A drug-related Good Samaritan Law and calling emergency medical services for drug overdoses in a Canadian setting
Bibliographic record
Abstract
BACKGROUND: People who use drugs (PWUD) are known to fear calling emergency medical services (EMS) for drug overdoses. In response, drug-related Good Samaritan Laws (GSLs) have been widely adopted in the USA and Canada to encourage bystanders to call emergency medical services (EMS) in the event of a drug overdose. However, the effect of GSLs on EMS-calling behaviours has been understudied. We sought to identify factors associated with EMS-calling, including the enactment of the Canadian GSL in May 2017, among PWUD in Vancouver, Canada, a setting with an ongoing overdose crisis. METHODS: Data were derived from three prospective cohort studies of PWUD in Vancouver in 2014-2018. Multivariable logistic regression was used to determine factors associated with EMS-calling among PWUD who witnessed an overdose event. An interrupted time series (ITS) analysis was employed to assess the impact of GSL on monthly prevalence of EMS-calling. RESULTS: Among 540 eligible participants, 321 (59%) were males and 284 (53%) reported calling EMS. In multivariable analysis, ever having administered naloxone three or more times (adjusted odds ratio [AOR] 2.00; 95% confidence interval [CI] 1.08-3.74) and residence in the Downtown Eastside (DTES) neighbourhood of Vancouver (AOR 1.96; 95% CI 1.23-3.13) were positively associated with EMS-calling, while living in a single occupancy hotel (SRO) was negatively associated with EMS-calling (AOR 0.51; 95% CI 0.30-0.86). The post-GSL enactment period was not associated with EMS-calling (AOR 0.81; 95% CI 0.52-1.25). The ITS found no significant difference in the monthly prevalence of EMS-calling between pre- and post-GSL enactment periods. CONCLUSION: We observed EMS being called about half the time and the GSL did not appear to encourage EMS-calling. We also found that individuals living in SROs were less likely to call EMS, which raises concern given that fatal overdose cases are concentrated in SROs in our setting. The link between many naloxone administrations and EMS-calling could indicate that those with prior experience in responding to overdose events were more willing to call EMS. Increased efforts are warranted to ensure effective emergency responses for drug overdoses among PWUD.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".