Profiling knowledge brokers in the rehabilitation sector across Canada: A descriptive study
Bibliographic record
Abstract
RATIONALE: Knowledge brokers (KBs) can help promote the uptake of the latest research evidence into clinical practice. Little is known about who they are, the types of roles they perform, and the training they receive. Establishing a portrait of Canadian KBs working in the rehabilitation sector may inform health care organizations and knowledge translation specialists on how best to advance KBs practices. The overall goal was to describe the profile of KBs working to promote the uptake of evidence within rehabilitation settings in Canada. Specifically, this study aimed to describe the sociodemographic and professional characteristics, work activities, and training of KBs. METHODS: A cross-sectional online survey was administered to KBs working in rehabilitation settings across Canada. The survey included 20 questions covering sociodemographic and professional characteristics, work activities, and training opportunities. Response frequency and percentage were calculated for all categorical variables, and the weighted average (WA) for each role was calculated across participants. Descriptive analysis was conducted for all open-ended questions. RESULTS: Of 475 participants accessing the website, 198 completed the survey questionnaire, including 99 clinicians, 35 researchers, and 26 managers. While over two-thirds of respondents had completed a graduate degree, only 38% reported receiving KBs-related training. The respondents' primary roles corresponded to a linking agent (WA = 1.84), followed by capacity builder (WA = 1.76), information manager (WA = 1.71), facilitator (WA = 1.41), and evaluator (WA = 1.32). CONCLUSIONS: KBs are mostly expert clinicians who tend to perform brokering activities part-time targeting their peers. Participants mostly perform the linking agent, capacity builder, and information roles. Moreover, only a few participants received formal training to perform brokering activities.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".