Computerized adaptive self-report diagnostic assessment for mental health: the SC

心理健康计算机化自适应自我报告诊断评估：SC

基本信息

批准号：
8200450
负责人：
BENJAMIN B BRODEY
金额：
$ 16.78万
依托单位：
TELESAGE, INC.
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-07-01 至 2013-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8200450
关键词：
Adult Biological Markers Categories Cognitive Collaborations Communities Confusion Consensus DSM-IV DSM-V Databases Development Diagnosis Diagnostic Diagnostic and Statistical Manual Diagnostic and Statistical Manual of Mental Disorders Disease Exhibits Generations Gold Individual Interview Interviewer Language Lead Link Mental Health Mental disorders Mentally Ill Persons Methodology Methods Mission National Institute of Mental Health Outcome Outpatients Participant Patient Self-Report Patients Phase Population Protocols documentation Public Sector Questionnaires Reading Recruitment Activity Reporting Research Research Personnel Respondent Sampling Schedule Screening procedure Series Small Business Innovation Research Grant Specific qualifier value Target Populations Testing Text Time Training Translations Treatment Effectiveness Validation Vocabulary base clinical care clinical practice computerized cost effective diagnosis standard follow-up improved instrument member mental health organization phrases response success

项目摘要

DESCRIPTION (provided by applicant): In order to improve our understanding and treatment of mental illnesses, the primary mission of the National Institutes of Mental Health (NIMH), one must first accurately diagnose research participants and patients receiving routine clinical care. In this way, the appropriate links may be drawn between specific disorders, biological markers, and the effectiveness of treatments. The gold-standard for diagnosing mental health disorders is a standardized clinician-administered interview. However, such diagnostic interviews are not always practical in research and clinical practice due to time, expertise, and monetary requirements. Currently only one validated self-report diagnostic assessment exists, namely the Psychiatric Diagnostic Screening Questionnaire (PDSQ). However, this instrument was not developed for and has not been validated with a community mental health population. The PDSQ also lacks many important features that ease respondent and staff burden and increase usefulness in research and clinical practice. Given the tremendous need for valid cost-effective diagnostic assessments, we propose to develop the SCID-SR, a computerized, adaptive self-report diagnostic assessment with self-scoring, instantaneous narrative report generation, and a searchable response database. The SCID-SR will contain 15 diagnostic categories and will be based on the SCID-I, a gold-standard clinician administered diagnostic assessment for Axis I disorders, which uses criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). The SCID-SR will be appropriate for use with an adult community mental health population, including SPMI individuals. It will be developed using rigorous item authoring methods that have been used successfully in previous SBIR projects and produce items that are easy to read and understand, hold widespread cultural relevance, and are amenable to direct translation to other languages. SCID-SR items will be reviewed and rated by an Expert Panel during development and subsequently tested with three iterative rounds of cognitive interviewing with 60 adult community mental health outpatients from one of the largest private non-profit mental health organizations in the U.S. Cognitive interviews will include thinkalouds by participants while responding to the SCID-SR self-report items, follow-up interview questions pertaining to vocabulary, timeframe and selection of responses, as well as a modified set of SCID-I questions in order to establish correspondence between SCID-SR responses and SCID-I ratings by a trained interviewer. Results of cognitive interviews will inform the selection of the final set of SCID-SR items by the Expert Panel. The final set of SCID-SR items will undergo full quantitative validation with a large, diverse sample of community mental health outpatients in Phase II. Necessary revisions of the SCID-SR to conform with DSM-V, scheduled for release in 2013, will also be completed in Phase II. PUBLIC HEALTH RELEVANCE: Accurate diagnoses are the necessary first step to improving our understanding and treatment of mental illnesses, the primary mission of the National Institutes of Mental Health (NIMH). Availability of the SCID-SR, a computerized, adaptive self-report diagnostic assessment of Axis I mental health disorders, will provide researchers and clinicians with a rigorous, cost-effective alternative to clinician-administered diagnostic assessments. The SCID-SR will include Likert response sets, concise, easy to read and understand questions, automated scoring, instantaneous generation of comprehensive narrative reports, and a searchable response database, which will ease staff and respondent burden and increase usefulness in research and clinical practice.

描述（由申请人提供）：为了提高我们对精神疾病的理解和治疗，国家心理健康研究所（NIMH）的主要任务，必须首先准确诊断研究参与者和接受常规临床护理的患者。这样，可以在特定疾病，生物标记和治疗效率之间提出适当的联系。诊断心理健康障碍的金标准是标准化的临床医生管理访谈。但是，由于时间，专业知识和货币要求，这种诊断访谈在研究和临床实践中并不总是实用的。目前，只有一项验证的自我报告诊断评估，即精神诊断筛查问卷（PDSQ）。但是，该工具尚未开发，也没有得到社区心理健康人群的验证。 PDSQ还缺乏许多重要的特征，可以减轻受访者和员工负担，并在研究和临床实践中提高实用性。鉴于非常需要有效的成本效益诊断评估，我们建议开发SCID-SR，一种计算机化的自适应自我报告诊断评估，并具有自我评分，瞬时叙事报告生成以及可搜索的响应数据库。 SCID-SR将包含15个诊断类别，并将基于SCID-I，这是一家金色标准的临床医生对轴I疾病的诊断评估，该评估使用精神障碍的诊断和统计手册，第四版，文本修订版（DSM-IV-tr）。 SCID-SR适用于包括SPMI个人在内的成人社区心理健康人群。它将使用严格的项目创作方法开发，这些方法已成功地用于以前的SBIR项目中，并生产易于阅读和理解，具有广泛文化相关性的项目，并且可以直接转化为其他语言。 SCID-SR项目将在开发过程中由专家小组审查和评估，随后对三个迭代的认知访谈进行了测试作为一组经过修改的SCID-I问题，以建立训练有素的访调员之间的SCID-SR响应与SCID-I评级之间的对应关系。认知访谈的结果将为专家小组选择最终的SCID-SR项目选择。最终的SCID-SR项目将通过第二阶段的社区心理健康门诊病人进行大量，多样化的社区心理健康门诊患者样本。 SCID-SR的必要修订以符合计划于2013年发布的DSM-V，也将在第二阶段完成。公共卫生相关性：准确的诊断是提高我们对精神疾病的理解和治疗的必要第一步，这是美国国家心理健康研究所（NIMH）的主要任务。 SCID-SR的可用性是对轴I心理健康障碍的计算机化自适应自我报告诊断评估，它将为研究人员和临床医生提供严格的，具有成本效益的替代临床医生管理诊断评估的替代方法。 SCID-SR将包括Likert响应集，简洁，易于阅读和理解问题，自动评分，瞬时生成全面的叙事报告以及可搜索的响应数据库，这将减轻员工和受访者的负担，并在研究和临床实践中增加有用性。