Creating an action plan to advance knowledge translation in a domestic violence research network: a deliberative dialogue
Bibliographic record
Abstract
Background: There is limited research on how knowledge translation of a domestic violence (DV) research network is shared. This lack of research is problematic because of the complexity of establishing a research network, encompassing diverse disciplines, methods, and focus of study potentially impacting how knowledge translation functions. Aims and objectives: To address the limited research, we completed a deliberative dialogue with the following questions: Is there a consensus regarding a coherent knowledge translation framework for a domestic violence research network? What are the key actions that a domestic violence research network could take to enhance knowledge translation? Methods: Deliberative dialogue is a group process that blends research and practice to identify potential actions. In total, 16 participants attended three deliberative dialogue meetings. We applied a qualitative analysis to the data to identify the key actions. Findings: The deliberative dialogue facilitated mutual agreement regarding four key actions: (1) agreement on a knowledge translation approach; (2) active promotion of dedicated leadership within an authorising environment; (3) development of sustainable partnerships through capacity building and collaboration, particularly with DV survivors; and (4) employment of multiple strategies applying different kinds of evidence for diverse purposes and emerging populations. Discussion and conclusions: The use of the deliberative dialogue has uncovered specific factors required for the successful knowledge translation of domestic violence research. These factors have been added to the Integrated Knowledge Translation (IKT) capacity framework to enhance its application for domestic violence research. Future research could explore these organisational, professional and individual factors further by evaluating them in 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 | 0.269 | 0.268 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.007 | 0.041 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".