Effectively engaging stakeholders and the public in developing violence prevention messages
Bibliographic record
Abstract
BACKGROUND: Preventing family violence requires that stakeholders and the broader public be involved in developing evidence-based violence prevention strategies. However, gaps exist in between what we know (knowledge), what we do (action), and the structures supporting practice (policy). DISCUSSION: We discuss the broad challenge of mobilizing knowledge-for-action in family violence, with a primary focus on the issue of how stakeholders and the public can be effectively engaged when developing and communicating evidence-based violence prevention messages. We suggest that a comprehensive approach to stakeholder and public engagement in developing violence prevention messages includes: 1) clear and consistent messaging; 2) identifying and using, as appropriate, lessons from campaigns that show evidence of reducing specific types of violence; and 3) evidence-informed approaches for communicating to specific groups. Components of a comprehensive approach must take into account the available research evidence, implementation feasibility, and the context-specific nature of family violence. While strategies exist for engaging stakeholders and the public in messaging about family violence prevention, knowledge mobilization must be informed by evidence, dialogue with stakeholders, and proactive media strategies. This paper will be of interest to public health practitioners or others involved in planning and implementing violence prevention programs because it highlights what is known about the issue, potential solutions, and implementation considerations.
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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 | 0.061 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".