Reflections on Knowledge Brokering Within a Multidisciplinary Research Team
Bibliographic record
Abstract
Knowledge brokering (KB) may be one approach of helping researchers and decision makers effectively communicate their needs and abilities, and move toward increased use of evidence in health care. A multidisciplinary research team in Nova Scotia, Canada, has created a dedicated KB position with the goal of improving access to quality colorectal cancer care. The purpose of this paper is to provide an in-progress perspective on KB within this large research team. A KB position ("knowledge broker") was created to perform two primary tasks: (1) facilitate ongoing communication among team members; and (2) develop and maintain collaborations between researchers and decision makers to establish partnerships for the transfer and use of research findings. In this article, we discuss our KB model and its implementation, describe the broker's functions and activities, and present preliminary outcomes. The primary functions of the KB position have included: sustaining team members' engagement; harnessing members' expertise and sharing it with others; developing and maintaining communication tools/strategies; and establishing collaborations between team members and other stakeholders working in cancer care. The broker has facilitated an integrated knowledge translation approach to research conduct and led to the development of new collaborations with external stakeholders and other cancer/health services researchers. KB roles will undoubtedly differ across contexts. However, descriptive assessments can help others determine whether such an approach could be valuable for their research programs and, if so, what to expect during the process.
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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 | 0.269 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.039 | 0.059 |
| Scholarly communication | 0.035 | 0.036 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.016 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier 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".