“Basically every safety protocol we have in place to protect against overdose, parents can't access”: Mothers who use unregulated drugs’ experiences of dual public health emergencies
Bibliographic record
Abstract
BACKGROUND: A growing body of research details the impacts of COVID-19 pandemic-related public health directives and service disruptions on people who use unregulated drugs, however, there is limited research on the gendered impacts, particularly among mothers. METHODS: To explore experiences navigating the COVID-19 pandemic during a toxic drug crisis, phone-based semi-structured interviews were conducted from May 2020-Sept. 2021 with 45 women who use unregulated drugs in British Columbia. Iterative-based thematic analysis informed by intersectional theory identified unaccounted for impacts on parents. FINDINGS: Respondents' experiences of the COVID-19 pandemic involved contending with an increasingly toxic drug supply alongside reduced resources, including service reductions and closures and reduced access to harm reduction supplies in a context already marked by women's marginalization within treatment and policy. Mothers described pandemic-related increased custody and care barriers and burdens, resulting in inability to prioritize self-care. Decisions to prioritize associated risks of either COVID-19 or drug toxicity, given conflicting public health guidelines, were experienced as high stakes for mothers, due to their unique vulnerability to institutional scrutiny. Pandemic-informed overdose risk mitigations, such as access to legal pharmaceutical-grade alternatives to the toxic drug supply, also involved additional risks for mothers (e.g., heightened monitoring; child apprehension), thus, some respondents, responsibilized for their health and childcare, resourcefully relied upon informal, social networks to help mitigate potential harms. CONCLUSION: While mothers and their experiences are heterogeneous, gendered distinctions impact provision and experience of health care, harm reduction and social supports. Mothers who use drugs must navigate health and care responsibilities, exacerbated by pandemic-related health barriers, while simultaneously inhabiting an unremitting state of fear of punitive measures or postapprehension despair. The continued social exclusion of mothers who use drugs, propelled by moralizing discourses framing them as deviant and consequentially undeserving, can have devastating health impacts (on individuals and communities) yet remain underaddressed.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".