COVID-19 and the Health of Illicit Substance Users: Preliminary Analysis from Illicit Drug Transaction Data
Bibliographic record
Abstract
Background: While much attention has been given to how COVID-19 patients are treated (or fail to be treated), the impact of the pandemic on illicit drug users remains largely undiscussed.The consequences of COVID-19 on substance users and on the health care system are exposed.Objectives: The aim of this short report is to understand the health issues that illicit drug users may be currently facing following the lockdowns due to the COVID-19 pandemic. Methods:We analysed 262 self-reported submissions of illicit drug transactions on the darkweb.The self-reports include the date of the transaction, the types of illicit drugs bought/sold, and whether the shipment of the illicit drugs succeeded, had issues (ex.unusually long delivery, an error in the type of drug shipped, quantity or concentration of the drug), or failed.Results: Between January 1 st 2020 and March 21 st , 2020, deliveries of illicit drug on the darkweb were mostly successful (60% to 100%).Starting on March 21 st , the number of shipments that had issues or failed to be delivered increased rapidly and represented a majority of all shipments (79%). Conclusion:The flow of darkweb drugs has been disrupted at the same time as COVID-19 pandemic started to lead to lockdowns.This suggests that the lockdowns could have disrupted the sourcing of illicit drugs, thereby possibly impacting the health of illicit drug users.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".