Sharing drug checking results in a Canadian setting: a multi-site analysis
Bibliographic record
Abstract
BACKGROUND: Drug checking services (DCS) have been implemented as a harm reduction measure to address high rates of illicit drug morbidity and mortality. In addition to reducing individual-level risk, there is also potential for DCS users to increase community level awareness of drug market conditions through sharing of drug checking results. However, little is known about the patterns of information sharing among people who use DCS. METHODS: Data were derived from a cross-sectional study conducted at 22 community harm reduction sites offering DCS across British Columbia (BC) between March 2021 and July 2024. Two variable selection methods - backwards stepwise selection and elastic net regularization - were used to fit models which explored the relationships between select socio-demographic characteristics, drug use patterns and experiences with drug checking with the main outcome measure: sharing drug checking results. RESULTS: 516 participants were included in the study (34 % women, median age 42); 274 (53 %) reported that they shared their drug checking results. Factors significantly and positively associated with sharing results in multivariable logistic regression analysis using both selection methods included: drug dealing, using DCS more than once, stable employment and white ethnicity. Among participants who reported they shared their drug checking results, 66 % reported sharing results with friends and family and 28 % reported sharing results with their drug dealer. CONCLUSION: This study revealed that over half the study sample shared their drug checking results with others, with results sharing being positively associated with recent drug dealing, frequent DCS utilisation, employment and ethnicity. Drug checking results were most often shared with friends, family and drug dealers. These findings extend the evidence base of DCS as a harm reduction tool by demonstrating how engagement with DCS facilitates information dissemination amongst drug market actors.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".