Knowledge translation: A mandate for Federal research agencies
Bibliographic record
Abstract
In this time of tightening budgets and increased accountability, Federal agencies must be seen not only as effective in carrying out their missions (i.e., generating scientifically based results) but also as supportive of relevant work. The challenges to being relevant are at least as formidable as those to achieving scientific rigor. The U.S. Department of Education's National Institute on Disability and Rehabilitation Research (NIDRR) recently published its Long-Range Plan for Fiscal Years 2005-2009 in the Federal Register, in which it outlined a new approach to its legislatively mandated role in disseminating valuable information to persons with disabilities and their families [1]. This new approach at NIDRR can be traced in part to the first conference under the Translating Research into Practice initiative, which was held in 2003 by the U.S. Department of Health and Human Services, Agency for Healthcare Research and Quality. At this conference, one topic of discussion was the implication of evaluating the quality of findings. In this context, the conference participants reflected interest in the concept of knowledge translation (KT) as developed by the Canadian Institute of Health Research (CIHR). This institute defined KT as-- ... the exchange, synthesis and ethically-sound application of knowledge --within a system of interactions among researchers and users--to accelerate the capture of the benefits of ... [2]. The CIHR further states that KT encompasses all steps between the creation of new knowledge and its application to yield beneficial outcomes for society. This includes knowledge dissemination, communication, technology transfer, ethical context, knowledge management, knowledge utilization, two-way exchange between researchers and those who apply knowledge, implementation technology assessment, synthesis of results within a global context, development of consensus guidelines, and [2]. In a forthcoming book chapter, Campbell describes KT as closing the gap between discovery and deployment [3]. Through the leadership of Richard P. Melia, PhD, * NIDRR gave KT a strategic role in the logic model outlined in NIDRR's long-range plan mentioned previously. Dr. Melia vigorously promoted the understanding of the essential centrality of KT to beginning with conceptualization and continuing through utilization of the results, and the benefits available from the complex system of interactions among researchers and users. In some ways, KT is analogous to participatory action research, or research that involves all relevant parties in actively examining current action together to maximize the likelihood of relevant, meaningful [4] Key concepts of KT include its cyclic nature, in which the interactions of researchers and knowledge users (KUs) continually influence the direction, progress, and results of so its continual relevance or meaning to KUs is assured. The CIHR recognizes that commercialization of results is an important aspect of KT efforts; thus, technology transfer is also an important part of the KT process. Equally important in the current (and foreseeable) environment is evidence vetting, in which great stress is placed on establishing and maintaining rigor in methods. Because of the historic problems of small populations of heterogeneous individuals, rehabilitation is on the cutting edge of what will likely become the wave of the future--the n of 1--design that will become more and more widespread as the effect of genomics on medicine develops to the point that therapies will be tailored to each individual. Thus, the challenges we now face in conducting clinical in the current flat earth society of randomized controlled trials [5] will be faced by more and more medical disciplines and will demand that we identify and develop methods that yield the rigorous results demanded not only by KUs but also by those holding agencies accountable for producing meaningful results, both inside and outside government. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.163 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".