Core knowledge translation competencies: a scoping review
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT) is the broad range of activities aimed at supporting the use of research findings leading to evidence-based practice (EBP) and policy. Recommendations have been made that capacity building efforts be established to support individuals to enact KT. In this study, we summarized existing knowledge on KT competencies to provide a foundation for such capacity building efforts and to inform policy and research. Our research questions were "What are the core KT competencies needed in the health sector?" and "What are the interventions and strategies to teach and reinforce those competencies?" METHODS: We used a scoping review approach and an integrated KT process by involving an Advisory Group of diverse stakeholders. We searched seven health and interdisciplinary electronic databases and grey literature sources for materials published from 2003 to 2017 in English language only. Empirical and theoretical publications in health that examined KT competencies were retrieved, reviewed, and synthesized. RESULTS: Overall, 1171 publications were retrieved; 137 were fully reviewed; and 15 empirical and six conceptual academic, and 52 grey literature publications were included and synthesized in this scoping review. From both the academic and grey literature, we categorized 19 KT core competencies into knowledge, skills, or attitudes; and identified commonly used interventions and strategies to enhance KT competencies such as education, organizational support and hands-on training. CONCLUSIONS: These initial core KT competencies for individuals provide implications for education, policy, knowledge brokering, and future research, and on the need for future evaluation of the KT competencies presented. We also discuss the essential role of organizational support and culture for successful KT activities/practice.
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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 | 0.079 | 0.202 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.058 | 0.041 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".