Vietnam’s policing in harm reduction: Has one decade seen changes in drug control?
Bibliographic record
Abstract
Alongside raising awareness and creating activities to develop a harm-reduction approach in the HIV/AIDS campaign since the end of the 2000s, broader harm-reduction interventions in Vietnam were also deployed that included several positive steps. Police forces, a fundamental sector in reducing the supply of illicit drugs, were also involved, partly to concretize this approach. As the first paper to examine the role of police in harm-reduction interventions in Vietnam, the current study utilizes qualitative approaches relying on in-depth interviews conducted with multiple key informants from government and its related bodies, United Nations personnel, and non-government organizations (NGOs), as well as police officers. We uncover noticeable progress in changing minds and approaches to apply harm reduction in drug policy, particularly within policing. However, major barriers in regulations, slow acceptance by police forces, and a lack of curriculum and courses in police training have limited harm-reduction approaches. As the first study to review and assess the policy of harm reduction after one decade, the paper contributes to a deeper understanding of the nature of Vietnam’s police provisions to balance and improve harm reduction in drug control.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".