Bereaved mothers’ engagement in drug policy reform: A multisite qualitative analysis
Bibliographic record
Abstract
BACKGROUND: Globally, a tainted drug supply is claiming the lives of tens of thousands of people who use drugs and current measures are not quelling this crisis. Within this context, mothers who have lost a child to substance use have emerged as vocal advocates for drug policy changes. This paper explores mothers' experiences in drug policy advocacy to uncover how they are using their stories to drive policy change. METHODS: Critical qualitative and narrative methods informed individual interviews with 43 mothers who had lost a child to substance use from across three regions in Canada: British Columbia, Prairie Provinces, and Eastern Provinces. Multisite qualitative analysis (MSQA) provided a rigorous analytical method to identify how social context informed participants' advocacy efforts within and across geographies, together with a theoretical lens from Haraway to understand mothers' activism as situated knowledge. RESULTS: Mothers' drug policy advocacy was shaped by social context and norms, which influenced the types of advocacy targets pursued, within the constraints of the social and political ethos of each geographic region. Yet across regions, narratives of shared aims and experiences also emerged. Specifically, the notion that people of all backgrounds are dying and that losing a child to substance use can "happen to anyone" - though people who experience structural vulnerabilities are disproportionately impacted. Additionally, mothers' stories were identified as a particularly powerful tool for conveying emotional knowledge and prompting action that complements other forms of knowledge or evidence. CONCLUSION: To date, efforts to address the drug poisoning epidemic have done little to curb casualties. Mothers whose child's death is related to substance use are one group who are bringing their experiences to advocacy efforts aimed at generating new solutions, including calls for decriminalization and legal regulation of drugs. This and other lived experience perspectives represent a critical voice in decision-making and hold the potential to inform more responsive and impactful drug policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".