A sea of need: provider accounts of strategies used to manage admission demands to safer opioid supply programs in Ontario
Bibliographic record
Abstract
BACKGROUND: Since 2016, over 50,928 people have died of an opioid-related overdose in Canada. The unregulated supply of drugs is increasingly toxic and volatile, and fentanyl from unregulated, street-based markets is driving this epidemic. Concerns that existing overdose prevention approaches were insufficient to address the rising number of overdoses led to the implementation of safer supply programs (SSPs) in Canada. SSPs provide prescribed medications to people who use drugs and are designed for individuals at high risk of overdose for whom existing care options have been ineffective or inappropriate. Evidence of SSP impact is growing but implementation processes, including admissions, are not well understood nor well-described in practice guidelines. Our purpose was to describe how the admission processes of four Ontario SSPs evolved and how these changes influenced program reach and perceived effectiveness. METHODS: During 2021, we conducted short demographic and semi-structured interviews with healthcare providers (n = 21) from four SSPs in Ontario about implementation processes, challenges, and impacts. Thematic analysis of data concerning admission processes was conducted in MAXQDA and descriptive statistics in SPSSv28. RESULTS: Although the desire was for SSPs to have a broad reach, programs quickly realized they needed to develop strategies to manage the high demand for their programs. To manage this demand, strategies were implemented like waitlists, which were later replaced by points-based admission criteria. These admission criteria evolved over time, leading to a client population with high medical and social needs. The combination of high-acuity clients, limited capacity, and funding constraints, exacerbated by COVID-19, caused significant distress and burnout among service providers, prompting further changes to the SSPs. DISCUSSION: The implementation of SSPs in Ontario highlights the challenges of addressing intersecting public health emergencies in a resource-constrained healthcare system. SSPs, were adaptive and evolved in real time; while these adaptations addressed significant equity gaps, they also underscored the limitations of operating within an under-funded primary care model. The narrowing of admission criteria, necessitated by overwhelming demand and limited resources, ultimately constrained their reach and potential population-level impact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".