Alternative approaches to achieving community safety and well-being across law enforcement and public health: Western European findings
Bibliographic record
Abstract
This paper provides the results from Western Europe of a wider project (Envisaging the Future of Policing and Public Health Globally) for the Global Law Enforcement and Public Health Association (GLEPHA) which aimed to identify policing and public health alternative initiatives to provide community safety and well-being. A desktop review of projects that included evaluation evidence and/or impact of innovative delivery were selected for the study. The criteria allowed the inclusion of international, national, regional, and local initiatives that fit the broader aims of the global “envisaging the future” GLEPHA project. In total, 41 projects were reviewed with varying levels of information on approach and evaluation. Data capture recorded the country, location, funder details, themes (e.g., violence, mental health, drugs), key words, program descriptions, and any links and key findings from evaluation studies. A number of key themes, drivers, and challenges were identified in collaborative work between policing and public health. These included elements of communication and generating a shared language, the need for evaluation to be embedded in the project plan and mobilisation, and the problems with “hot-topic” issues and short-term funding. This paper also outlines two case studies of projects within Western Europe: Violence Reduction Units in the United Kingdom, and the Stockholm prevents Alcohol and Drug Problems (STAD). Key aspects of these projects are presented and the successes and potential challenges discussed. Key recommendations regarding the future of law enforcement and public health–related initiatives are discussed.
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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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".