The impact of the COVID-19 pandemic on access to harm reduction and treatment services among people who inject drugs in Toronto, Canada: A qualitative investigation
Bibliographic record
Abstract
ABSTRACT (230 words) The COVID-19 pandemic and related restrictions exacerbated Canada’s ongoing drug toxicity overdose crisis. Opioid agonist therapy (OAT), safer opioid supply (SOS), and supervised consumption sites (SCS) are interventions that aim to reduce risk of morbidity and mortality from drug toxicity and were affected by COVID-19. Between September and October 2020, we conducted qualitative interviews with 24 people who inject drugs receiving services at community harm reduction programs in Toronto, Canada to examine the health and socioeconomic impacts of COVID-related service disruptions. Participants who were already receiving OAT and SOS prior to the start of pandemic reported high levels of continuity of care when pandemic measures were implemented, with medical appointments switching to telemedicine. Participants reported easy access to harm reduction supplies, but those accessing SCS reported increased wait times due to COVID-related capacity restrictions that reduced the number of injection spaces available due to physical distancing requirements. Participants reported extreme difficulty accessing shelter beds and food insecurity due to the closure of drop-in programs, food banks, and food distribution programs and noted the deep impacts these changes had on their health and socioeconomic well-being. Disruption in service delivery of shelters and food programs reveal the need for adaptation of strategies to ensure service continuity. Preparedness planning for future public health emergencies can benefit from analysis of lessons learned, as continuity of care was successfully ensured in OAT, SOS and harm reduction service delivery.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".