Mothers Who Use Drugs: Closing the Gaps in Harm Reduction Response Amidst the Dual Epidemics of Overdose and Violence in a Canadian Urban Setting
Bibliographic record
Abstract
Objectives. To identify key gaps in overdose prevention interventions for mothers who use drugs and the paradoxical impact of institutional practices that can increase overdose risk in the context of punitive drug policies and a toxic drug supply. Methods. Semistructured interviews were conducted with 40 women accessing 2 women-only, low-barrier supervised consumption sites in Greater Vancouver, British Columbia, Canada, between 2017 and 2019. Our analysis drew on intersectional understandings of structural, everyday, and symbolic violence. Results. Participants’ substance use and overdose risk (e.g., injecting alone) was shaped by fear of institutional and partner scrutiny and loss (or feared loss) of child custody or reunification. Findings indicate that punitive policies and institutional practices that frame women who use drugs as unfit parents continue to negatively shape the lives of women, most significantly among Indigenous participants. Conclusions. Nonpunitive policies, including access to safe, nontoxic drug supplies, are critical first steps to decreasing women’s overdose risk alongside gender-specific and culturally informed harm-reduction responses, including community-based, peer-led initiatives to maintain parent–child relationships. (Am J Public Health. 2022;112(S2):S191–S198. https://doi.org/10.2105/AJPH.2022.306776 )
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".