“As long as that place stays open, I’ll stay alive”: Accessing injectable opioid agonist treatment during dual public health crises
Bibliographic record
Abstract
BACKGROUND: Since the onset of the COVID-19 pandemic, overdose rates in North America have continued to rise, with more than 100,000 drug poisoning deaths in the past year. Amidst an increasingly toxic drug supply, the pandemic disrupted essential substance use treatment and harm reduction services that reduce overdose risk for people who use drugs. In British Columbia, one such treatment is injectable opioid agonist treatment (iOAT), the supervised dispensation of injectable hydromorphone or diacetylmorphine for people with opioid use disorder. While evidence has shown iOAT to be safe and effective, it is intensive and highly regimented, characterized by daily clinic visits and provider-client interaction-treatment components made difficult by the pandemic. METHODS: Between April 2020 and February 2021, we conducted 51 interviews with 18 iOAT clients and two clinic nurses to understand how the pandemic shaped iOAT access and treatment experiences. To analyze interview data, we employed a multi-step, flexible coding strategy, an iterative and abductive approach to analysis, using NVivo software. RESULTS: Qualitative analysis revealed the ways in which the pandemic shaped clients' lives and the provision of iOAT care. First, client narratives illuminated how the pandemic reinforced existing inequities. For example, socioeconomically marginalized clients expressed concerns around their financial stability and economic impacts on their communities. Second, clients with health comorbidities recognized how the pandemic amplified health risks, through potential COVID-19 exposure or by limiting social connection and mental health supports. Third, clients described how the pandemic changed their engagement with the iOAT clinic and medication. For instance, clients noted that physical distancing guidelines and occupancy limits reduced opportunities for social connection with staff and other iOAT clients. However, pandemic policies also created opportunities to adapt treatment in ways that increased patient trust and autonomy, for example through more flexible medication regimens and take-home oral doses. CONCLUSION: Participant narratives underscored the unequal distribution of pandemic impacts for people who use drugs but also highlighted opportunities for more flexible, patient-centered treatment approaches. Across treatment settings, pandemic-era changes that increase client autonomy and ensure equitable access to care are to be continued and expanded, beyond the duration of the pandemic.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".