Safer Opioid Supply programs: Hydromorphone prescribing in Ontario as a harm reduction intervention to combat the drug poisoning crisis
Bibliographic record
Abstract
SETTING: The crisis of unregulated fentanyl-related overdose deaths presents a significant public health challenge. This article describes the implementation and evaluation of four Safer Opioid Supply programs (SSPs) in Ontario, one in London and three in Toronto. INTERVENTION AND IMPLEMENTATION: SSPs aim to curtail overdose fatalities while connecting individuals using drugs to healthcare services. The programs involve a daily dispensed prescription of immediate-release hydromorphone tablets for take-home dosing alongside an observed dose of long-acting opioids like slow-release oral morphine. Implemented within a multidisciplinary primary care framework, these programs emphasize patient-centred approaches and comprehensive health and social support. OUTCOMES: In our study conducted in 2020/2021, clients and service providers reported that receiving pharmaceutical opioids through these programs improved the clients' health and well-being. The regulated supply was reported to lead to decreases in overdose incidents, use of unregulated substances, and criminalized activities. Increased engagement with healthcare and harm reduction services and improvements in social determinants of health, such as food security, were also reported. Despite these positive outcomes, some implementation challenges, including capacity issues and provider burnout, were described by service providers. IMPLICATIONS: Our findings suggest that the combination of safer supply, wrap-around support, and harm reduction within primary care settings can lead to increased healthcare engagement, HIV/HCV prevention, testing, and treatment uptake, reducing the burden of infectious diseases and overdose risk. SSPs have the potential to meaningfully reduce overdose rates, address the ongoing overdose crisis, and if scaled up, influence population-level outcomes.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".