Moving towards a continuum of safer supply options for people who use drugs: A qualitative study exploring national perspectives on safer supply among professional stakeholders in Canada
Bibliographic record
Abstract
BACKGROUND: Novel public health interventions are needed to address the toxic drug supply and meet the needs of people who use drugs amidst the overdose crisis. Safer supply - low-barrier distribution of pharmaceutical grade substances - has been implemented in some jurisdictions to provide safer alternatives to the unregulated drug supply, yet no studies to date have explored professional stakeholder perspectives on this approach. METHODS: We used purposive sampling to recruit professional stakeholders (n = 17) from four locations in British Columbia, Ontario, and Nova Scotia, including program managers, executive directors, political and health authority representatives, and healthcare providers involved in the design, implementation, and/or operation of safer supply programs in their communities. Semi-structured, one-to-one interviews were conducted, and interview data were coded and analyzed using thematic analyses. RESULTS: Participants defined safer supply as low-barrier access to substances of known quality and quantity, offered on a continuum from prescribed to a legal, regulated supply, and focused on upholding autonomy and liberation of people who use drugs. Stakeholders expressed support for safer supply but explained that current iterations do not meet the needs of all people who use drugs and that implementation is limited by a lack of willing prescribers, stigma towards people who use drugs, and precarity of harm reduction programs to political ideology. Stakeholders expressed strong support for wider-reaching approaches such as decriminalization, legalization, and regulation of substances as a way to fully realize a continuum of safer supply, directly address the overdose crisis and toxic drug supply, and ensure equity of access nationally. CONCLUSION: The results of this study highlight the need for innovative strategies to address the overdose crisis and that safer supply has the potential to benefit certain people who use drugs. A one-size-fits-all approach is not sufficient and the perspectives of professional stakeholders should be considered alongside those of people who use drugs when designing and implementing future safer supply.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".