Probing trends of opioid seizures and drug checking samples under a nascent “decriminalization” framework in Vancouver, Canada
Bibliographic record
Abstract
Background We sought to examine nascent trends surrounding the impacts of police drug seizures in the period after a substantial public policy shift surrounding the personal possession of drugs in British Columbia (BC), Canada. We explored the intersection of drug testing and police opioid seizures in Vancouver, BC. Methods Our descriptive analysis draws on internal police drug seizure data and public drug checking data, using simple linear regression and correlates to measure associations between the quantity of police-reported opioid seizures and the percentage of drug samples that contained both fentanyl and benzodiazepines during the first 6 months of BC's decriminalization framework. A one-month lag was applied to drug-checking data to account for the time delay between seizures and their impact on the drug supply. Results In the 6 month period between 1 February and 31 July 2023, police recorded 1805 drug seizures, of which ∼42.7% were opioids ( n = 771). The drug sample database recorded 9085 drug samples that were tested for fentanyl and/or benzodiazepines. An early potential relationship ( p -value = 0.03) was observed between drug samples that tested positive for both benzodiazepines and fentanyl with the raw number of monthly opioid seizures by the Vancouver Police Department. Conclusions Our descriptive analysis shows a potential link between drug samples testing positive for both benzodiazepines and fentanyl, and the quantity of opioid seizures by Vancouver Police Department officers, and highlights considerations of removing police from managing drug use and possession.
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 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 | 0.001 |
| 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) | 0.000 | 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".