Risk mitigation guidance and safer supply prescribing among young people who use drugs in the context of COVID-19 and overdose emergencies
Bibliographic record
Abstract
Across North America, overlapping overdose and COVID-19 emergencies have had a substantial impact on young people who use drugs (YPWUD). New risk mitigation guidance (RMG) prescribing practices were introduced in British Columbia, Canada, in 2020 to allow people to decrease risk of overdose and withdrawal and better self-isolate. We examined how the prescribing of hydromorphone tablets specifically impacted YPWUD's substance use and care trajectories. Between April 2020 and July 2021, we conducted virtual interviews with 30 YPWUD who had accessed an RMG prescription of hydromorphone in the previous six months and 10 addiction medicine physicians working in Vancouver. A thematic analysis was conducted. YPWUD participants highlighted a disjuncture between RMG prescriptions and the safe supply of unadulterated substances such as fentanyl, underscoring that having access to the latter is critical to reducing their reliance on street-based drug markets and overdose-related risks. They described re-appropriating these prescriptions to meet their needs, stockpiling hydromorphone so that it could be used as an "emergency backup" when they were unable to procure unregulated, illicit opioids. In the context of entrenched poverty, hydromorphone was also used to generate income for the purchase of drugs and various necessities. For some YPWUD, hydromorphone prescriptions could be used alongside opioid agonist therapy (OAT) to reduce withdrawal and cravings and improve adherence to OAT. However, some physicians were wary of prescribing hydromorphone due to the lack of evidence for this new approach. Our findings underscore the importance of providing YPWUD with a safe supply of the substances they are actively using alongside a continuum of substance use treatment and care, and the need for both medical and community-based safe and safer supply models.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".