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Research on the management of symptoms is a primary focus of the National Institutes of Health (NIH) National Institute of Nursing Research (NINR, 2011). NINRsupported research has already contributed inmajor ways to managing symptoms and improving quality of life and overall function, especially among individuals and families living with acute and chronic conditions. Symptoms are a primary research focus of seven currently (2014) funded NINR-funded centers, including exploratory centers (P20) and centers of excellence (P30) designed to further build symptom science. Effective leveraging of resources and collaborations between researchers and across institutions are critical to a recently published Logic Model for Center Sustainability authored by the Center directors (Dorsey et al., 2014).Sharing symptom data across studies is important to leveraging the results of research in a cost-effective manner. However, symptom data obtained in a single study may not be comparable across studies, thus hampering the ability to generalize research findings and compare the effects of treatments and characteristics of samples in different settings. Use of common data elements (CDEs), defined as fundamental, logical units of data pertaining to one kind of information that are clearly conceptualized (Cohen, Thompson, Yates, Zimmerman, & Pullen, 2015; Meredith, Zozus, Wilgus, & Hammond, under review), improves efficiency and data quality. CDEs are especially valuable in symptom science because people with a broad range of acute and chronic conditions, developmental stages, and ethnic backgrounds often experience common symptoms (e.g., anxiety, depression, fatigue, pain, sleep disturbance). Interventions (e.g., exercise) focused on symptoms may have common outcomes across populations (e.g., cancer, heart disease). Although researchers across disciplines are increasingly using CDEs, as exemplified by initiatives of the National Institute of Neurological Disease and Stroke (NINDS, 2014), National Library of Medicine (NLM, 2014), National Cancer Institute (NCI, 2014), and the National Institute on Drug Abuse (NIDA, 2014), there has been little systematic use of CDEs to support symptom science.The purposes of this article are to (a) recommend best practices for the use of CDEs for symptom science within and across centers; (b) evaluate the benefits and challenges associated with the use of CDEs for symptom science; (c) propose CDEs to be used in symptom science to serve as the basis for this emerging science; and (d) suggest implications and recommendations for future research and dissemination of CDEs for symptom science.Best Practices for Developing, Managing, Selecting, and Using Common Data ElementsBest practices for using CDEs include developing or identifying and selecting CDEs; creating or choosing and managing formats and electronic platforms for data collection and sharing; and assuring quality and administrative oversight to provide access to the data. These practices maximize the quality of the data and successful and efficient data sharing across studies.The symptom CDE initiative described in this article addresses a unique unmet need. However, best practices used by other NIH units informed our work. Several organizations described collaborative CDE development processes (e.g., NIH-NINDS, NIH-NCI, NIH-NIDA, NLM, National Data Base for Autism Research) and identified CDEs appropriate to the research associated with their missions. Although some of the CDEs include symptoms, none of these initiatives focused specifically on symptom science.The process of developing and making CDEs available is ideally transparent, inclusive, and involves identifying, developing, and vetting CDEs by national and international experts in the scientific community. For example, the iterative process that NINDS used to develop and refine CDEs (NINDS, 2014) occurred over 12 to 18 months. It included convening a working group, subdividing the working group based on areas of need, holding an introductory meeting, developing CDEs for assigned areas by subgroups, reviewing the work of all the subgroups, revising the CDEs based on feedback, obtaining public review of the identified CDEs, revising the CDEs based on feedback, and posting the first versions of the CDEs on the website. … |
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