Identifying competencies for integrated knowledge translation: a Delphi study
Bibliographic record
Abstract
BACKGROUND: Considerable progress has been made to advance the field of knowledge translation to address the knowledge-to-action gap in health care; however, there remains a growing concern that misalignments persist between research being conducted and the issues faced by knowledge users, such as clinicians and health policy makers, who make decisions in the health care context. Integrated knowledge translation (IKT) is a collaborative research model that has shown promise in addressing these concerns. It takes advantage of the unique and shared competencies amongst researchers and knowledge users to ensure relevance of the research process and its outcomes. To date, core competencies have already been identified to facilitate training in knowledge translation more generally but they have yet to be prioritized for IKT more specifically. The primary aim of this study was to recruit a group of researchers and knowledge users to identify and prioritize core competencies for researchers and knowledge users to engage with IKT. METHODS: We recruited health care knowledge users (KUs) and researchers with experience and knowledge of IKT for a quantitative, cross-sectional study. We employed a modified Delphi approach consisting of three e-survey rounds to establish consensus on competencies important to IKT for KUs and researchers based on mean rating of importance and agreement between participants. RESULTS: Nineteen (73%) of the initial 26 participants were researchers (response rate = 41% in the first round; retention in subsequent rounds > 80%). Participants identified a total of 46 competencies important for IKT (18 competencies for KUs, 28 competencies for researchers) under 3 broad domains. Technical research skills were deemed extremely important for researchers, while both groups require teamwork and knowledge translation skills. CONCLUSIONS: This study provides important insight into distinct and overlapping IKT competencies for KUs and researchers. Future work could focus on how these can be further negotiated and contextualized for a wide range of IKT contexts, projects and teams. Greater attention could also be paid to establishing competencies of the entire team to support the research co-production process.
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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 | 0.073 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".