Demanding a safer supply: Stories from the frontlines of the drug poisoning crisis in the context of the housing crisis and COVID-19 pandemic
Bibliographic record
Abstract
Hearing and understanding stories by people who use drugs is paramount in addressing the drug poisoning crisis on this land we call Canada. The purpose of this research was to explore the stories that people in safer supply programs (SSP) tell about their experiences navigating the drug poisoning crisis in the context of the housing crisis and the COVID-19 pandemic. Socio-narratology was used to understand the dialogical nature of the story and the storyteller. The thesis begins with an autobiographical reflection and a look at the history of the drug poisoning crisis, with special interest in how it relates to colonization as Indigenous peoples have been disproportionately affected by the racist and colonial war on people who use drugs. The importance of this lens is evident as the research study includes the stories and experiences of three Indigenous peoples who have stabilized in an SSP. The participants each had two interviews/dialogues, and the data was analyzed using hermeneutic dialogical narrative analysis. Analytic interests that resonated through the narratives included experiencing pain, seeking support, sense of belonging, finding purpose, creating meaning, and blaming the system and calling for change. The study demonstrates the importance of equipping people who use drugs with the knowledge, resources, and support necessary to alter their lifestyle in ways they deem important. The study highlights the importance of including the voices of people with lived and living experience of drug use when designing, implementing, and evaluating SSPs. While COVID-19 impacted the participant’s ability to connect, we learned that this was not their biggest concern. Instead, what they wanted to see was (1) an end to the unnecessary drug poisoning deaths due to the toxic street supply of drugs, (2) the addition of appropriate and affordable housing, and (3) the incorporation of Indigenous ways of knowing and being to enhance Western models of care. The findings and recommendations of this socio-narratology study offer new insights and understandings related to the role we can play in abating the drug poisoning crisis in a way that witnesses, respects, and values the voices, stories, and lives of people who use drugs.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| 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".