‘It just doesn't stop’: Perspectives of women who use drugs on increased overdoses during the <scp>COVID</scp>‐19 pandemic
Bibliographic record
Abstract
INTRODUCTION: In Canada, the COVID-19 pandemic collided with an ongoing overdose crisis driven by a toxic unregulated drug supply. Public health guidance intended to limit transmission of COVID-19 (e.g., social distancing) directly contradicted guidance responding to the ongoing overdose crisis (e.g., never use drugs alone), exacerbating harms among people reliant on the toxic unregulated drug supply. While existing literature characterises many harms associated with consuming unregulated drugs during COVID-19, less is known about the specific impacts on women. We explored the perspectives of women who use unregulated drugs and experienced socio-economic marginalisation on how the COVID-19 environment shaped their overdose risk in British Columbia, Canada. METHODS: We conducted semi-structured interviews remotely with 45 participants between May 2020 and September 2021, and analysed the data thematically using a social violence framework. RESULTS: Participants identified contamination of the unregulated drug supply, particularly with benzodiazepines, as a significant driver of overdose and gendered violence among women who use drugs. 'Social distancing' guidelines (e.g., guest restrictions in supportive housing, reduced capacity in harm reduction services) compounded these risks and resulted in more women using drugs alone, reducing opportunities for timely overdose intervention. In response, participants practiced individualised acts of caregiving (e.g., establishing informal networks that regularly check on each other) to mitigate the risks of overdose and gendered violence for themselves and their community. DISCUSSION AND CONCLUSIONS: These intersecting health crises perpetuated individualised approaches to addressing the risks of overdose and gendered violence, rather than addressing underlying social and structural drivers of these risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".