A scoping review and concept analysis to inform Canada’s safe(r) opioid supply research agenda
Bibliographic record
Abstract
• “Safe(r) supply” lumps various approaches together; yet, approaches range widely from prescription-based (medicalized) to non-prescription-based (non-medicalized). • Medicalized and non-medicalized approaches share a core harm-reduction intent but differ in their underlying philosophies, key characteristics, and potential benefits and harms. • Standardized terminology is recommended, transitioning away from using the language of “safer supply” in reference to medical models and adopting the terminology of “prescribed opioid alternative interventions” to reflect the range models of care situated along the continuum from harm reduction to treatment. • Systematic data collection, standardized documentation, and robust evaluation are needed. Providing pharmaceutical opioid medications as alternatives to the unregulated drug market, commonly referred to as safe supply or safer supply (hereafter “safe(r) supply”), has emerged as a harm reduction strategy in Canada, with wide variation in principles and implementation. We aimed to clarify the concept of safe(r) opioid supply across harm-reduction and clinical contexts. We conducted a scoping review and concept analysis. We systematically searched six major electronic databases and the grey literature to identify articles published between 2010 and 2024. Informed by Walker and Avant’s concept analysis methodology, we extracted definitions and descriptions of programs and interventions, organizing key characteristics into thematic dimensions to develop a framework distinguishing various care approaches. Our review included 95 articles. Safe(r) supply operationalizes under two broad approaches: a medicalized/prescribed approach (‘safer supply’) and a non-medicalized/community-based approach (‘safe supply’). We outlined three illustrative cases that nest within these approaches: (1) Prescribed opioids with opioid agonist therapy (OAT) offered and/or co-prescribed, (2) Prescribed opioids without OAT, (3) Community-based distribution of unregulated drugs with known composition. Safe(r) supply encompasses prescribed opioid alternatives interventions (safer supply) and non-medicalized (safe supply) approaches with shared antecedents but distinct attributes and consequences. This study highlights the need to better define and standardize the parameters of safer supply approaches, including population, dosing, and intended objectives, to enable a more precise assessment of their potential benefits and risks. This nuanced understanding is crucial for developing evidence-based strategies in response to Canada’s drug poisoning crisis.
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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 | 0.144 | 0.265 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.099 | 0.082 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".