The impact of the COVID-19 pandemic on people who use drugs in three Canadian cities : a cross-sectional analysis
Bibliographic record
Abstract
Background: The COVID-19 pandemic had a disproportionate impact on the health and wellbeing of people who use drugs (PWUD) in Canada. However less is known about jurisdictional commonalities and differences in COVID-19 exposure and impacts of pandemic-related restrictions on competing health and social risks among PWUD living in large urban centres. Methods: Between May 2020 and March 2021, leveraging infrastructure from ongoing cohorts of PWUD, we surveyed 1,025 participants from Vancouver (n = 640), Toronto (n = 158), and Montreal (n = 227), Canada to describe the impacts of pandemic-related restrictions on basic, health, and harm reduction needs. Results: Among participants, awareness of COVID-19 protective measures was high; however, between 10 and 24% of participants in each city-specific sample reported being unable to self-isolate. Overall, 3–19% of participants reported experiencing homelessness after the onset of the pandemic, while 20–41% reported that they went hungry more often than usual. Furthermore, 8–33% of participants reported experiencing an overdose during the pandemic, though most indicated no change in overdose frequency compared the pre-pandemic period. Most participants receiving opioid agonist therapy in the past six months reported treatment continuity during the pandemic (87–93%), however, 32% and 22% of participants in Toronto and Montreal reported missing doses due to service disruptions. There were some reports of difficulty accessing supervised consumption sites in all three sites, and drug checking services in Vancouver. Conclusion: Findings suggest PWUD in Canada experienced difficulties meeting essential needs and accessing some harm reduction services during the COVID-19 pandemic. These findings can inform preparedness planning for future public health emergencies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".