The war on drugs is a war on us: young people who use drugs and the fight for harm reduction in the Global South
Bibliographic record
Abstract
In the Global South, young people who use drugs (YPWUD) are exposed to multiple interconnected social and health harms, with many low- and middle-income countries enforcing racist, prohibitionist-based drug policies that generate physical and structural violence. While harm reduction coverage for YPWUD is suboptimal globally, in low- and middle-income countries youth-focused harm reduction programs are particularly lacking. Those that do exist are often powerfully shaped by global health funding regimes that restrict progressive approaches and reach. In this commentary we highlight the efforts of young people, activists, allies, and organisations across some Global South settings to enact programs such as those focused on peer-to-peer information sharing and advocacy, overdose monitoring and response, and drug checking. We draw on our experiential knowledge and expertise to identify and discuss key challenges, opportunities, and recommendations for youth harm reduction movements, programs and practices in low- to middle-income countries and beyond, focusing on the need for youth-driven interventions. We conclude this commentary with several calls to action to advance harm reduction for YPWUD within and across Global South settings.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".