Advancing Implementation Science through Measure Development and Evaluation

通过措施制定和评估推进实施科学

• 批准号: 9315040
• 负责人: Cara Charissa Lewis
• 金额: $ 54.35万
• 依托单位: KAISER FOUNDATION HEALTH PLAN OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-23 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9315040
• 关键词: Address; Adoption; Advisory Committees; Caring; Decision Making; Development; Evaluation; Evidence based practice; Foundations; Generations; Goals; Health; Housing; Intervention; Interview; Knowledge; Laboratory Study; Link; Literature; Measurement; Measures; Mental Health; Methods; Mission; Nature; Outcome; Outcome Measure; Output; Performance; Plague; Policy Research; Procedures; Process; Property; Psyche structure; Psychiatry; Psychometrics; Publishing; Q-Sort; Rattus; Research; Research Personnel; Research Training; Review Literature; Risk; Running; Schools; Science; Scientist; Series; Solid; Specialist; Structure; System; Testing; United States National Institutes of Health; Work; base; community setting; evidence base; field study; implementation research; implementation science; improved; innovation; insight; meetings; sound; tool; uptake

 DESCRIPTION (provided by applicant): Poor quality and impractical measurement has impeded the study of implementation barriers, facilitators and strategies necessary for promoting widespread delivery of evidence-based care and improving mental health. Measures derive research and policy/practice insights that guide subsequent decision making. However, for measures to advance science and inform practice they need to be both psychometrically strong and pragmatic. No studies have evaluated the psychometric properties of a comprehensive array of measures delineated in highly-cited, foundational implementation science frameworks such as the Consolidated Framework for Implementation Research (CFIR) 4 or the Implementation Outcomes Framework (IOF).5 Moreover, no research has attempted to advance pragmatic implementation science measures, and the only existing pragmatic criteria was generated in the absence of stakeholder involvement. Our long-term goal is to develop a comprehensive battery of reliable, valid, and pragmatic measures that researchers and stakeholders could use to advance the science and practice of implementation. The overarching objective of this application is to put forth a measurement-focused research agenda for implementation science (i.e., which constructs possess psychometrically strong and pragmatic measures, which require further development) as well as measures and methods to accomplish this work. Three aims guide this proposal. One, establish a stakeholder-driven operationalization of pragmatic measures and develop reliable, valid rating criteria for assessing the construct. Our approach to generating "pragmatic" rating criteria employs mixed methods (literature review, qualitative interviews, and Q-sort and Delphi methods) to engage stakeholders. Two, develop reliable, valid, and pragmatic measures of accept- ability, appropriateness, and feasibility. We have vetted an effective development procedure to establish their reliability; substantive, discriminant content, structural, known-groups, and predictive validity; structural invariance; and sensitivity to change. Our approach to developing and testing new measures involves a series of laboratory studies to assess psychometric properties under controlled conditions and field studies to gauge psychometric and pragmatic performance under "real world" conditions. Three, identify CFIR and IOF-linked measures that demonstrate both psychometric and pragmatic strength. Our approach to assessing over 420 existing measures of 46 constructs builds upon our preliminary studies and accesses an established task force. The pragmatic measure rating criteria will be integrated into our established psychometric rating criteria rendering psychometric and pragmatic measure profiles. We predict that few existing measures are psycho- metrically sound and pragmatic, but that rigorous, replicable development and testing procedures will generate measures that possess these qualities. Once pragmatic, psychometrically strong measures are identified, the field can generate robust scientific knowledge about which implementation strategies work best, when, and for whom so that stakeholders can use evidence-based approaches for implementing evidence-based practices.