Law enforcement and public health approaches in the Asia-Pacific region
Bibliographic record
Abstract
The Asia-Pacific region comprises a large number of countries, all with different policing systems and variations in the extent to which these agencies collaborate with multisectoral partners in response to public health–related issues, including harm reduction, gender-based violence, mental health, and COVID-19 pandemic responses. We reviewed 90 programs involving partnerships and cooperation between law enforcement and public health agencies across the region. From this review, we recommend that police agencies develop training and engage in collaborative engagement with a range of health and community stakeholders to evolve police officers’ views towards a public health perspective in relation to policing activities. Furthermore, law enforcement and public health approaches should embrace technological advancement and innovation to promote both public safety and community health. As a specific example, different areas have employed different strategies to deal with the COVID-19 pandemic, mainly depending upon available resources andcultural and social factors. However, more collaboration between government agencies, the private sector, and NGOs is needed at national and local levels to effectively respond to the pandemic worldwide.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".