‘I won’t make it without this program’: the impact of safer opioid supply program closures in Ontario
Bibliographic record
Abstract
BACKGROUND: Canada is in the midst of a worsening overdose crisis, driven largely by the unregulated drug supply. In response, safer opioid supply (SOS) programs were implemented to provide pharmaceutical-grade opioids alongside critical services. However, in August 2024, Ontario's provincial government introduced restrictions on harm reduction initiatives, coinciding with the expiration of federal funding, forcing many programs to close. In their place, the government announced the implementation of Homelessness and Addiction Recovery Treatment Hubs, which exclude harm reduction programs, including SOS programs. This study explores the experiences of SOS program clients and the anticipated impacts of these program closures on their lives. METHODS: A qualitative study design was used, involving semi-structured interviews with people who use drugs who were enrolled in six SOS programs across Ontario. Participants were recruited through convenience and snowball sampling. Eligible participants were current SOS clients (≥ 6 months), aged 18 or older, and English-speaking. Interviews were conducted virtually and explored participants' experiences with SOS programs, anticipated impacts of program closures, and strategies to mitigate risks. Data were thematically analyzed using NVivo. RESULTS: Participants reported that SOS programs reduced their reliance on the unregulated drug supply, decreased their overdose risk, and connected them to wraparound services. The impending closures triggered widespread fear, uncertainty, and anxiety, particularly about returning to the unregulated supply. Participants also expressed concerns over the loss of access to critical health and social services and the potential decline in their quality of life. Many expressed frustration over the lack of meaningful alternatives, difficulties in securing new prescribers, and distress over forced medication tapers. CONCLUSION: This study highlights participants' concerns that SOS program closures may force them back into an increasingly dangerous unregulated market, ultimately putting their lives at risk, along with reversing the many benefits SOS programs provided, such as connections to essential health and social services. By replacing harm reduction programs with treatment services, the government is not reducing the demand for opioid use; instead, it forces a return to the unregulated drug market, ultimately putting individuals at risk of overdose.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".