Substance use care innovations during COVID-19: barriers and facilitators to the provision of safer supply at a toronto COVID-19 isolation and recovery site
Bibliographic record
Abstract
BACKGROUND: Early in the COVID-19 pandemic, there was an urgent need to establish isolation spaces for people experiencing homelessness who were exposed to or had COVID-19. In response, community agencies and the City of Toronto opened COVID-19 isolation and recovery sites (CIRS) in March 2020. We sought to examine the provision of comprehensive substance use services offered to clients on-site to facilitate isolation, particularly the uptake of safer supply prescribing (prescription of pharmaceutical opioids and/or stimulants) as part of a spectrum of comprehensive harm reduction and addiction treatment interventions. METHODS: We conducted in-depth, semi-structured interviews with 25 clients and 25 staff (including peer, harm reduction, nursing and medical team members) from the CIRS in April-July 2021. Iterative and thematic analytic methods were used to identify key themes that emerged in the interview discussions. RESULTS: At the time of implementation of the CIRS, the provision of a safer supply of opioids and stimulants was a novel and somewhat controversial practice. Prescribed safer supply was integrated to address the high risk of overdose among clients needing to isolate due to COVID-19. The impact of responding to on-site overdoses and presence of harm reduction and peer teams helped clinical staff overcome hesitation to prescribing safer supply. Site-specific clinical guidance and substance use specialist consults were crucial tools in building capacity to provide safer supply. Staff members had varied perspectives on what constitutes 'evidence-based' practice in a rapidly changing, crisis situation. CONCLUSION: The urgency involved in intervening during a crisis enabled the adoption of prescribed safer supply, meeting the needs of people who use substances and assisting them to complete isolation periods, while also expanding what constitutes acceptable goals in the care of people who use drugs to include harm reduction approaches.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".