Exploring the role of peers in addressing the toxic drug crisis in emergency departments
Bibliographic record
Abstract
Introduction: Health systems across Canada are facing high numbers of patients who use substances, have a diagnosed substance use disorder, or are experiencing a toxic drug poisoning, necessitating innovative approaches to care. The article discusses a unique pilot project that incorporates individuals with experience using unregulated substances (i.e., peers) into emergency departments to improve patient outcomes and enhance staff satisfaction, in response to the significant impact of the toxic drug crisis on healthcare systems. Methods: The project used an overarching Plan-Do-Study-Act quality improvement framework, and an adapted ‘Four Approaches to Evaluation’ to assess the impacts of embedding peers into the emergency department. Data collection included quantitative/qualitative intake forms, patient/staff experience surveys, and a focus group. Results: The most common reasons for peer encounters (N = 764) were emotional support, harm reduction, referrals, witnessed consumption, and requests for necessities. The patient survey (N = 51) results demonstrated how the peers helped majority of patients feel safe and more supported while accessing emergency care. ED staff (N = 22) shared positive experiences with the new program, citing improvements in quality of life, access to harm reduction services, and creating a more supportive health system. During focus groups, peers (N = 2) outlined the importance of having this role embedded into emergency departments to ensure patients are receiving the care they need in a high-stress environment that, historically, has had the potential to cause significant harm through stigma and biases to people who use substances. Conclusion: Integrating peers into EDs during the toxic drug crisis has greatly improved support for both patients and staff. This approach boosts staff morale, reduces workload stress, decreases stigma, and enhances patient care. Overall, it optimizes resources and strengthens both patient and provider experiences.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".