Knowledge of a Drug-Related Good Samaritan Law Among People Who Use Drugs, Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: Across the United States and Canada drug-related Good Samaritan laws (GSLs) have been enacted to encourage observers of acute drug overdose events to contact emergency medical services (EMS) without fear of legal repercussions. However, little is known about the working knowledge of GSLs among people who use illicit drugs (PWUD). We sought to evaluate the prevalence and factors associated with accurate knowledge of a GSL among PWUD in Vancouver, Canada, 1 year after the GSL was enacted. METHOD: We used data from participants in three community-recruited prospective cohort studies of PWUD interviewed between June and November 2018. Multivariable logistic regression was used to identify factors associated with accurate knowledge of the GSL. RESULTS: Among 1,258 participants, including 760 males (60%), 358 (28%) had accurate knowledge of the GSL. In multivariable analyses, participants who reported ever having a negative police encounter (defined as being stopped, searched, or detained by the police) were less likely to have accurate knowledge of the GSL (adjusted odds ratio [AOR] = 0.70; 95% CI [0.54, 0.90]), while those involved in drug dealing were more likely to have accurate knowledge of the GSL (AOR = 1.50; 95% CI [1.06, 2.06]). DISCUSSION: Despite having been enacted for a full year, approximately three quarters of participants did not have accurate GSL knowledge, warranting urgent educational efforts among PWUD. Additional research is needed to understand whether GSLs can mitigate the fears of legal repercussions among those engaged in drug dealing and with past negative experiences with the police.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".