HIV, drugs and the legal environment
Bibliographic record
Abstract
A large body of scientific evidence indicates that policies based solely on law enforcement without taking into account public health and human rights considerations increase the health risks of people who inject drugs (PWIDs) and their communities. Although formal laws are an important component of the legal environment supporting harm reduction, it is the enforcement of the law that affects PWIDs' behavior and attitudes most acutely. This commentary focuses primarily on drug policies and policing practices that increase PWIDs' risk of acquiring HIV and viral hepatitis, and avenues for intervention. Policy and legal reforms that promote public health over the criminalization of drug use and PWID are urgently needed. This should include alternative regulatory frameworks for illicit drug possession and use. Changing legal norms and improving law enforcement responses to drug-related harms requires partnerships that are broader than the necessary bridges between criminal justice and public health sectors. HIV prevention efforts must partner with wider initiatives that seek to improve police professionalism, accountability, and transparency and boost the rule of law. Public health and criminal justice professionals can work synergistically to shift the legal environment away from one that exacerbates HIV risks to one that promotes safe and healthy communities.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| 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".