Take-home drug checking as a novel harm reduction strategy in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Drug checking is a harm reduction strategy used to identify components of illicitly obtained drugs, including adulterants, to prevent overdose. This study evaluated the distribution of take-home fentanyl test strips to people who use drugs (PWUD) in British Columbia, Canada. The primary aim was to assess if the detection of fentanyl in opioid samples was concordant between a take-home model and testing by trained drug checking staff. METHODS: Take-home fentanyl test strips were distributed at ten sites providing drug checking services from April to July 2019. The fentanyl positivity of the aggregate take-home and on-site drug checking groups were compared by class of substance tested. An administered survey assessed acceptability and behaviour change. RESULTS: 1680 take-home results were obtained from 218 unique participants; 68% of samples (n=1142) were identified as opioids and 23% (n=382) were stimulant samples. During this period, 852 samples were tested using on-site drug checking. The fentanyl positivity of opioid samples was 90.0% for take-home samples and 89.1% for on-site samples (Difference 0.8% (95% CI -2.3% to 3.9%)). These results were not affected by previous experience with test strips. Fentanyl positivity of stimulants in the take-home group was higher than on-site (24.7% vs. 3.2%), but the study was underpowered to conduct statistical analysis on this sub-group. When fentanyl was detected, 27% of individuals reported behaviour change that was considered safer/positive. Greater than 95% of participants stated they would use fentanyl test strips again. CONCLUSIONS: Take-home fentanyl test strips used by PWUD on opioid samples can provide similar results to formal drug checking services and are a viable addition to existing overdose prevention strategies. Use of this strategy for detection of fentanyl in stimulant samples requires further evaluation. This intervention was well accepted and in some participants was associated with positive behaviour change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".