Barriers to dissemination and translation of the findings of research projects can occur in a number of places along the continuum from completion of the project to its dissemination into the scientific literature and translation into practice and policy. For instance, given the extreme shortage of faculty in schools of nursing and increasing demands on faculty time, [1, 2] nurse scientists must have the skills, tools, and support to disseminate their research findings in a timely manner into high-quality nursing and interdisciplinary journals. Another barrier is the difficulty of publishing the results of well-designed and adequately-powered efficacy studies when the null hypothesis is not rejected even though the information might be useful in refining future research directions. Indeed, several meta-analyses of self-management programs for chronic disease suggest that publication bias exists in this body of literature. [3, 4] In terms of dissemination and translation into practice, the health care literature documents that, even decades after conclusive evidence of benefits of a medical treatment have been published, e.g., beta blockers for patients recovering from heart attack or lowering cholesterol in coronary artery disease, many patients fail to receive the benefit of these advancements. [5] Although these same types of figures are not available for self-management interventions, the problem is widely acknowledged. An additional barrier to translation of self-management research findings into policy is the lack of economic analyses to accompany the efficacy and effectiveness data. [6] This barrier is addressed through the economic analysis training activities that are described in the Design, Methods, Biostatistics, and Economic Analysis Core. We address the remaining issues through the services and resources of the Dissemination and Translation Core.

Agency
National Institute of Health (NIH)
Institute
National Institute of Nursing Research (NINR)
Type
Center Core Grants (P30)
Project #
5P30NR010677-05
Application #
8289985
Study Section
Special Emphasis Panel (ZNR1)
Project Start
Project End
2013-06-30
Budget Start
2011-07-01
Budget End
2013-06-30
Support Year
5
Fiscal Year
2011
Total Cost
$136,094
Indirect Cost
Name
Columbia University (N.Y.)
Department
Type
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032
Jang, Nara; Bakken, Suzanne (2017) Relationships Between Demographic, Clinical, and Health Care Provider Social Support Factors and Internalized Stigma in People Living With HIV. J Assoc Nurses AIDS Care 28:34-44
Smaldone, Arlene; Findley, Sally; Bakken, Suzanne et al. (2016) Study protocol for a randomized controlled trial to assess the feasibility of an open label intervention to improve hydroxyurea adherence in youth with sickle cell disease. Contemp Clin Trials 49:134-42
Yoon, Sunmoo; Gutierrez, Jose (2016) Behavior Correlates of Post-Stroke Disability Using Data Mining and Infographics. Br J Med Med Res 11:
Collins, Sarah A; Yoon, Sunmoo; Rockoff, Maxine L et al. (2016) Digital divide and information needs for improving family support among the poor and underserved. Health Informatics J 22:67-77
Redeker, Nancy S; Anderson, Ruth; Bakken, Suzanne et al. (2015) Advancing Symptom Science Through Use of Common Data Elements. J Nurs Scholarsh 47:379-88
Sheehan, Barbara; Lucero, Robert J (2015) Initial Usability and Feasibility Evaluation of a Personal Health Record-Based Self-Management System for Older Adults. EGEMS (Wash DC) 3:1152
Odlum, Michelle; Yoon, Sunmoo (2015) What can we learn about the Ebola outbreak from tweets? Am J Infect Control 43:563-71
Yoon, Sunmoo; Suero-Tejeda, Niurka; Bakken, Suzanne (2015) A Data Mining Approach for Examining Predictors of Physical Activity Among Urban Older Adults. J Gerontol Nurs 41:14-20
Masterson Creber, Ruth M; Lee, Christopher S; Margulies, Kenneth et al. (2015) Identifying biomarker patterns and predictors of inflammation and myocardial stress. J Card Fail 21:439-45
Bakken, Suzanne; Jia, Haomiao; Chen, Elizabeth S et al. (2014) The Effect of a Mobile Health Decision Support System on Diagnosis and Management of Obesity, Tobacco Use, and Depression in Adults and Children. J Nurse Pract 10:774-780

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