The role of public health in the primary prevention of interpersonal violence: A systematic review of international frameworks
Bibliographic record
Abstract
In recent years, there has been a surge of interest in violence as a public health issue. Preventing violence before it occurs and developing effective response strategies are key to achieving the United Nations Sustainable Development Goals and improving health and well-being. This systematic scoping review explores the role of public health frameworks in the primary prevention of interpersonal violence. A systematic literature search was undertaken to identify frameworks from both academic and grey literature. Extracted records (n = 17) were thematically analyzed to explore themes, divergences, and theoretical underpinnings. Most frameworks were published in the last decade by national and international public health bodies. The majority were from high-income countries and explored a range of interpersonal violence types. Nine themes were identified, which provide opportunities for violence prevention across the socio-ecological model, including: families, caregivers, and early years; early identification and support; schools, education, and skill development; safe community environments; safe activities and trusted adults; social norms and values; empowerment and equality; policy and legislation; and poverty reduction. These frameworks evidence the leadership role played by public health in the development and implementation of the primary prevention of violence. However, to effectively embed a public health approach, the review identified several areas which warrant further attention. These included redressing disparities in evidence, particularly from low income countries; building the evidence base for addressing community and structural determinants of violence such as gender, poverty, and inequality; and investing in research which explores the implementation of primary prevention approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.027 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".