Bibliographic record
Abstract
Knowledge brokering is a strategy to support collaborations and partnerships within and across clinical, research, and policy worlds to improve the generation and use of research knowledge. Knowledge brokers function in multiple roles to facilitate the use of evidence by leveraging the power of these partnerships. The application of theory can provide clarity in understanding the processes, influences, expected mechanisms of action, and desired outcomes of knowledge brokering. Viewing knowledge brokering from the perspective of its role domains can provide a means of organizing these elements to advance our understanding of knowledge brokering. The objectives of this special interest article are (1) to describe the context for knowledge brokering in health care, (2) to provide an overview of knowledge translation theories applied to knowledge brokering, and (3) to propose a model outlining the role domains assumed in knowledge brokering. The Role Model for Knowledge Brokering is composed of 5 role domains, including information manager, linking agent, capacity builder, facilitator, and evaluator. We provide examples from the literature and our real-world experience to demonstrate the application of the model. This model can be used to inform the practice of knowledge brokering as well as professional development and evaluation strategies. In addition, it may be used to inform theory-driven research examining the effectiveness of knowledge brokering on knowledge generation and translation outcomes in the health care field, as well as on patient health outcomes.Video Abstract is available for more insights from the authors (see Supplemental Digital Content 1, http://links.lww.com/JNPT/A126).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".