Good Samaritan Drug Overdose Act awareness among people who use drugs in British Columbia, Canada
Bibliographic record
Abstract
Introduction: To address the increase in opioid-related overdoses and deaths in Canada the Good Samaritan Drug Overdose Act (GSDOA) was enacted in May 2017. The GSDOA aims to reduce concerns of police attending overdose events and encourage bystanders to call emergency services. This study explores GSDOA awareness and understanding and the factors associated with GSDOA awareness among people who use drugs (PWUD). Methods: A cross-sectional drug and harm reduction service use survey containing GSDOA-specific questions wasconducted from October to December 2019 at 22 harm reduction supply distribution sites across British Columbia.Descriptive analysis and multivariable logistic regression were conducted to assess correlates of GSDOA awareness. Results: Overall, 54.2% (n = 315) of the eligible study sample (n = 581) reported being aware of the GSDOA. Of respondents reporting awareness, 45.2% and 61.3%, respectively, had a full understanding of when and to whom the GSDOA provides legal protection. In the multivariable model, GSDOA awareness was significantly associated with respondents identifying as cis-men (adjusted odds ratio (AOR) = 2.03 [95% CI: 1.30–3.19]); and those who obtained harm reduction supplies frequently (at least a few times/week) compared with those who did not obtain supplies or obtained them less frequently (AOR = 1.78 [95% CI: 1.14–2.76]). Conclusion: More than 2 years after its introduction, approximately half of harm reduction site clients reported being aware of the GSDOA, and, of these, less than two-thirds had a complete understanding of who is legally protected by the GSDOA. Future GSDOA knowledge dissemination should target PWUD who are less engaged with harm reduction services to improve GSDOA awareness and understanding.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".