Building a Wales without violence: Using behavioural science to implement a public health approach
Bibliographic record
Abstract
Violence among children and young people is preventable through a public health approach. However, there remains a scarcity of knowledge about its implementation to effect system-level change for violence prevention and the range of public health actions available to support it. This article describes how the Wales Violence Prevention Team (VPT), Public Health Wales, applied behavioural science to inform the implementation of Wales Without Violence – a co-produced framework for preventing violence among children and young people. Using the capability, opportunity, motivation, behaviour model, the VPT engaged professionals across sectors to identify the behaviours, barriers, and facilitators for embedding the framework’s nine violence prevention principles. The process enabled an exploration of the support needed to enhance professionals’ capability, opportunity, and motivation for adopting a public health approach to violence prevention. The application of behavioural science to explore barriers and support needs for professionals involved in violence prevention also supported the VPT in clarifying its own role within the violence prevention landscape in Wales to maximize its resources. This article provides insights for advancing violence prevention activity through a public health approach.
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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 | 0.051 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".