Wales without violence: A framework for preventing violence among children and young people
Bibliographic record
Abstract
Violence among children and young people (CYP) is a complex societal issue that has detrimental impacts on the health and well-being of children, young people, and adults throughout their lives. Population health research tells us that CYP are adversely at risk of experiencing violence and are at higher risk of experiencing multiple forms of violence. However, evidence suggests that prevention approaches are most effective when implemented with CYP and can have positive health, well-being, and social impacts across the life-course. This social innovation narrative sets out how the Wales Violence Prevention Unit and Peer Action Collective Cymru coproduced a strategic multi-agency framework for the prevention of violence among CYP in Wales. The first of its kind to be developed in the United Kingdom, this national framework acts as a guide to strategic action on violence prevention, amplifying the voices of CYP, and providing evidence of “what works.” This evidence-informed, coproduced framework used an innovative participatory design process to listen to the voices of a diverse range of stakeholders, highlighting the voices of CYP. Informed by the views and experiences of over 1,000 people in Wales, and grounded in the lived experiences of CYP, the Framework proposes nine strategies to prevent violence among CYP as part of a public health approach to violence prevention. These strategies represent evidence-based approaches proven to reduce violence among CYP, address the risk factors for youth violence, and build individual, community and societal resilience.
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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 | 0.022 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".