Barriers and Facilitators to Knowledge Brokering Activities: Perspectives from Knowledge Brokers Working in Canadian Rehabilitation Settings
Bibliographic record
Abstract
INTRODUCTION: Knowledge translation experts advocate for employing knowledge brokers (KBs) to promote the uptake of research evidence in health care settings. Yet, no previous research has identified potential barriers for KBs to promote the uptake of research evidence in rehabilitation settings. This study aimed to identify the barriers and facilitators for KBs in Canadian rehabilitation settings as perceived by individuals serving as KBs. METHODS: Qualitative study using semistructured telephone interviews with individuals performing KB activities in rehabilitation settings across Canada. The interview topic guide was informed by the Consolidated Framework for Implementation Research (CFIR) and consisted of 20 questions covering three domains (characteristics of individuals, inner setting, and outer settings). We conducted qualitative descriptive analysis combining deductive coding guided by the CFIR. RESULTS: Characteristics of individuals included having communication skills, clinical experience, research skills, and interpersonal features, in addition to being confident and motivated and receiving training. The inner setting domain included having constant networking with stakeholders and being aware of stakeholders' needs, in addition to resources availability, leadership engagement, knowledge accessibility, prioritizing brokering activities, and monitoring KBs' performance. Finally, the outer setting domain showed that KBs need to be connected to a community of practice to promote information exchange and avoid work duplications. DISCUSSION: Factors likely to hinder or promote the optimal use of KBs within Canadian rehabilitation settings include skill sets and networking abilities; organizational culture, resources, and leadership engagement; and the need for specific training for KBs and for evaluation tools to monitor their performance.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.038 | 0.013 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".