“It’s just a perfect storm”: Exploring the consequences of the COVID-19 pandemic on overdose risk in British Columbia from the perspectives of people who use substances
Bibliographic record
Abstract
BACKGROUND: Despite the implementation and expansion of public health and harm reduction strategies aimed at preventing and reversing overdoses, rates of overdose-related events and fatalities continue to rise in British Columbia. The COVID-19 pandemic created a second, concurrent public health emergency that further exacerbated the illicit drug toxicity crisis, reinforced existing social inequities and vulnerabilities, and highlighted the precariousness of systems in place that are meant to protect the health of communities. By exploring the perspectives of people with recent experience of illicit substance use, this study sought to characterize how the COVID-19 pandemic and associated public health measures influenced risk and protective factors related to unintentional overdose by altering the environment in which people live and use substances, influencing the ability of people who use substances to be safe and well. METHODS: One-on-one semi-structured interviews were conducted by phone or in-person with people who use illicit substances (n = 62) across the province. Thematic analysis was performed to identify factors shaping the overdose risk environment. RESULTS: Participants pointed to factors that increased risk of overdose, including: [1] physical distancing measures that created social and physical isolation and led to more substance use alone without bystanders nearby able to respond in the event of an emergency; [2] early drug price spikes and supply chain issues that created inconsistencies in drug availability; [3] increasing toxicity and impurities in unregulated substances; [4] restriction of harm reduction services and supply distribution sites; and [5] additional burden placed on peer workers on the frontlines of the illicit drug toxicity crisis. Despite these challenges, participants highlighted factors that protected against overdose and substance-related harm, including the emergence of new programs, the resiliency of communities of people who use substances who expanded their outreach efforts, the existence of established social relationships, and the ways that individuals consistently prioritized overdose response over concerns about COVID-19 transmission to care for one another. CONCLUSIONS: The findings from this study illustrate the complex contextual factors that shape overdose risk and highlight the importance of ensuring that the needs of people who use substances are addressed in future public health emergency responses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".