Virtual Knowledge Brokering: Describing the Roles and Strategies Used by Knowledge Brokers in a Pediatric Physiotherapy Virtual Community of Practice
Bibliographic record
Abstract
INTRODUCTION: Knowledge transfer in pediatric rehabilitation is challenging and requires active, multifaceted strategies. The use of knowledge brokers (KBs) is one such strategy noted to promote clinician behavior change. The success of using KBs to transfer knowledge relies on their ability to adapt to ever-changing clinical contexts. In addition, with the rapid growth of online platforms as knowledge transfer forums, KBs must become effective in virtual environments. Although the role of KBs has been studied in various clinical contexts, their emerging role in specific online environments designed to support evidence-based behavior change has not yet been described. Our objective is to describe the roles of, and strategies used by, four KBs involved in a virtual community of practice to guide and inform future online KB interventions. METHODS: A descriptive design guided this study and a thematic content analysis process was used to analyze online KB postings. The Promoting Action on Research in Health Sciences knowledge transfer framework and online andragogical learning theories assisted in the coding. A thematic map was created illustrating the links between KBs' strategies and emerging roles in the virtual environment. RESULTS: We analyzed 95 posts and identified three roles: 1) context architect: promoting a respectful learning environment, 2) knowledge sharing promoter: building capacity, and 3) linkage creator: connecting research-to-practice. Strategies used by KBs reflected invitational, constructivism, and connectivism approaches, with roles and strategies changing over time. DISCUSSION: This study increases our understanding of the actions of KBs in virtual contexts to foster uptake of research evidence in pediatric physiotherapy. Our results provide valuable information about the knowledge and skills required by individuals to fulfill this role in virtual environments.
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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 | 0.019 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; a candidate call from one teacher head, 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".