Barriers to calling emergency services amongst people who use substances in the event of overdose: A scoping review
Bibliographic record
Abstract
BACKGROUND: North America is grappling with an ongoing drug overdose crisis. While harm reduction measures like take-home naloxone kits, and supervised consumption sites, have helped reduce mortality, other strategies to address this public health emergency are required. Good Samaritan Laws (GSLs) offer legal protection for individuals who report overdoses, yet people who use substances (PWUS) may still hesitate to seek help due to concerns about existing legislation. This scoping review explores barriers preventing PWUS from calling emergency services for overdoses, along with potential solutions and facilitators to address this challenge. METHODS: PRISMA-ScR was used as a guide to conduct this study. Health sciences librarians searched Medline, Embase, PsychINFO, CINAHL, and SCOPUS to identify relevant articles. Six reviewers contributed to screening and extracting the articles through Covidence. Two reviewers performed thematic analysis using NVivo software to identify key barriers and facilitators. RESULTS: An initial search found 6275 articles for title and abstract screening, resulting in 48 studies meeting the inclusion criteria. The primary barrier to calling 911 pertained to concerns about police arrivng with other first responders, especially regarding their presence and involvement at the scene of overdose. This was followed by legal repercussions, including fear of arrest, incarceration, and fear of eviction, amongst others. Some studies noted the lack of knowledge or trust in GSLs as a deterrent to seeking medical assistance. Additional barriers included concerns about privacy and confidentiality, preference to manage an overdose alone/receive help from another peer, confidence in naloxone effectiveness, limited access to cell phones, peer pressure to not call for help, and identifying as Black, Indigenous, or a Person of Colour (BIPOC). Facilitators include increased GSL awareness among PWUS and law enforcement, expanded legal safeguards for 911 callers, reduced police intervention in overdose cases, and enhanced naloxone availability at key access points. CONCLUSION: Despite the good intentions of GSLs, PWUS continue to experience significant barriers to calling emergency services in the event of an overdose. Understanding these barriers and key facilitators is necessary to inform future drug policy and advocacy efforts.
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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 | 0.019 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".