Engaging families and parent advocates in research on substance use and drug policy reform: Guiding principles from a Canadian community‐academic partnership
Bibliographic record
Abstract
Canada is in the midst of a public health emergency in drug poisoning (overdose) deaths. In this context parents, and especially mothers, of those who have died from drug poisoning have mobilised to advocate for urgent responses and drug policy reforms. To document this emerging women-led advocacy, we initiated a community-academic research partnership with three parent groups representing families in Canada bereaved by drug-related deaths. In this commentary, we describe four guiding principles we developed during the course of this project, to ensure an ethical and equitable approach to conducting our research partnership. In particular, we emphasise how we navigated parents' roles as vocal advocates for addressing drug stigma and expanding harm reduction while actively working to avoid eclipsing the need to centre the perspectives of people who use drugs. Meaningful and collaborative partnerships between academics and community groups may facilitate greater understandings of how families and communities can be allied in drug policy reforms urgently needed to prevent drug poisoning deaths.
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 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 | 0.356 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.056 | 0.055 |
| Scholarly communication | 0.026 | 0.010 |
| Open science | 0.010 | 0.023 |
| Research integrity | 0.020 | 0.030 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".