Third party drug checking: accessing harm reduction services on the behalf of others
Bibliographic record
Abstract
BACKGROUND: Drug checking uses chemical analytical technologies to analyze drugs from the unregulated market to reduce substance use-related risks. We aim to examine the frequency of third party use of a community drug checking service to explore the potential for harm reduction to extend beyond the individual into the community, increase service accessibility, and to contribute to upstream interventions in the supply. METHODS: Over 31 months, data were collected from a point-of-care drug checking service operated in Victoria, Canada. Through the implementation of survey questions at the intake of the service, data were collected about whether the drug check was for the individual, to sell, and/or for others. RESULTS: Just over half (52%) of service users were checking for reasons that extended beyond individual use. When checking for others, friends were the most common response, representing 52% of responses, and outreach/support workers checking for others was the second most at 32%. Twelve percent of service users reported checking to sell or for a supplier. CONCLUSIONS: Third party checking is a frequent, and important aspect of drug checking services, which through facilitating community engagement and increasing accessibility, has expanded the reach of interventions beyond individuals to reduce risks within the unregulated market. Therefore, drug checking as an overdose response should be responsive and accessible for those using the service on the behalf of others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".