BackgroundTranslational research is a key area of focus of the National Institutes of Health (NIH), as demonstrated by the substantial investment in the Clinical and Translational Science Award (CTSA) program. The goal of the CTSA program is to accelerate the translation of discoveries from the bench to the bedside and into communities. Different classification systems have been used to capture the spectrum of basic to clinical to population health research, with substantial differences in the number of categories and their definitions. Evaluation of the effectiveness of the CTSA program and of translational research in general is hampered by the lack of rigor in these definitions and their application. This study adds rigor to the classification process by creating a checklist to evaluate publications across the translational spectrum and operationalizes these classifications by building machine learning-based text classifiers to categorize these publications.MethodsBased on collaboratively developed definitions, we created a detailed checklist for categories along the translational spectrum from T0 to T4. We applied the checklist to CTSA-linked publications to construct a set of coded publications for use in training machine learning-based text classifiers to classify publications within these categories. The training sets combined T1/T2 and T3/T4 categories due to low frequency of these publication types compared to the frequency of T0 publications. We then compared classifier performance across different algorithms and feature sets and applied the classifiers to all publications in PubMed indexed to CTSA grants. To validate the algorithm, we manually classified the articles with the top 100 scores from each classifier.ResultsThe definitions and checklist facilitated classification and resulted in good inter-rater reliability for coding publications for the training set. Very good performance was achieved for the classifiers as represented by the area under the receiver operating curves (AUC), with an AUC of 0.94 for the T0 classifier, 0.84 for T1/T2, and 0.92 for T3/T4.ConclusionsThe combination of definitions agreed upon by five CTSA hubs, a checklist that facilitates more uniform definition interpretation, and algorithms that perform well in classifying publications along the translational spectrum provide a basis for establishing and applying uniform definitions of translational research categories. The classification algorithms allow publication analyses that would not be feasible with manual classification, such as assessing the distribution and trends of publications across the CTSA network and comparing the categories of publications and their citations to assess knowledge transfer across the translational research spectrum.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-016-0992-8) contains supplementary material, which is available to authorized users.
Engagement of the community through informal dialogue with researchers and physicians around health and science topics is an important avenue to build understanding and affect health and science literacy. Science Cafés are one model for this casual interchange; however the impact of this approach remains under researched. The Community Engagement Key Function of the Clinical and Translational Science Institute of Southeast Wisconsin hosted a series of Science Cafés in which topics were collaboratively decided upon by input from the community. Topics ranged from Personalized Medicine to Alzheimer's and Dementia to BioMedical Innovation. A systematic evaluation of the impact of Science Cafés on attendees' self‐confidence related to five health and scientific literacy concepts showed statistically significant increases across all items (Mean differences between mean retrospective pre‐scores and post‐scores, one tailed, paired samples t‐test, n = 141, p < .0001 for all items). The internal consistency of the five health and scientific literacy items was excellent (n = 126, α = 0.87). Thematic analysis of attendees' comments provides more nuance about positive experience and suggestions for possible improvements. The evaluation provides important evidence supporting the effectiveness of brief, casual dialogue as a way to increase the public's self‐rated confidence in health and science topics.
BACKGROUND: In 2010, the Patient Centered Outcomes Research Institute (PCORI) was created to fund patientcentered research that meaningfully engages stakeholders impacted by that research. As a result, investigators became interested in understanding who are appropriate stakeholders and what meaningful engagement in research looks like (6, 8-10). OBJECTIVE: To understand how and when stakeholder engagement worked well and identify areas for enhancing engagement in a PCORI-funded research study of peer-topeer support of older adults in three communities across the USA. DESIGN: Qualitative interview study. PARTICIPANTS: Twelve members of the inter-disciplinary research team. APPROACH: Interviews were conducted via phone, recorded, and transcribed. Transcripts were analyzed using a constant comparative method to identify themes. Transcripts were independently coded; coded themes were discussed by a small group of the research team to check interpretation and clarify meaning. Once initial themes were identified, the interviews and codes were shared with an external consultant who recoded all 12 transcripts and conducted further analysis and interpretation. Documentation from research meetings was used to validate our findings. KEY RESULTS: Strategies for facilitating meaningful engagement in the partnership, proposal, study design, and planning phase were very similar to community-based participatory research and include the use of community to identify research needs, equitable compensation and leadership, and budgeting for engagement activities. Strategies in the data collection phase include the use of cultural brokers, weekly data calls between the academic PI and imbedded research assistants, and maintaining joint ownership for research. CONCLUSIONS: Major funding institutions (e.g., NIH, PCORI) recognize that community engagement leads to higher quality, more meaningful research (7, 21). Our results support that assumption and in addition, suggest an investment in engagement strategies at the onset of a research project and the use of cultural brokers can greatly contribute to the success of implementing a large, multi-site research project.
IntroductionScience Cafés facilitated by the Clinical and Translational Science Institute of Southeast Wisconsin seek to increase health and scientific literacy through informal conversation between researchers and community members. The goal was to understand what factors have the greatest influence on attendees’ perceived changes in health and science literacy levels (PCHSL) to increase impact.MethodsPrevious research established the evaluation used in the Science Cafés to measure PCHSL. In this study, comparisons were made between (1) 2 different approaches to Science Cafés (Genomics Science Cafés or Health Science Cafés) and (2) regression models to show which factors best predicted PCHSL.ResultsThe approach of the Genomics Science Cafés series to Science Cafés showed a larger impact on PCHSL. Regression models suggest SES and education significantly contributes to PCHSL.ConclusionsInsights for program development to have greater impact on PCHSL were identified. Continuing to optimize dissemination of research findings to the public is essential for improving community health and well-being.
Lengthy review times for institutional review boards (IRBs) are a well-known barrier to research. In response to numerous calls to reduce review times, we devised "Real-Time IRB," a process that drastically reduces IRB review time. In this, investigators and study staff attend the IRB meeting and make changes to the protocol while the IRB continues its meeting, so that final approval can be issued at the meeting. This achieved an overall reduction in time from submission to the IRB to final approval of 40%. While this process is time and resource intensive, and cannot address all delays in research, it shows great promise for increasing the pace by which research is translated to patient care.
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