Knowledge Brokering: An Innovative Model for Supporting Evidence‐Informed Practice in Respiratory Care
Bibliographic record
Abstract
The process of adopting research findings in the clinical setting is challenging, regardless of the area of practice. One strategy to facilitate this process is the use of knowledge brokering. Knowledge brokers (KBs) are individuals who work to bridge the gap between researchers and knowledge users. In the health care setting, KBs work closely with clinicians to facilitate enhanced uptake of research findings into clinical practice. They also work with researchers to ensure research findings are translatable and meaningful to clinical practice. The present article discusses a KB's role in a respiratory care setting. Working closely with both researchers and clinicians, the KB has led teams in the process of conceptualizing, developing, testing, disseminating and evaluating several projects related to respiratory care, including projects related to mobility in critical care settings and acute exacerbations of chronic obstructive pulmonary disease; inspiratory muscle training; and the use of incentive spirometry in postsurgical populations. The KB role has provided an important communication link between researcher and knowledge user that has facilitated evidence-informed practice to improve patient care.
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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 | 0.088 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.019 | 0.034 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".