优化对有药物使用史的军事人员的药物使用检测 MST。这些发现也将有助于阐明这一高危人群使用药物的病因。 AS为临床使用提供了一个预测模型,以便更好地针对该人群中的高危个体。 这项研究项目将在罗德岛大学心理学系内进行; 在健康行为研究和方法论方面有着深厚的历史和承诺的机构。申请人 将可以接触到赞助商和顾问,他们在MST、物质使用、先进方法、 和统计分析,这将有助于她的职业目标,以发展更多的知识和熟练程度 (A)退伍军人的性创伤(例如,MST)和物质使用;(B)赠款/手稿开发;(C) 统计和方法能力(即机器学习);和(D)大数据。 拟议的项目使用及时和创新的方法来推动关于MST之间关系的科学研究 以及军事人员的物质使用。解决这一人群中的物质使用问题是必要的,以改善 我国退伍军人的健康,并与国家药物滥用研究所的使命相一致。
英文摘要
Project Summary/Abstract Military sexual trauma (MST) is a serious and pervasive problem among military populations, affecting approximately 16% of military personnel and veterans [1]. Substance use disproportionately affects individuals with a history of MST. Individuals with (vs. without) a history of MST are twice as likely to misuse substances [2-4]. Substance use among military samples has been linked to higher rates of negative consequences across several domains (e.g., health, occupational, legal [5, 6]), including death (e.g., overdose [7], traffic accidents [8], suicide [7, 8]). Further, while understudied among individuals with a history of MST in particular, negative substance use outcomes have been shown to be more severe among trauma-exposed populations, including more severe clinical presentations and poorer treatment prognosis [9, 10]. These findings emphasize the importance of clarifying the association between MST and substance use among military populations. Despite the clinical relevance and public health significance of substance use among military populations, research in this area has relied almost exclusively on cross-sectional designs. Moreover, the vast majority of studies in this area have utilized traditional statistical methods, which are limited in scope and capabilities. These limitations have important clinical implications, as they restrict our ability to specify the exact nature and directionality of the relationship between MST and substance use, thereby affecting how findings are translated into prevention and intervention efforts. The proposed research aims to fill these critical gaps by utilizing the Army STARRS pre/post-deployment study, a large, prospective military dataset to: (1) explicate the directional relation between MST and substance use using a longitudinal dataset, and (2) employ machine learning methods to develop an algorithm to optimize detection of substance use in military personnel with a history of MST. These findings will assist in elucidating the etiology of substance use among this high-risk group, as well as provide a prediction model for clinical use to better target at-risk individuals in this population. This research project will take place within the Department of Psychology at the University of Rhode Island; an institution with a strong history and commitment to health behavior research and methodology. The applicant will have access to sponsors and consultants with expertise in MST, substance use, advanced methodology, and statistical analysis that will facilitate her career objectives to develop increased knowledge and proficiency in (a) sexual trauma (e.g., MST) and substance use in military veterans; (b) grant/manuscript development; (c) statistical and methodological capabilities (i.e., machine learning); and (d) big data. The proposed project uses a timely and innovative approach to advance science on the relation between MST and substance use in military personnel. Addressing substance use in this population is necessary to improve the health of our nation's veterans, and aligns with the mission of the National Institute on Drug Abuse.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1080/15299732.2021.1989110
发表时间: 2022-05
期刊: JOURNAL OF TRAUMA & DISSOCIATION
影响因子: 3.3
作者: [Forkus, Shannon R., Weiss, Nicole H., Goncharenko, Svetlana, Schick, Melissa R., Monteith, Lindsey L., Contractor, Ateka A.]
通讯作者: Contractor, Ateka A.
Military sexual trauma and alcohol misuse among military veterans: The roles of negative and positive emotion dysregulation.
军事性创伤和滥用士兵的酗酒:负面情绪和积极情绪失调的作用。
DOI: 10.1037/tra0000604
发表时间: 2020-10
期刊: PSYCHOLOGICAL TRAUMA-THEORY RESEARCH PRACTICE AND POLICY
影响因子: 6.3
作者: [Forkus, Shannon R., Rosellini, Anthony J., Monteith, Lindsey L., Contractor, Ateka A., Weiss, Nicole H.]
通讯作者: Weiss, Nicole H.
The effect of self-compassion on integrated treatment outcomes among veterans with co-occurring AUD and PTSD.
Predicting Substance Use among Military Veterans with a Positive MST Screen: A Machine Learning Approach
  • 批准号:
    10458304
  • 项目类别:
  • 资助金额:
    $0.25万
  • 财政年份:
    2021
  • 负责人:
    SHANNON FORKUS
  • 依托单位:
海外基金