“COVID just kind of opened a can of whoop-ass”: The rapid growth of safer supply prescribing during the pandemic documented through an environmental scan of addiction and harm reduction services in Canada
Bibliographic record
Abstract
OBJECTIVES: In the context of the ongoing overdose crisis, a stark increase in toxic drug deaths from the unregulated street supply accompanied the onset of the COVID-19 pandemic. Injectable opioid agonist treatment (iOAT - hydromorphone or medical-grade heroin), tablet-based iOAT (TiOAT), and safer supply prescribing are emerging interventions used to address this crisis in Canada. Given rapid clinical guidance and policy change to enable their local adoption, our objectives were to describe the state of these interventions before the pandemic, and to document and explain changes in implementation during the early pandemic response (March-May 2020). METHODS: Surveys and interviews with healthcare providers comprised this mixed methods national environmental scan of iOAT, TiOAT, and safer supply across Canada at two time points. Quantitative data were summarized using descriptive statistics; interview data were coded and analyzed thematically. RESULTS: 103 sites in 6 Canadian provinces included 19 iOAT, 3 TiOAT and 21 safer supply sites on March 1, 2020; 60 new safer supply sites by May 1 represented a 285% increase. Most common substances were opioids, available at all sites; most common settings were addiction treatment programs and primary care clinics, and onsite pharmacies models. 79% of safer supply services were unfunded. Diversity in service delivery models demonstrated broad adaptability. Qualitative data reinforced the COVID-19 pandemic as the driving force behind scale-up. DISCUSSION: Data confirmed the capacity for rapid scale-up of flexible, community-based safer supply prescribing during dual public health emergencies. Geographical, client demographic, and funding gaps highlight the need to target barriers to implementation, service delivery and sustainability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".