Drug trafficking, use, and HIV risk: The need for comprehensive interventions
Bibliographic record
Abstract
The rapid increase in communication and transportation between Africa and other continents as well as the erosion of social fabric attended by poverty, ethnic conflicts, and civil wars has led to increased trafficking and consumption of illicit drugs. Cannabis dominates illicit trade and accounts for as much as 40% of global interdiction. Due to escalating seizures in recent years, the illicit trade in heroin and cocaine has become a concern that has quickly spread from West Africa to include Eastern and Southern Africa in the past 10 years. All regions of Africa are characterized by the use of cannabis, reflecting its entrenched status all over Africa. Most alarming though is the use of heroin, which is now being injected frequently and threatens to reverse the gain made in the prevention of HIV/AIDS. The prevalence of HIV infection and other blood-borne diseases among injection drug users is five to six times that among the general population, calling for urgent intervention among this group. Programs that aim to reduce the drug trafficking in Africa and needle syringe programs as well as medication-assisted treatment (MAT) of heroin dependence while still in their infancy in Africa show promise and need to be scaled up.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 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".