Exploring the knowledge translation of domestic violence research: A literature review
Bibliographic record
Abstract
There is growing recognition of the links between knowledge translation, policy and practice, particularly in the domestic violence research area. A literature review applying a systematic approach with a realist lens was the preferred methodology. The review answered the following question: What are the mechanisms of change in research networks which 'work' to support knowledge translation? A search of eight electronic databases for articles published between 1960 and 2018 was completed, with 2,999 records retrieved, 2,869 records excluded and 130 full-text articles screened for final inclusion in the review. The inclusion criteria were purposefully broad, including any study design or data source (including grey literature) with a focus on domestic violence knowledge translation. The analysis of included studies using a realist lens identified the mechanisms of change to support knowledge translation. A disaggregation of the included studies identified five theories focused on the following outcomes: (1) develop key messages, (2) flexible evidence use, (3) strengthen partnerships, (4) capacity building and (5) research utilisation. This review adds to our understanding of knowledge translation of domestic violence research. The mechanisms of change identified may support knowledge translation of research networks. Further research will focus on exploring the potential application of these program theories with a research network.
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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 | 0.039 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.030 | 0.032 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".