Nationwide Environmental Scan of Knowledge Brokers Training
Bibliographic record
Abstract
INTRODUCTION: Knowledge brokers (KBs) can promote the uptake of best practice guidelines in rehabilitation. Although many institutions offer training opportunities to health care professionals who wish to undertake KBs roles, the characteristics and content of those educational training opportunities (ETOs) are currently unknown. This study aimed to describe the ETOs available to rehabilitation professionals in Canada and determine whether the ETOs meet the competencies expected of the KBs roles. METHODS: We conducted a Canada-wide environmental scan to identify ETOs using three strategies: online search, phone calls, and snowball. To be included in the study, ETOs had to be offered to rehabilitation professionals in Canada and be targeting KBs competencies and/or roles. We mapped each of the content to the KBs competencies (knowledge and skills) within the five roles of KBs: information manager, linking agent, capacity builder, facilitator, and evaluator. RESULTS: A total of 51 ETOs offered in three Canadian provinces, British Columbia, Ontario, and Quebec, were included in the analysis. For KBs competencies, 76% of ETOs equipped attendees with research skills, 55% with knowledge brokering skills, and 53% with knowledge on implementation science. For KBs roles, over 60% of ETOs supported attendees to in performing the capacity builder role and 39% the evaluator role. DISCUSSION: Findings suggest that ETOs focused primarily on preparing participants with the research and knowledge brokering skills required to perform the capacity builder and evaluator roles. Comprehensive educational training covering all KBs roles and competencies are needed.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".