“There are solutions and I think we're still working in the problem”: The limitations of decriminalization under the good Samaritan drug overdose act and lessons from an evaluation in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Drug prohibition has been associated with increased risk of overdose. However, drug prohibition remains the dominant drug policy, including in Canada with the Controlled Drugs and Substances Act. In 2017, the Good Samaritan Drug Overdose Act (GSDOA) was enacted, to encourage people to contact emergency medical services by providing bystanders at the scene of an overdose with legal protection for simple possession and conditions related to simple possession. METHODS: We conducted an evaluation of the GSDOA in British Columbia, Canada that included one-on-one interviews with people who use illicit drugs (PWUD), to determine peoples' experiences and perceptions surrounding this form of decriminalization. We present findings from a thematic analysis of 37 interviews. RESULTS: We identified limitations of the GSDOA at overdose events; key themes and concerns causing PWUD to hesitate to or avoid contacting emergency medical services included drug confiscation, the thin line between simple possession and drug trafficking, and enforcement of other charges and court ordered conditions that are not legally protected by the GSDOA. Moreover, participants discussed the GSDOA as inequitable; benefiting some while excluding PWUD with intersecting marginalized identities. CONCLUSION: Our findings are pertinent in light of many jurisdictions across the world considering dejure decriminalization, including BC and Vancouver. The GSDOA and associated limitations that emerged in our evaluation can serve to guide jurisdictions implementing or amending dejure decriminalization policies.
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 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 | 0.057 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.038 | 0.028 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.010 |
| 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".