Knowledge Translation Plan to Diffuse, Disseminate and Apply Evidence on Children Exposed to Intimate Partner Violence
Bibliographic record
Abstract
Background: A consistent body of evidence shows that childhood exposure to intimate partner violence (IPV) represents a major risk factor for the development of a wide range of short- and long-term adjustment problems that span virtually all spheres of functioning. Although knowledge translation (KT) has the potential to improve the value of research evidence by making it available to professionals who can use them to promote the health of children exposed to IPV such as nurses, KT of research findings concerning this population has received very little attention in the literature.Aim: The purpose of the present paper is to propose a comprehensive end-of-grant KT plan to diffuse and disseminate clinically relevant research evidence on children who have been exposed to IPV to selected knowledge users. In the KT plan, we emphasize the key role that nurse practitioners play as relevant knowledge users and in the implementation of some of the proposed KT strategies.Methods: A systematic literature review was performed to identify research on children and adolescents exposed to IPV upon which we built an evidence-based KT plan targeting a variety of relevant audiences. In designing the KT plan, we adopted the definition of end-of-grant KT developed by the Canadian Institutes of Health Research (CIHR), which includes a wide range of activities targeting different audiences and involving a variety of health professionals, including nurses.Conclusions: Given the high prevalence of childhood exposure to IPV worldwide and the associated adjustment problems children experience, the development of KT strategies to transfer clinically relevant information on this population to relevant knowledge users should be deemed a priority in the professional practice of nurses worldwide.
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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 | 0.186 | 0.257 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".